ArticlePDF Available

Conceptual approaches for defining data, information, and knowledge

Authors:

Abstract

The field of Information Science is constantly changing. Therefore, information scientists are required to regularly review—and if necessary—redefine its fundamental building blocks. This article is one of a group of four articles, which resulted from a Critical Delphi study conducted in 2003–2005. The study, “Knowledge Map of Information Science,” was aimed at exploring the foundations of information science. The international panel was composed of 57 leading scholars from 16 countries, who represent (almost) all the major subfields and important aspects of the field. This particular article documents 130 definitions of data, information, and knowledge formulated by 45 scholars, and maps the major conceptual approaches for defining these three key concepts. © 2007 Wiley Periodicals, Inc.
The field of Information Science is constantly changing.
Therefore, information scientists are required to regu-
larly review—and if necessary—redefine its fundamental
building blocks. This article is one of a group of four
articles, which resulted from a Critical Delphi study con-
ducted in 2003–2005. The study, “Knowledge Map of Infor-
mation Science,” was aimed at exploring the foundations
of information science. The international panel was com-
posed of 57 leading scholars from 16 countries, who
represent (almost) all the major subfields and important
aspects of the field. This particular article documents
130 definitions of data, information, and knowledge for-
mulated by 45 scholars, and maps the major conceptual
approaches for defining these three key concepts.
Introduction
Context
The field of Information Science (IS) is constantly chang-
ing. Therefore, information scientists are required to regu-
larly review—and if necessary—redefine its fundamental
building blocks. This article is one of a group of four arti-
cles, which resulted from a Critical Delphi study conducted
in 2003–2005. The study, Knowledge Map of Information
Science, explores the theoretical foundations of information
science. It maps the conceptual approaches for defining
data, information, and knowledge, which is presented here,
maps the major conceptions of Information Science (Zins,
2007a), portrays the profile of contemporary Information
Science by documenting 28 classification schemes compiled
by leading scholars during the study (Zins, in press), and cul-
minates in developing a systematic and scientifically based
knowledge map of the field (Zins, 2007b).
The three concepts of data, information, and knowledge,
which are the foci of this article, are fundamental in the con-
text of information science. They are often regarded as the
basic building blocks of the field. For this very reason, the
formulation of systematic conceptions of data, information,
and knowledge is crucial for the development of a system-
atic conception of Information Science, as well as for th con-
struction of a systematic knowledge map of the field.
Data, Information, and Knowledge
The academic and professional IS literature supports di-
versified meanings for each concept. Evidently, the three key
concepts are interrelated, but the nature of the relations
among them is debatable, as well as their meanings.
Interrelations. Many scholars claim that data, information,
and knowledge are part of a sequential order. Data are the
raw material for information, and information is the raw ma-
terial for knowledge. However, if this is the case, then Infor-
mation Science should explore data (information’s building
blocks) and information, but not knowledge, which is an en-
tity of a higher order. Nevertheless, it seems that information
science does explore knowledge because it includes the two
subfields, knowledge organization, and knowledge manage-
ment, which can be confusing. Should we refute the sequential
order? Should we change the name of the field from Infor-
mation Science to Knowledge Science? Or should we go to
the extreme of excluding the two subfields of knowledge or-
ganization and knowledge management from information
science?
Information versus knowledge. Another common view is
that knowledge is the product of a synthesis in the mind of
the knowing person, and exists only in his or her mind. If
this is the case, we might well exclude the subfields of
knowledge organization and knowledge management from
information science. Besides, is Albert Einstein’s famous
equation “E MC2” (which is printed on my computer
screen, and is definitely separated from any human mind)
information or knowledge? Is “2 2 4” information or
knowledge?
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 58(4):479–493, 2007
Conceptual Approaches for Defining Data, Information,
and Knowledge
Chaim Zins
Knowledge Mapping Research, 26 Hahaganah Street, Jerusalem 97852, Israel.
E-mail: chaim.zins@gmail.com
Received November 15, 2005; revised March 10, 2006; accepted March 10,
2006
©2007 Wiley Periodicals, Inc. Published online 22 January 2007 in Wiley
InterScience (www.interscience.wiley.com). DOI: 10.1002/asi.20508
Synonyms. The alternative view that information and
knowledge are synonyms is problematic too. If information
and knowledge are synonyms, could we use the term Knowl-
edge Science rather than Information Science?
Such issues are rooted in various subjectivist and empiri-
cist schools of philosophy, and are not addressed here as a
philosophical treatise. This article is focused on exploring
the meanings of the three fundamental concepts of data, in-
formation, and knowledge and the relations among them, as
they are perceived by leading scholars in the information
science academic community.
Methodology
The scientific methodology is Critical Delphi. Critical
Delphi is a qualitative research methodology aimed at facil-
itating critical and moderated discussions among experts
(the panel). The international and intercultural panel is
composed of 57 participants from 16 countries. It is unique
and exceptional; it is comprised of leading scholars who
represent nearly all the major subfields and important as-
pects of the field (see Appendix A). The indirect discussions
were anonymous and were conducted in three successive
rounds of structured questionnaires. The first questionnaire
contained 24 detailed and open-ended questions covering
16 pages. The second questionnaire contained 18 questions
in 16 pages. The third questionnaire contained 13 questions
in 28 pages (see relevant excerpts from the three question-
naires in Appendix B). The return rates were relatively high:
57 scholars (100%) returned the first round, 39 (68.4%) re-
turned the second round, and 39 (68.4%) returned the third
round. Forty-three panelists (75.4%) participated in two
rounds (i.e., R1 and either R2 or R3), and 35 panelists
(61.4%) participated in all three rounds. In addition, each
participant received his or her responses that I initially in-
tended to cite in future publications. The responses were sent
to the each panel member with relevant critical reflections.
Forty-seven (82.4%) participants responded and approved
their responses. Twenty three of them, which is 48.9% (23
out of 47), and 40.3% of the entire panel (23 out of 57) re-
vised their original responses. Therefore, one can say that
actually the critical process was composed of four rounds.
The Panel’s Definitions
Forty-four panel members contributed their definitions
and reflections as follows.1
Data. In computational systems data are the coded invari-
ances. In human discourse data are that which is stated, for
instance, by informants in an empirical study. Information is
related to meaning or human intention. In computational sys-
tems information is the contents of databases, the web, etc. In
human discourse systems information is the meaning of
statements as they are intended by the speaker/writer and
understood/misunderstood by the listener/reader. Knowl-
edge is embodied in humans as the capacity to understand,
explain and negotiate concepts, actions and intentions. [1]
(Hanne Albrechtsen)
Datum is the representation of concepts or other entities, fixed
in or on a medium in a form suitable for communication, inter-
pretation, or processing by human beings or by automated sys-
tems (Wellisch, 1996). Information is (1) a message used by a
sender to represent one or more concepts within a communica-
tion process, intended to increase knowledge in recipients.
(2) A message recorded in the text of a document. Knowledge
is knowing, familiarity gained by experience; person’s range of
information; a theoretical or practical understanding of; the
sum of what is known.” [2] (Elsa Barber)
Data is a symbol set that is quantified and/or qualified.
Information is a set of significant sings that has the ability to
create knowledge . . . The essence of the information phe-
nomenon has been characterized as the occurrence of a com-
munication process that takes place between the sender and
the recipient of the message. Thus, the various concepts of in-
formation tend to concentrate on the origin and the end point
of this communication process (Wersig & Neveling, 1975).
Knowledge is information that has been appropriate by the
user. When information is adequately assimilated, it produces
knowledge, modifies the individual’s mental store of informa-
tion and benefits his development and that of the society in
which he lives. Thus, as the mediating agent in the production
of knowledge, the information, qualifies itself, in form and
substance, as significant structures able to generate knowl-
edge for the individual and his group.” [3] (Aldo Barreto)
Data are sensory stimuli that we perceive through our senses.
Information is data that has been processed into a form that
is meaningful to the recipient (Davis & Olson, 1985).
Knowledge is what has understood and evaluated by the
knower.” [4] (Shifra Baruchson–Arbib)
Datum is every thing or every unit that could increase the
human knowledge or could allow to enlarge our field of sci-
entific, theoretical or practical knowledge, and that can be
recorded, on whichever support, or orally handed. Data can
arouse information and knowledge in our mind. Informa-
tion is the change determined in the cognitive heritage of an
individual. Information always develops inside of a cogni-
tive system, or a knowing subject. Signs that constitute the
words by which a document or a book has made are not in-
formation. Information starts when signs are in connection
with an interpreter (Morris, 1938). Knowledge is structured
and organized information that has developed inside of a
cognitive system or is part of the cognitive heritage of an
individual (based on C. S. Peirce; Burks, 1958; Hartshorne
& Weiss, 1931). [5] (Maria Teresa Biagetti)
Data. The word “data” is commonly used to refer to records
or recordings encoded for use in computer, but is more
widely used to refer to statistical observations and other
recordings or collections of evidence.
Information. The word “information” is used to refer to a
number of different phenomena. These phenomena have
been classified into three groupings: (1) Anything perceived
as potentially signifying something (e.g. printed books);
(2) The process of informing; and (3) That which is learned
480 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007
DOI: 10.1002/asi
1I added the boldface in this section.
from some evidence or communication. All three are valid
uses (in English) of the term “information.” I personally am
most comfortable with no. 1, then with no. 3, but acknowl-
edge that others have used and may use no 2.
Knowledge. The word “knowledge” is best used to refer to
what someone knows, which is, in effect, what they believe,
including belief that some of the beliefs of others should not
be believed. By extension the word “knowledge” is used
more loosely for (1) what social groups know collectively;
and (2) what is in principle knowable because it has been
recorded somehow and could be recovered even though, at
any given time, no individual knows (or remembers) it. [6]
(Michael Buckland)
Data are the basic individual items of numeric or other in-
formation, garnered through observation; but in themselves,
without context, they are devoid of information. Informa-
tion is that which is conveyed, and possibly amenable to
analysis and interpretation, through data and the context in
which the data are assembled. Knowledge is the general
understanding and awareness garnered from accumulated
information, tempered by experience, enabling new contexts
to be envisaged. [7] (Quentin L. Burrell)
Data are (or datum is) an abstraction. I mean, the concept of
‘data’ or ‘datum’ suggests that there is something there that
is purely given and that can be known as such. The last one
hundred years of (late) philosophic discussion and, of
course, many hundred years before, have shown that there is
nothing like ‘the given’ or ‘naked facts’ but that every
(human) experience/knowledge is biased. This is the
‘theory-laden’ theorem that is shared today by such different
philosophic schools as Popper’s critical rationalism (and his
followers and critics such as Kuhn or Feyerabend), analytic
philosophy (Quine, for instance), hermeneutics (Gadamer),
etc. Modern philosophy (Kant) is very acquainted with this
question: experience (“Erfahrung”) is a product of ‘sensory
data’ within the framework of perception (“Anschauung”)
and the categories of reason (“Verstand”) (“perception with-
out concepts is blind, concepts without perception are
void”). Pure sensory data are as unknowable as “things in
themselves”.”
Information is a multi-layered concept with Latin roots
(‘informatio’ to give a form) that go back to Greek ontol-
ogy and epistemology (Plato’s concept of ‘idea’ and Aristo-
tle’s concepts of ‘morphe’ but also to such concepts as
‘typos’ and ‘prolepsis’) (See Capurro, 1978; Capurro &
Hjøerland, 2003). The use of this concept in information
science is at the first sight highly controversial but it
basically refers to the everyday meaning (since Modernity):
“the act of communicating knowledge” (OED). I would
suggest to use this definition as far as it points to the
phenomenon of message that I consider the basic one in
information science.
Message, information, understanding. Following systems
theory and second-order cybernetics, I suggest to distinguish
between ‘message’, ‘information’ and ‘understanding.’ All
three concepts constitute the concept of communication
(See, for instance, Luhmann, 1996, with references to
biology (Maturana/Varela), cybernetics etc.). A ‘message’ is
a ‘meaning offer’ while ‘information’ refers to the selection
within a system and ‘understanding’ to the possibility
that the receiver integrates the selection within his/her pre-
knowledge—constantly open to revision i.e. to new commu-
nication—in accordance with the intention(s) of the sender.
The receiver mutates each time into a sender.
Knowledge is ‘no-thing’ (contrary to “information-
as-thing” as suggested by Michael Buckland, 1991a), i.e., it
is the event of meaning selection of a (psychic/social) sys-
tem from its ‘world’ on the basis of communication. The “act
of communicating knowledge” (OED’s definition of infor-
mation) is then to be understood as the act of making a
meaning offer (message) leading to understanding (and
misunderstanding) on the basis of a selection of meaning
(information). To know is then to understand on the basis
of making a difference between ‘message’ (or meaning
offer) and ‘information’ (or meaning selection). Human
knowledge is, as Popper states, basically conjectural. Or, to
put it in hermeneutic terms: understanding is always biased,
i.e., based on (implicit) pre-understanding. In more classical
terms we distinguish following Aristotle between ‘empirical
knowledge’ (or ‘know-how’ ‘empeiria’) and explicit
knowledge (or ‘know-that’, for instance, scientific knowl-
edge or ‘episteme’).
Data, information, knowledge. Putting the three concepts
(“data,” “information,” and “knowledge”) as done here,
gives the impression of a logical hierarchy: Information is
set together out of data and knowledge comes out from
putting together information. This is a fairytale. [8] (Raphael
Capurro)
Knowledge is that which is known, and it exists in the mind
of the knower in electrical pulses. Alternatively, it can be dis-
embodied into symbolic representations of that knowledge (at
this point becoming a particular kind of information, not
knowledge). Strictly speaking, represented knowledge is in-
formation. Knowledge—that which is known—is by defini-
tion subjective, even when aggregated to the level of social, or
public, knowledge—which is the sum, in a sense, of individ-
ual “knowings.” Data and information can be studied as per-
ceived by and “embodied” (known) by the person or as found
in the world outside the person...” [9] (Thomas A. Childers)
Data is the plural of datum, although the singular form is
rarely used. Purists who remember their first-year Latin may
insist on using a plural verb with data, but they forget that
English grammar permits collective nouns. Depending on
the context, data can be used in the plural or as a singular
word meaning a set or collection of facts. Etymologically,
data, as noted, is the plural of datum, a noun formed from the
past participle of the Latin verb dare–to give. Originally,
data were things that were given (accepted as “true”). A data
element, d, is the smallest thing which can be recognized as
a discrete element of that class of things named by a specific
attribute, for a given unit of measure with a given precision
of measurement (Rush & Davis, 2007; Landry & Rush,
1970; Yovits & Ernst, 1970).
Information. The verb ‘inform’ normally is used in the sense
to communicate (i.e., to report, relate, or tell) and comes from
the Latin verb informare, which meant to shape (form) an
idea. Data is persistent while information is transient, depend-
ing on context and the interpretation of the recipient. Informa-
tion is data received through a communication process that
proves of value in making decisions.
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007 481
DOI: 10.1002/asi
Knowledge involves both data and the relationships among
data elements or their sets. This organization of data based
on relationships is what enables one to draw generalizations
from the data so organized, and to formulate questions about
which one wishes to acquire more data. That is, knowledge
begets the quest for knowledge, and it arises from verified or
validated ideas (Sowell, 1996). [10] (Charles H. Davis)
Data are symbols organized according to established
algorithms.
Information represents a state of awareness (consciousness)
and the physical manifestations they form. Information, as a
phenomena, represents both a process and a product; a cogni-
tive/affective state, and the physical counterpart (product of)
the cognitive/affective state. The counterpart could range
from a scratch of a surface, movement (placement)of a rock;
a gesture(movement) speech(sound), written document, etc.
(requirement). Information answers questions of what,
where, when and who and permutations thereof.
Knowledge. Knowledge represents a cognitive/affective
state that finds definition in meaning and understanding.
Knowledge is reflected in the questions of “how” and “why.”
Knowledge extends the organism state of awareness (con-
sciousness/ information). Knowledge can be given physical
representation (presence) in the material products (technol-
ogy) thereof (books, film, speech, etc.).
Message. Message is a medium through which data; infor-
mation and knowledge are transmitted and used. It repre-
sents an instrument for moving the state of awareness and
meaning with reference to specific events (states, condi-
tions) from one implicit, or explicit source to another. When
the physical products of awareness are transferred from one
source to another, reference to the collective domain can be
realized. [11] (Anthony Debons)
Raw data (sometimes called source data or atomic data) is
data that has not been processed for use. [In the spirit of Tom
Stonier’s definition—Data: a series of disconnected facts and
observations] Here “unprocessed” might be understood in a
sense that no specific effort has been made to interpret or un-
derstand the data. They are the result of some observation or
measurement process, which has been recorded as “facts of
the world.” The word data is the plural of Latin datum, “some-
thing given”, which one also could call “atomic facts. Infor-
mation is the end product of data processing. Knowledge is
the end product of information processing. In much the same
way as raw data are used as input, and processed in order to
get information, the information itself is used as input for a
process that results in knowledge.
Theory laden. It is very true that all data are theory laden.
That does not mean that you can not produce new data which
in the next step will lead to the theory revision, and that new,
corrected theory will be the basis for producing new data
which after a while will lead to the correction of the existing
theory. We use our theory-laden data to refute theories!
Data-Information-Knowledge-Wisdom. According to
Stonier (1993, 1997), data is a series of disconnected facts
and observations. These may be converted to information by
analyzing, cross-referring, selecting, sorting, summarizing,
or in some way organizing the data. Patterns of information,
in turn, can be worked up into a coherent body of knowledge.
Knowledge consists of an organized body of information,
such information patterns forming the basis of the kinds of
insights and judgments which we call wisdom. The above
conceptualization may be made concrete by a physical anal-
ogy (Stonier, 1993): consider spinning fleece into yarn, and
then weaving yarn into cloth. The fleece can be considered
analogous to data, the yarn to information and the cloth to
knowledge. Cutting and sewing the cloth into a useful
garment is analogous to creating insight and judgment
(wisdom). This analogy emphasizes two important points:
(1) going from fleece to garment involves, at each step, an
input of work, and (2) at each step, this input of work leads
to an increase in organization, thereby producing a hierarchy
of organization.” [12] (Gordana.Dodig-Crnkovi)
Datum is a unique piece of content related to an entity.
Information is the sum of the data related to an entity. [13]
(Henri Jean-Marie Dou)
Data are a set of symbols representing a perception of raw
facts (i.e., following Debons, Horne, & Cronenweth (1988),
events from which inferences or conclusions can be drawn).
Information is organized data (answering the following
basic questions: What? Who? When? Where?). Knowledge
is understood information (answering following basic ques-
tions: why?, how?, for which purpose?).” [14] (Nicolae
Dragulanescu)
Data. Here, data typically means the “raw” material
obtained from observation (broadly understood, but not
necessarily, as “sense impressions,” which is a key notion
of empiricist philosophy). Such data is typically quantitative,
presented in numbers and figures. [15] (Hamid Ekbia)
Prolog. These definitions are offered as an elaboration on
physicist Heinz Pagels’ (1988) observation:
“Information is just signs and numbers, while knowledge
has semantic value. What we want is knowledge, but what
we often get is information. It is a sign of the times that
many people cannot tell the difference between information
and knowledge, not to mention wisdom, which even knowl-
edge tends to drive out.” (p. 49, cited in O’Leary and
Brasher, 1996, p. 262).
These distinctions in turn trace back at least as far as T. S.
Eliot’s lament:
Where is the Life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?
Choruses from the Rock
Data can be defined as a class of information objects, made
up of units of binary code that are intended to be stored,
processed, and transmitted by digital computers. As such,
data consists of information in a narrow sense—i.e., as in-
scribed in binary code, units of data are not likely to be
immediately meaningful to a human being. But units of data,
as “informational building blocks,” when collected and
processed properly, can form information in the broader
sense (see below), i.e., that is more likely to be meaningful
to a human being (as sense-making beings).
Information. Collocations of data (information in the nar-
row sense—see above) that thereby become meaningful to
human beings—e.g., as otherwise opaque units of binary
code are collected and processed into numbers, artificial and
482 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007
DOI: 10.1002/asi
natural languages, graphic objects that convey significance
and meaning, etc. Such collocations of data can be made
meaningful by human beings (as sense-making beings) es-
pecially as such data collocations/information connect with,
illuminate, and are illuminated by still larger cognitive
frameworks—most broadly, worldviews that further incor-
porate knowledge and wisdom (see below). On this defini-
tion, information can include but is not restricted to data. On
the contrary, especially as Borgmann (1999) argues, there
are other forms of information (natural, cultural) that are not
fully reducible to data as can be transmitted, processed,
and/or produced by computers and affiliated technologies.
Knowledge is one step above information, and one step
below wisdom. Knowledge in the broadest sense approaches
a reasonably comprehensive worldview, i.e., a cognitive
framework that establishes the major parameters and ten
thousand details of human social and ethical realities, includ-
ing basic values, beliefs, habits, notions of identity, relation-
ships among human beings (including gender identity and
issues) and relationships between humanity and larger reali-
ties (political, environmental, religious). Knowledge, how-
ever, can remain detached, objective, and thereby useless.
Transforming cognitive forms of knowledge into ethical
judgment and action is a primary task and goal of wisdom
(see Dreyfus, 2001; Ess, 2003, 2004). [16] (Charles Ess)
Data are a string of symbols. Information is data that is
communicated, has meaning, has an effect, has a goal.
Knowledge is a personal/cognitive framework that makes it
possible for humans to use information. [17] (Raya Fidel)
Data. It depends on your framework. If you are a Kantian, it
is the foundation for the a priori categories of the under-
standing. If you are a computer programmer it is pre-
processed information (data collected according to some al-
gorithm for some purpose) or post-processed information
(e.g., tables of such information). In this latter case data can-
not be defined apart from information, because it is depen-
dent on it. If you are a biologist, it might be stimuli, but these
scientific approaches are built on a faulty understanding of
perception (e.g., perception is sensations (i.e., stimuli) glued
together—which is false).
Information is resources useful or relevant or functional for
information seekers.
Knowledge. For some philosophers, validated, true infor-
mation is that which coheres with other truths (coherence
theory of truth). For others, what corresponds to reality (cor-
respondence theory of truth). For others, it is what works or
is functional (pragmatic theory of truth). At any event it is
always contextual.
Alot of our so-called truths, knowledge, or known ‘facts’ are
really orthodoxy—what we collectively believe at a certain
point in time.2Today when someone would observe an
unsupported object falling, when pushed for an explanation,
they would utter the phrase/explanation: “the law of grav-
ity.” Unfortunately, it is an explanation that fails to ex-
plain—we still do not know what the “weak force” is, what
gravity is, but we are taught in our so-called scientific
approach, to utter a phrase that is supposed to—in the
naming of it—to explain it. Four centuries back, it was
attributed to the “will of God.” Is this a worse explanation?
Possibly. In both cases, we are living in images and
metaphors and the orthodox frameworks of the time.
Most reference collections in libraries are expressions of
orthodoxies of various subject domains. [18] (Thomas
J. Froehlich)
Data are representations of facts about the world. Informa-
tion is data organized according to an ontology that defines
the relationships between some set of topics. Information
can be communicated. Knowledge is a set of conceptual
structures held in human brains and only imperfectly repre-
sented by information that can be communicated. Knowl-
edge cannot be communicated by speech or any form of
writing, but can only be hinted at. [19] (H.M. Gladney)
Data is one or more kinds of energy waves or particles
(light, heat, sound, force, electromagnetic) selected by a
conscious organism or intelligent agent on the basis of a pre-
existing frame or inferential mechanism in the organism or
agent. Information is an organism’s or an agent’s active or
latent inferential frame that guides the selection of data for
its own further development or construction. Knowledge is
one or more sets of relatively stable information. A Message
is one or more inferred data sets gleaned from external or
internal energetic reactions. [20] (Glynn Harmon)
Data are facts and statistics that can be quantified, mea-
sured, counted, and stored. Information is data that has
been categorized, counted, and thus given meaning, rele-
vance, or purpose. Knowledge is information that has been
given meaning and taken to a higher level. Knowledge
emerges from analysis, reflection upon, and synthesis of
information. It is used to make a difference in an enterprise,
learn a lesson, or solve a problem. [21] (Donald Hawkins)
Datum is smallest collectable unit associated with a phe-
nomenon. Normally, data occur in collections that are col-
lected in order to monitor a process, assess a situation,
and/or otherwise gain a referent on a phenomenon. This does
not mean that data are always defined, collected or used
appropriately for the question in hand, but that that is the
intention when doing so. They are building blocks, even if
the building is engineered incorrectly.
Information. I would usually expect information to be an
assessment or interpretation of data. Often information is not
far removed from the ‘smallest collectable unit’ as I have de-
fined “datum.” But I expect it to be some abstraction from
data.. Information does not inherently mean empirical or
first hand analysis of data. It also does not guarantee correct
interpretation of data although that is expected.
Knowledge is more subject, and intangible compared to in-
formation or data. It is what an individual takes from informa-
tion and data, and what they incorporate into their beliefs,
values, procedures, actions, etc. It is heavily internally
oriented, understood completely only to the person possessing
it. Much work around knowledge implies how to get the
knowledge “out of” one head and in to another. Such transfer
entails encoding knowledge into transferable information and
decoding again into knowledge. Knowledge and information
are not the same, but they feed from and support each other.
Amessage is the encoded information or codified/explicit
knowledge that is disseminated to others. Very much a
Shannon and Weaver transmission model, but I also consider
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007 483
DOI: 10.1002/asi
2Plato differentiated between “doxa” (opinion) and “orthodoxa” (right
or true opinion).
that encoding and decoding have a heavy personal, contextual
and historical influence. [22] (Caroline Haythornthwaite)
Data are dynamic objects of cultural experience having the
aspect of being meaning-neutral and a dual nature of de-
scription and instruction. Information is dynamic objects of
cultural experience having the aspect of being belief-neutral
and a dual nature of content and medium. Knowledge is
dynamic objects of cultural experience having the aspect of
being action-neutral and a dual nature of abstracting to and
from the world. [23] (Ken Herold)
Data are the raw observations about the world collected by
scientists and others, with a minimum of contextual inter-
pretation. Information is the aggregation of data to make
coherent observations about the world. Knowledge is the
rules and organizing principles gleaned from data to aggre-
gate it into information. [24] (William Hersh)
Data are observations and measurements you make on ob-
jects (artifacts, sites, seeds, bones) and on their contexts.
Data are theory-laden.
Regarding the theory of knowledge organization we may say
that knowledge is not organized by elements called data
combined or processed according to some algorithmic pro-
cedure. What data are is domain specific and theory-laden.
At the most general level what is seen as data is depending
of the epistemological view that one subscribes to.
Information. The most fruitful theoretical view is here based
on Karpatschof’s interpretation of information and activity
theory, AT (2000, p. 128). In order to define information,
Karpatschof introduces the concept of release mechanisms,
being systems having at their disposal a store of potential en-
ergy, the systems being “designed” to let this energy out in
specific ways, whenever trigged by a signal fulfilling the
specifications of the release mechanism. The signal that trig-
gers a certain release mechanism is a low energy phenome-
non fulfilling some release specifications. The signal is thus the
indirect cause, and the process of the release mechanism the
direct cause of the resulting reaction, which is a high-energy
reaction compared to the energy in the signal. Information is
thus defined as a quality by a given signal relative to a cer-
tain mechanism.
The release mechanism has a double function: (1) it rein-
forces the weak signal and (2) it directs the reaction by defin-
ing the functional value of a signal in the pre-designed sys-
tem of the release mechanism. There has been a tendency to
consider information to be an obscure category in addition to
the classical categories of physics. Information is indeed a
new category, but it cannot be placed, eclectically, beside the
prior physical categories. Information is a category, not be-
side, but indeed above the classical categories of physics.
Therefore, information is neither directly reducible to these
classical categories, nor is it a radically different category of
another nature than mass and energy.
Information is, in fact, the causal result of existing physical
components and processes. Moreover, it is an emergent result
of such physical entities. This is revealed in the systemic de-
finition of information. It is a relational concept that includes
the source, the signal, the release mechanism and the reaction
as its reactants. The release mechanism is a signal processing
system and an information processing system.
Information is thus defined in physical terms of signals,
mechanisms and energy, but probably first arose with the
biological world. Hjørland (2002) outlines the development
of information processing mechanisms in the biological, the
cultural and the social world.
Many professionals can claim to work with “the generation,
collection, organization, interpretation, storage, retrieval, dis-
semination, transformation and use of information”. This is
not specific to information professionals. (Their specific work
is discussed in Capurro & Hjørland (2003) and elsewhere).
Hjørland (2000) investigates when and why the word “infor-
mation” became associated with library schools (and thus
knowledge organization) and what the theoretical implica-
tions in the shift from documents to information imply.”
Knowledge. Different epistemologies (theories of knowl-
edge) have different views on the nature of knowledge. I sub-
scribe to the pragmatic theory of knowledge. The most impor-
tant influence from pragmatic philosophy has been skepticism
towards any claim of knowledge. A claim of knowledge
should never be regarded as finally verified. It should just be
regarded as just a claim. However, claims may be supported
by empirical and logical arguments. Knowledge claims are
parts of more comprehensive theories. Knowledge claims are
not purely arbitrary. Instead of regarding science as a collec-
tion of true statements, it should be regarded as a collection of
supported knowledge claims. In ordinary speech, knowledge
then means that part of our background assumptions, that we
do not find it fruitful to put questions to. [25] (Birger Hjorland)
Data are atomic facts, basic elements of “truth,” without in-
terpretation or greater context. It is related to things we
sense. Information is a set of facts with processing capabil-
ity added, such as context, relationships to other facts about
the same or related objects, implying an increased useful-
ness. Information provides meaning to data. Knowledge is
information with more context and understanding, perhaps
with the addition of rules to extend definitions and allow in-
ference. [26] (Donald Kraft)
Datum (in our sector mainly electronic) is the conventional
representation, after coding (using ASCII, for example), of
information. Information is knowledge recorded on a spa-
tio-temporal support. Knowledge is the result of forming in
mind an idea of something (Le Coadic, 2004). [27] (Yves
François Le Coadic)
Data are commonly seen as simple, isolated facts, though
products of intellectual activity in their rough shape. Knowl-
edge is the appropriation of information in the process of
learning, acting, interpreting. Knowledge is in the head of
people, yet knowledge can be shared. Knowledge refers to
the way information is used during the intellectual process.
[28] (Jo Link-Pezet)
Data are formalized parts (i.e., digitalized contents) of socio-
cultural information potentionally proccessable by technical
facilities which disregard the cognitive process and that is
why it is necessary to provide them with meanings from out-
side (i.e., they are objective). Information is a relationship
between an inner arrangement (i.e., a priori set structure
(S
ˇmajs & Krob, 2003), implicate order3) of a system and its
484 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007
DOI: 10.1002/asi
3The concepts of implicate and explicate orders are explained in
Bohm (1980).
present embodiment in reality (explicate order) including
mediating memory processes (i.e., historically dependent
processes) releasing the meaning. Knowledge is tacitly or
consciously grasped and interiorized content of information
related and meaningfully integrated into a unifying frame of
experience among other information contents interiorized in
the same way, the complex of which reflects subjective un-
derstanding of environment. Mistakes arise from integration
of misinformation or from integration of contradictory infor-
mation into a unifying frame of experience (the second leads
to cognitive dissonance and motivates to seek another infor-
mation). [29] (Michal Lorenz)
Data are perceptible or perceived—if and when the signal
can be interpreted by the ‘user’—attributes of physical, bio-
logical, social or conceptual entities. Information is
recorded and organized data that can be communicated
(Porat & Rubin, 1977). However, it is advisable to distin-
guish between the various states or conditions of informa-
tion (e.g. information-as an object (Buckland, 1991b), or
semantic, syntactic and paradigmatic states (Menou, 1995).
Knowledge is information that is understood, further to its
utilization, stored, retrievable and reusable under appropri-
ate circumstances or conditions. [30] (Michel Menou)
Data are sets of characters, symbols, numbers, and audio/vi-
sual bits that are represented and/or encountered in raw
forms. Inherently, knowledge is needed to decipher data and
turn them into information. Information is facts, figures, and
other forms of meaningful representations that when
encountered by or presented to a human being are used to en-
hance his/her understanding of a subject or related topics.
Knowledge is a reservoir of information that is stored in the
human mind. It essentially constitutes the information that
can be “retrieved” from the human mind without the need to
consult external information sources. [31] (Haidar Moukdad)
Data are raw material of information, typically numeric.
Information is data which is collected together with com-
mentary, context and analysis so as to be meaningful to others.
Knowledge is a combination of information and a person’s
experience, intuition and expertise.[32] (Charles Oppenheim)
Datum is an object or crude fact perceived by the subject,
non-constructed nor elaborated in the consciousness,
without passing through neither analysis processes nor
evaluation for its transfer as information. Information is
aphenomenon generated from knowledge and integrated
therein, analyzed and interpreted to achieve the transfer
process of message (i.e., meaningful content) and the cog-
nitive transformations of people and communities, in a
historical, cultural and social context. Knowledge is a so-
cial and cognitive process formed by the passing or as-
similated information to thought and to action. Message
is the meaningful content of information. [33] (Lena
Vania Pinheiro)
Data are primitive symbolic entities, whose meaning de-
pend on it integration within a context that allow their un-
derstanding by an interpreter. Information is the intentional
composition of data by a sender with the goal of modifying
the knowledge state of an interpreter or receiver. Knowl-
edge is the intelligent information processing by the receiver
and it consequent incorporation to the individual or social
memory (Belkin & Robertson, 1976; Blair, 2002) [34]
(Maria Pinto)
Signs. The distinctive feature of signs is that they denote
something, regardless of whether that something exists or
does not exist, is concrete or abstract, possible or impossible,
a thing or an event, a substance or a determination, an indi-
vidual or a collective. Analysis even of one single sign leads
to a multiplicity of signs and their denoted items. For this
reason, we may say that the sign contains a reference to both
the denoted item considered per se, in isolation, and the con-
texts or situations in which the denoted item appears. And of
these of especial importance are those that, for lack of better
terminology, we can call the proximal context and the distal
context. The proximal context is the net of relations that hold
among the items denoted by signs. On the other hand, the
distal context is the outcome of a categorization procedure.
Its most usual form is that constituted by the reply to ques-
tions like ‘what is this?’, where acceptable replies are of the
type ‘this is an animate being’, ‘this is an artifact’, ‘this is a
property’, etc. This codification of the two types of context
enables me to propose the following distinction between
data and information.
Datum. Def. 1. x is a datum x is a sign that denotes enti-
ties or attributes in a proximal context. In the light of this de-
finition one understands why conventional analyses of con-
sistency and integrity, or procedures of normalization, are
effective techniques for the organization and rationalization
of data. From a technological point of view, relational data-
bases are the currently most advanced products available for
the efficient handling of data.
Information. Def. 2. x is an item of information x is a
datum in a distal context. Definition 2 tells us that informa-
tion is made up of more structured items. That is to say, in-
formation is the embedding of signs-in-a-proximal-context
(i.e., data) in a distal context. Information, thus, adds greater
structure to data. These definitions provide a first explana-
tion for the scant interest aroused by proposals to draw more
exact distinctions between data and information. In effect, in
concrete cases of application, it is often difficult to distin-
guish precisely between distal and proximal contexts.
Conditions of knowledge. Knowledge is apparently not re-
ducible solely to information and data. The problem is to un-
derstand ‘what is lacking’, what must be added to information
and data in order to achieve true knowledge. My claim is that
the meaning of a sign is given by the position of the sign in a
field of signs (in a space). On the other hand, the content of a
sign is given by the position of the item (denoted by the sign)
in a field of items. Data, information, meanings and contents
cover the field of knowledge. This amounts to saying that we
have knowledge when we know (1) which item is denoted by
which sign, (2) the item’s proximal context, (3) the item’s dis-
tal contexts, (4) the sign’s position in the field of signs, (5) the
item’s position in the field of items (Poli, 2001).
Data, information, knowledge, message. I am unable to
understand why data, information, knowledge and message
are placed on the same level of analysis. I would suggest
considering message as the “vehicle” carrying either data or
information (which can be taken as synonymous). Knowl-
edge hints to either a systematic framework (e.g., laws, rules
or regularities, that is higher-order “abstractions” from data)
or what somebody or some community knows (“I know that
you are married”). In this latter sense knowledge presents a
“subjective” side. [35] (Roberto Poli)
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007 485
DOI: 10.1002/asi
Data are a representation of facts or ideas in a formalized
manner, and hence capable of being communicated or ma-
nipulated by some process. So: data is related to facts and
machines (Holmes, 2001). Information is the meaning that a
human assigns to data by means of the known conventions
used in its representation. Information is related to meaning
and humans (Holmes, 2001). [36] (Ronald Rousseau)
Datum is a quantifiable fact that can be repeatedly mea-
sured. Information is an organized collection of disparate
datum. Knowledge is the summation of information into in-
dependent concepts and rules that can explain relationships
or predict outcomes. [37] (Scott Seaman)
Data are raw evidence, unprocessed, eligible to be processed
to produce knowledge. Information is the process of becom-
ing informed; it is dependent on knowledge, which is
processed data. Knowledge perceived, becomes information.
Knowledge is what is known, more than data, but not yet in-
formation. Recorded knowledge may be accessed in formal
ways. Unrecorded knowledge is accessible in only chaotic
ways. [38] (Richard Smiraglia)
Data are discrete items of information that I would call facts
on some subject or other, not necessarily set within a fully
worked out framework. Information is facts and ideas com-
municated (or made available for communication). Knowl-
edge is the considered product of information. Selection as
to what is valid and relevant is a necessary condition of the
acquisition of knowledge. [39 ] (Paul Sturges)
Data are facts that are the result of observation or measure-
ment. (Landry et al., 1970). Information is meaningful data.
Or data arranged or interpreted in a way to provide meaning.
Knowledge is internalized or understood information that
can be used to make decisions. [40] (Carol Tenopir)
Data are unprocessed, unrelated raw facts or artifacts.
Information is data or knowledge processed into relations
(between data and recipient). Knowledge is information
scripted into relations with recipient experiences. [41]
(Joanne Twining)
Data are representations of facts and raw material of infor-
mation. Information is data organized to produce meaning.
Knowledge is meaningful content assimilated for use. The
three entities can be viewed as hierarchical in terms of com-
plexity, data being the simplest and knowledge, the most
complex of the three. Knowledge is the product of a synthe-
sis in our mind that can be conveyed by information, as one
of many forms of its externalization and socialization. [42]
(Anna da Soledade Vieira)
Data are alphabetic or numeric signs, which without context
do not have any meaning. Information is a set of symbols
that represent knowledge. Information is what context cre-
ates/gives to data. It is cognitive. Normally it is understood as
a new and additional element in collecting data and
information for planned action. Knowledge is enriched infor-
mation by a person’s or a system’s own experience. It is cog-
nitive based. Knowledge is not transferable, but through in-
formation we can communicate about it. (Note that the highest
level of information processing is the generation of wisdom,
where various kinds of knowledge are communicated and in-
tegrated behind an action. [43] (Irene Wormell)
Data are artifacts that reflect a phenomenon in natural or so-
cial world in the form of figures, facts, plots, etc. Informa-
tion is anything communicated among living things. It is one
of the three mainstays supporting the survival and evolution
of life, along with energy and materials. Knowledge is a
human construct, which categorize things, record significant
events, and find causal relations among things and/or events,
etc. in a systematic way. [44] (Yishan Wu)
Last but not least, here are my reflections on data, infor-
mation, and knowledge:
Inferential propositional knowledge. In traditional
epistemology, there are three main kinds of knowledge:
practical knowledge, knowledge by acquaintance, and
propositional knowledge (Bernecker & Dretske, 2000).
Practical knowledge refers to skills (i.e., functional abili-
ties, such as driving a car). Knowledge by acquaintance is
direct nonmediated recognition of external physical objects
and organisms (e.g., “this is Albert Einstein”), or the direct
recognition of inner phenomena (e.g., pain, hunger). Propo-
sitional knowledge usually comes in the form of ‘knowing
that’’; S (subject) knows that P (proposition). It is the re-
flective and/or the expressed content of what a person
thinks that he or she knows. Note that the contents of our
reflective and/or expressed thoughts are in the form of
propositions. Propositional knowledge is divided into infer-
ential and non-inferential knowledge. Non-inferential
propositional knowledge refers to direct intuitive under-
standing of phenomena (e.g., ““This is a true love”). Infer-
ential knowledge is a product of inferences, such as induc-
tion and deduction. The field of information science, as well
as any academic field, is composed of inferential proposi-
tional knowledge, as they are published in articles and
books. This analysis is focused on defining “data,” “infor-
mation,” and “knowledge” as they are related and imple-
mented in inferential propositional knowledge.
Subjective versus objective realms. Data (D), informa-
tion (I), and knowledge (K) phenomena have two distinctive
modes of existence; namely, in the subjective and in the ob-
jective realms. Correspondingly, we differentiate between
subjective knowledge and objective knowledge. Note that
subjective knowledge is equivalent here to the knowledge of
the subject or the individual knower, and objective knowledge
is equivalent here to knowledge as an object or a thing. Sub-
jective knowledge exists in the individual’s internal world
(i.e., as a thought), whereas objective knowledge exists in the
individual’s external world (e.g., as it is published in books,
presented in digital libraries, and stored in electronic devices).
In this context, they are not related to arbitrariness and truth-
fulness, which are usually attached to the concepts of subjec-
tive knowledge and objective knowledge. To avoid confusion,
I will use the terms universal knowledge and collective knowl-
edge (i.e., knowledge in the collective realm) rather than ob-
jective knowledge. The distinction between subjective knowl-
edge and universal knowledge differs from the distinction
between private knowledge and public knowledge. Private
knowledge is the individual’s intimate knowledge. These are
thoughts on contents known only to the individual, such as
486 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007
DOI: 10.1002/asi
intimate dreams and feelings, and “hidden agenda” (i.e., hid-
den goals and incentives). Public knowledge refers to thoughts
that the individual consider as knowledge, and they are on
contents known to other people as well (e.g., “2 2 4,”
“Paris is the capital of France”).
Six distinctive concepts. Having established the distinc-
tion between the subjective and the universal domains, we
are in a position to define the three key concepts data, infor-
mation, and knowledge. In fact, we have six concepts to
define, divided into two distinctive sets of three. One set re-
lates to the subjective domain, and the other—to the univer-
sal domain.
D-I-K in the subjective domain. In the subjective do-
main, data are the sensory stimuli, which we perceive
through our senses. Information is the meaning of these
sensory stimuli (i.e., the empirical perception). For example,
the noises that I hear are data. The meaning of these noises
(e.g., a running car engine) is information. Still, there is an-
other alternative as to how to define these two concepts—
which seems even better. Data are sense stimuli, or their
meaning (i.e., the empirical perception). Accordingly, in the
example above, the loud noises, as well as the perception of
a running car engine, are data. Information is empirical
knowledge. Accordingly, in the example above, the knowl-
edge that the engine is now on and the car is leaving is in-
formation, since it is empirically based. Information is a type
of knowledge, rather than an intermediate stage between
data and knowledge. Knowledge is a thought in the individ-
ual’s mind, which is characterized by the individual’s justifi-
able belief that it is true. It can be empirical and non-empirical,
as in the case of logical and mathematical knowledge (e.g.,
“every triangle has three sides”), religious knowledge (e.g.,
“God exists”), philosophical knowledge (e.g., “Cogito ergo
sum”), and the like. Note that knowledge is the content of a
thought in the individual’s mind, which is characterized by
the individual’s justifiable belief that it is true, while “know-
ing” is a state of mind which is characterized by the three
conditions: (1) the individual believe that it is true, (2) S/he
can justify it, and (3) It is true, or it is appear to be true.
D-I-K in the universal domain. In the universal domain,
data, information, and knowledge are human artifacts. They
are represented by empirical signs (i.e., signs that one can
sense through his/her senses). They can take on diversified
forms such as engraved signs, painted forms, printed words,
digital signals, light beams, sound waves, and the like. Uni-
versal data, universal information, and universal knowledge
mirror their cognitive counterparts. Meaning, in the objec-
tive domain data are sets of signs that represent empirical
stimuli or perceptions, information is a set of signs, which
represent empirical knowledge, and knowledge is a set of
signs that represent the meaning (or the content) of thoughts
that the individual justifiably believes that they are true.
Signs Versus meaning. Defining the D-I-K phenomena as
sets of signs needs to be refined. There is a fundamental dis-
tinction between documented (i.e., written, spoken, or physi-
cally expressed) propositions and meanings. “E MC2”,
“E MC2”, and “E MC2” are not three different types of
knowledge. These are three different sets of signs that repre-
sent the same meaning. In other words, they are three different
utterances of the same knowledge. Knowledge, in the collec-
tive domain, is the meaning that is represented by written and
spoken statements (i.e., sets of symbols). However, because
we cannot perceive with our senses the meaning itself, which
is an abstract entity, we can relate only to the sets of signs (i.e.,
written, spoken, or physically expressed propositions), which
represent it. Apparently, it is more useful to relate to the data,
information, and knowledge as sets of signs rather than as
meaning and its building blocks (Zins, 2004, 2006). [45]
(Chaim Zins)
Conceptual Approaches
Delimitations
Before presenting the analysis of the panel’s definitions let
me clarify the methodological considerations that guide me
while analyzing the panel’s diversified definitions. First, words
can be misleading. Definitions are theory-laden. They can best
be analyzed and evaluated in the context of the relevant theory.
For this very reason, the definitions are grouped here according
the contributing scholars rather than the defined concepts. The
scholar-based organization facilitates a better understanding of
the rationale and the interrelations among the three concepts,
as they are understood and defined by each scholar.
Second, many of the 45 citations reflect systematic and
comprehensive thinking and are based on solid theoretical and
philosophical foundations. However, a few are incomplete, in-
consistent, logically faulty, and philosophically problematic.
For this very reason, the study is focused on mapping the theo-
retical issues that we face while formulating coherent concep-
tions of data, information, and knowledge, and the conceptual
approaches to resolve them, rather than on evaluating the ac-
curacy, adequacy, and coherency of the panel’s definitions.
Anthropological Document
Forty-five scholars (including the researcher) shared their
thoughts and formulated about 130 definitions. This collection
of definitions is an invaluable “anthropological document” that
documents the conceptions of D-I-K, as they are understood by
leading scholars in the information science academic commu-
nity. Again, the definitions are rooted in diversified theoretical
grounds. Many of them reflect systematic and comprehensive
thinking and are based on solid theoretical and philosophical
foundations. A few, though, are incomplete, inconsistent, logi-
cally faulty, and philosophically problematic. Apparently, the
definitions show that the academic community speaks in dif-
ferent languages. Still, they provide the basis for mapping the
various conceptual approaches for defining data, information,
and knowledge in the context of information science.
Metaphysical Versus Nonmetaphysical Approaches
The most basic distinction is between metaphysical and
nonmetaphysical approaches. Metaphysical approaches refer
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007 487
DOI: 10.1002/asi
to data, information, or knowledge as metaphysical phenom-
ena. They reflect metaphysical postulates, such as “knowl-
edge is eternal,” and “knowledge is an independent entity/ob-
ject,” as well as religious beliefs, such as “God knows. . . .”
Obviously, for Information Science, all the panel members
unanimously implement nonmetaphysical approaches. Meta-
physical approaches, though, emerged mainly in theoretical
reviews (see, for example, citation [8]).
Human Exclusive Versus Nonexclusive Approaches
We zoom into nonmetaphysical approaches. Nonmeta-
physical approaches are divided into those exclusively cen-
tered on humans and those that ascribe the D-I-K phenomena
to non-human biological (e.g., animals and plants) and/or to
physical (e.g., planets, robots, ) phenomena as well. Citation
[20] exemplifies a non human-exclusive approach by using
the phrase “organism or intelligent agent” rather than “per-
son or human.” Apparently, nearly all the panel members
adopt human-exclusive approaches for defining D-I-K in the
context of information science.
Human-Centered Approaches
We zoom into human-exclusive approaches. Three classifi-
cations emerge as highly relevant. The first classification is
between cognitive exclusive versus nonexclusive approaches.
The second classification is between “propositional” exclu-
sive versus nonexclusive approaches. The third classification
is between the subjective domain versus the objective, or
rather universal domain.
Cognitive-based exclusive versus nonexclusive approaches.
Human-centered approaches are divided into those that refer
to D-I-K exclusively as cognitive phenomena and those that
refer to D-I-K in terms of cognitive, biological, or physical
phenomena, mutatis mutandis. The division between
cognitive-based exclusive approaches and nonexclusive
approaches emerges when one compares, for example,
Hjørland’s definition of information (see citation [25])” with
Poli’s definition of information (see citation [35]). Hjørland
defines information in terms of biological mechanism and
signals, while Poli defines information in terms of signs
and meanings.
Note that the term cognitive approaches should be refined
to cognitive-based approaches because it applies to human
thoughts and states of mind as well as to the human artifacts
that represent them (e.g., books, digital signals). Debons’
definition of information exemplifies a cognitive-based
approach. According to Debons, “Information represents a
state of awareness (consciousness) and the physical mani-
festations they form” (citation [11]). Albrechsten’s definition
of information (see citation [1]) also exemplifies a cognitive-
based approach because the contents of databases gain their
status of information by relating to “meaning and human in-
tention.” Another example is Harmon’s definition of data (see
citation [20]). The first part (“Data is one or more kinds of
energy waves or particles (light, heat, sound, force, electro-
magnetic)”) creates the deceptive impression that it exempli-
fies a physical-based approach for defining data. However,
the second part of the definition (“selected by a conscious or-
ganism or intelligent agent on the basis of a pre-existing
frame or inferential mechanism in the organism or agent.”)
makes Harmon’s definition an example of a cognitive-based
approach. Apparently, nearly all the panel members adopt
cognitive-based approaches for defining D-I-K in the con-
text of information science.
Propositional exclusive versus nonexclusive approaches. We
zoom into cognitive-based approaches. It seems that nearly all
the panel’s definitions presented above explicitly or implicitly
reflect propositional conceptions, although only I (see citation
[45]) specifically use the term propositional knowledge. The
concept of propositional conceptions originated from the dis-
tinction among various types of knowledge (i.e., practical
knowledge, knowledge by acquaintance, and propositional
knowledge, inferential and noninferential). Although the panel
did not specifically refer to the various types of knowledge, a
distinction should be made between focusing on propositional
knowledge as against dealing with all types of knowledge.
Propositional conceptions are those conceptions that refer to
D-I-K exclusively in the form of propositions and their build-
ing blocks. Apparently, it seems that the propositional concep-
tual approaches are embedded in the cognitive approaches.
The Mainstream of the Field
At this point, we can characterize the most common con-
ceptual approach for defining data, information, and knowl-
edge in the context of information science. Undoubtedly, the
most common conceptual approach that represents the main-
stream of the field is characterized as the non-metaphysical,
human-centered, cognitive-based, propositional approach.
Models for defining D-I-K. We zoom into non-metaphysical,
human-centered, cognitive-based, propositional approaches.
The third division, which is the division between the
subjective domain (SD) versus the universal domain (UD),
establishes the theoretical ground for formulating generic
models for defining D-I-K. The division between
D-I-K in the subjective domain, namely, D-I-K as inner phe-
nomena bound in the mind of the individual knower versus
the universal domain (UD), namely, D-I-K as external phe-
nomena to the mind of the individual knower was presented
earlier in this article (see citation [45], for further discussion
see Zins, 2004, 2006). The different combinations of D-I-K
phenomena in the universal domain and in the subjective
domain establish the ground for formulating different models.
By analyzing the 45 citations one can realize that in most
citations data are characterized as phenomena in the
universal domain, and knowledge is characterized as phe-
nomena in the subjective domain, though in many cases
these interpretations are not exclusive. This significantly limits
488 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007
DOI: 10.1002/asi
the number of optional models for defining D-I-K that can
present the mainstream of the field. To be precise, I spotted at
least five different models (see Figure 1).
1. The first model is UD: D-I; SD: K; meaning: D-I are ex-
ternal phenomena; K are internal phenomena. This model
is the most common one. The model is implemented in
citations [17], [40], and [43]. It underlies the rationale of
the name Information Science; that is, Information Sci-
ence is focused on exploring data and information, which
are seen external phenomena. It does not explore knowl-
edge, which are seen as internal phenomena.
2. The second model is UD: D; SD: I-K; meaning: D are
external phenomena; I-K are internal phenomena. Cita-
tions [5] and [20] exemplify the model.
3. The third model is UD:D-I-K; SD:I-K; meaning: D are ex-
ternal phenomena; I-K phenomena can be in both domains,
external or internal. Citation [6] exemplifies the model.
4. The fourth model is UD:D-I; SD:D-I-K; meaning: D-I
phenomena can be in both domains, external or internal; K
phenomena are internal. Citation [1] exemplifies the model.
5. The fifth model is UD: D-I-K; SD: D-I-K; meaning: D-I-K
phenomena can be in both domains, universal (i.e., exter-
nal) or subjective (i.e., internal). Citations [11] and [45]
exemplify the model.
The reader may refine my analysis, and may revise my in-
terpretation of the exemplary citations. Still, formulating
comprehensive and systematic definitions of data, informa-
tion, and knowledge requires reflection on these two domains
(S-U) and their key role in shaping our conceptions on these
three constitutive concepts (D-I-K) of information science.
AConcluding Remark
This study maps the major issues on the agenda of schol-
ars engaged in exploring and substantiating the foundations
of Information Science. Conceptual approaches were identi-
fied and formulated for defining data, information, and
knowledge. This might help the reader to a better under-
standing of the issues and the considerations involved in es-
tablishing the foundations of Information Science; however,
by no means does it replace the personal quest to ground
one’s positions on solid theoretical foundations
Acknowledgments
I would like to thank the Israel Science Foundation for a
research grant that made the study possible (2003–2005).
However, what made the difference were my 57 colleagues
who participated in this exhausting and time-consuming
study as panel members. Their invaluable contributions have
made this study really important, and I am truly grateful.
Special thanks go to Prof. Anthony Debons and Prof. Glynn
Harmon for their deep reflections throughout the study. The
study was conducted at Bar-Ilan University.
References
Belkin, N.J., & Robertson, S.E. (1976). Information science and the phe-
nomenon of information. Journal of the American Society for Informa-
tion Science, 27, 197–204.
Bernecker, S., & Dretske, F. (Eds.). (2000). Knowledge: Readings in con-
temporary epistemology. Oxford: Oxford University Press.
Blair, D.C. (2002). Knowledge management: Hype, hope or help? Journal
of the American Society for Information Science and Technology,
53(12), 1019–1028.
Bohm, D. (1980). Wholeness and the implicate order. New York: Routledge
& Kegan Paul.
Borgmann, A. (1999). Holding on to reality: The nature of information at
the turn of the millennium. Chicago: University of Chicago Press.
Buckland, M. (1991a). Information and information systems. New York:
Greenwood Press.
Buckland, M. (1991b). Information as thing. Journal of the American
Society of Information Science, 42(5), 351–360.
Capurro, R. (1978). Information. Ein Beitrag zur etymologischen und
ideengeschichtlichen Begründung des Informationsbegriffs [A contribution
to the etymological and conceptual history of the concept of information].
München: Saur Verlag.
Capurro, R., & Hjørland, B. (2003). The concept of information. Annual
Review of Information Science and Technology, 37(8), 343–411.
Davis, G.B., & Olson, M.H. (1985). Management information systems.
New York: McGraw Hill.
Debons, A., Horne, E., & Cronenweth, S. (1988). Information science: An
integrated view. New York: G.K. Hall.
Dreyfus, H. (2001) On the Internet. New York: Routledge.
Ess, C. (2003). Liberal arts and distance education: Can Socratic virtue
(arete) and Confucius’ exemplary person (junzi) be taught online? Arts
and Humanities in Higher Education, 2(2), 117–137.
Ess, C. (2004). Computing in philosophy and religion. In S. Schreibman,
R.G. Siemens, & J. Unsworth (Eds.), A companion to digital humanities
(pp. 132–142). Oxford: Blackwell.
Hjørland, B. (2000). Documents, memory institutions, and information
science. Journal of Documentation, 56(1), 27–41.
Hjørland, B. (2002). Principia informatica. Foundational theory of informa-
tion and principles of information services. In H. Bruce, R. Fidel, P. Ing-
wersen, & P. Vakkari (Eds.), Emerging frameworks and methods. Pro-
ceedings of the Fourth International Conference on Conceptions of
Library and Information Science (CoLIS4) (pp. 109–121). Greenwood
Village, CO: Libraries Unlimited.
Holmes, N. (2001). The great term robbery. Computer, 34(5), 94–96.
Karpatschof, B. (2000). Human activity—Contributions to the anthropolog-
ical sciences—From a perspective of activity theory. Copenhagen: Dansk
Psykologisk Forlag.
Landry, B.C., Mathis, B.A., Meara, N.M., Rush, J.E., & Young, C.E. (1970).
Definition of some basic terms in computer and information science, Journal
of the American Society for Information Science, 24(5), 328–342.
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007 489
DOI: 10.1002/asi
Model 1 Model 2 Model 3 Model 4 Model 5
UD SD UD SD UD SD UD SD UD SD
DD D DDDD
IIIIII I I
KKKKKKK
FIG. 1. Four models for defining data (D)–information (I)–knowledge (K).
Landry, B.C., & Rush, J.E. (1970). Toward a theory of indexing II. Journal
of the American Society for Information Science 21, 358–367.
Le Coadic, Y.F. (2004). La science de l’information [Information science]
Collection Que sais-je? (No. 2873). Paris: PUF.
Luhmann, N. (1996). Soziale Systeme. Frankfurt am Main: Suhrkamp.
Menou, M.J. (1995). The impact of information (Part 2): Concepts of informa-
tion and its value. Information Processing and Management, 31(4), 479–490.
Morris, C.W. (1938). Foundations of the theory of signs. Chicago: The Uni-
versity of Chicago Press.
O’Leary, S.D., & Brasher, B.E. (1996). The unknown God of the Internet:
Religious communication from the ancient agora to the virtual forum. In
C. Ess (Ed.), Philosophical perspectives on computer-mediated commu-
nication (pp. 233–269). Albany, NY: State University of New York Press.
Pagels, H. (1988). The dreams of reason. New York: Simon and Schuster.
Peirce, C.S. (1931). Collected papers of Charles Sanders Peirce. C.
Hartshorne & P. Weiss (Eds.) (Vol. I–VI). Cambridge, MA: Harvard
University Press.
Peirce, C.S. (1958). Writings of Charles S. Peirce. A chronological edition.
A.W. Burke (Ed.) (Vol. VII–VIII). Bloomington: Indiana University Press.
Poli, R. (2001). ALWIS. Ontology for knowledge engineers. Unpublished
doctoral dissertation, University of Utrecht, the Netherlands.
Porat, M.V., & Rubin, M. (1977). The information economy: Definition
and measurement (OT Special publication, Vol. 1, pp. 77–120).
Washington DC: Office of Telecommunications, U.S. Department of
Commerce.
Rush, J.E., & Davis, C.H. (2006). Guide to information science and tech-
nology. Manuscript in preparation.
S
ˇmajs, J., & Krob, J. (2003). Evoluc
ˇní ontologie [Evolutionary ontology].
Brno: Masaryk University.
Sowell, T. (1996). Knowledge and decisions. New York: Basic Books.
Stonier, T. (1993). The wealth of information. London: Thames/Methuen.
Stonier, T. (1997). Information and meaning—An evolutionary perspective.
Berlin: Springer.
Wellisch, H.H. (1996). Abstracting, indexing, classification, thesaurus.
construction: A glossary. Port Aransas, TX: American Society of
Indexers.
Wersig, G., & Neveling, U. (1975). Terminology of documentation: A
selection of 1200 basic terms. Paris: The UNESCO Press.
Yovits, M.C., & Ernst, R.L. (1970). Generalized information systems: Con-
sequences for information transfer. In H.B. Pepinsky (Ed.), People and
information (pp. 1–31). Elmsford, NY: Pergamon Press.
Zins, C. (2004). Knowledge mapping: An epistemological perspective.
Knowledge Organization, 31(1), 49–54.
Zins, C. (2006). Redefining information science: From information science
to knowledge science. Journal of Documentation, 62(4), 447–461.
Zins, C. (2007a). Conceptions of information science. Journal of the American
Society for Information Science and Technology, 58(3), 335–350.
Zins, C. (2007b). Knowledge map of information science. Journal of the
American Society for Information Science and Technology, 58(4), 526–535.
Zins, C. (in press). Classification schemes of information science: Twenty-
eight scholars map the field. Journal of the American Society for Infor-
mation Science and Technology.
Appendix A
The Panel
Dr. Hanne Albrechtsen, Institute of Knowledge Sharing,
Copenhagen, Denmark; Prof. Elsa Barber, University of
Buenos Aires, Argentina; Prof. Aldo de Albuquerque
Barreto, Brazilian Institute for Information in Science and
Technology, Brazil; Prof. Shifra Baruchson–Arbib, Bar
Ilan University, Ramat-Gan, Israel; Prof. Clare Beghtol,
University of Toronto, Canada; Prof. Maria Teresa Biagetti,
University of Rome 1, Italy; Prof. Michael Buckland,
University of California, Berkeley, CA; Mr. Manfred
Bundschuh, University of Applied Sciences, Cologne,
Germany; Dr. Quentin L. Burrell, Isle of Man Interna-
tional Business School, Isle of Man; Dr. Paola Capitani,
Working Group Semantic Web, Italy; Prof. Rafael Capurro,
University of Applied Sciences, Stuttgart, Germany;
Prof. Thomas A. Childers, Drexel University, Philadelphia,
PA; Prof. Charles H. Davis, Indiana University;
Prof. Anthony Debons, University of Pittsburgh,
Pittsburgh, PA; Prof. Gordana Dodig-Crnkovic,
Mälardalen University, Västerås/Eskilstuna, Sweden; Prof.
Henri Dou, University of Aix-Marseille III, France; Prof.
Nicolae Dragulanescu, Polytechnics University of
Bucharest, Romania; Prof. Carl Drott, Drexel University,
Philadelphia, PA; Prof. Luciana Duranti, University of
British Columbia, Canada; Prof. Hamid Ekbia, University
of Redlands, Redlands, CA; Prof. Charles Ess, Drury Uni-
versity, Springfield, MO; Prof. Raya Fidel, University of
Washington, Seattle, WA; Prof. Thomas J. Froehlich, Kent
State University, Kent, OH; Mr. Alan Gilchrist,Cura Con-
sortium and TFPL, London, UK; Dr. H.M. Gladney,HMG
Consulting, McDonald, PA; Prof. Glynn Harmon,Univer-
sity of Texas at Austin, Austin, TX; Dr. Donald Hawkins,
Information Today, Medford, NJ; Prof. Caroline
Haythornthwaite,University of Illinois at Urbana Cham-
paign, Urbana, IL; Mr. Ken Herold,Hamilton College,
Clinton, NY; Prof. William Hersh, Oregon Health &
Science University, Portland, OR; Prof. Birger Hjorland,
Royal School of Library and Information Science, Copen-
hagen, Denmark; Ms. Sarah Holmes,* the Publishing
Project, USA; Prof. Ian Johnson,* the Robert Gordon
University, Aberdeen, UK; Prof. Wallace Koehler,Val-
dosta State University, Valdosta, GA; Prof. Donald Kraft,
Louisiana State University, Baton Rouge, LO; Prof. Yves
François Le Coadic, National Technical University, Lyon,
France; Dr. Jo Link-Pezet,Urfist, and University of Social
Sciences, France; Mr. Michal Lorenz,Masaryk University
in Brno, Czech Republic; Prof. Ia McIlwaine,University
College, London, UK; Prof. Michel J. Menou,Knowledge
and ICT management consultant, France; Prof. Haidar
Moukdad,Dalhousie University, Halifax, Nova Scotia,
Canada; Mr. Dennis Nicholson,Strathclyde University,
UK; Prof. Charles Oppenheim,Loughborough University,
Leicestershire, UK; Prof. Lena Vania Pinheiro,Brazilian
Institute for Information in Science and Technology, Brazil;
Prof. Maria Pinto, University of Granada, Spain; Prof.
Roberto Poli, University of Trento, Italy; Prof. Ronald
Rousseau, KHBO, and University of Antwerp, Belgium;
Dr. Silvia Schenkolewski–Kroll, Bar Ilan University,
Ramat-Gan, Israel; Mr. Scott Seaman,* University of
Colorado, Boulder, CO; Prof. Richard Smiraglia, Long Is-
land University, Brookville, NY; Prof. Paul Sturges,
Loughborough University, Leicestershire, UK; Prof. Carol
Tenopir, University of Tennessee, Knoxville, TN; Dr.
Joanne Twining, Intertwining.org, a virtual information
490 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007
DOI: 10.1002/asi
*An observer (i.e., those panel members who did not strictly meet the
criteria for the panel selection and terms of participation).
consultancy, USA; Prof. Anna da Soledade Vieira, Federal
University of Minas Gerais, Belo Horizonte, Minas Gerais,
Brazil; Dr. Julian Warner, Queen’s University of Belfast,
UK; Prof. Irene Wormell, Swedish School of Library and
Information Science in Boräs, Sweden; Prof. Yishan Wu,
Institute of Scientific and Technical Information of China
(ISTIC), Beijing, China.
Appendix B
Excerpts From the Three Questionnaires on Data,
Information, and Knowledge
Knowledge Map of Information Science: Issues,
Principles, Implications
(First Round, December 15, 2003)
. . .
3: Data, Information, Knowledge
Three related concepts, “data,” information,” and
“knowledge,” emerge in the context of information science.
The academic and professional IS literature supports diver-
sified meanings for each concept. We begin the conceptual
analysis by trying to define these concepts.
Data. Data (the plural form of the Latin word datum,
which means “the given”).
Question 3.1
What are “data”? (Please, define the concept; Refer to
theoretical background. Thanks)
Answer 3.1
Data are (or datum is). . .
Information.
Question 3.2
What is “information”? (Please define the concept;
refer to theoretical background)
Answer 3.2
Information is. . .
Knowledge.
Question 3.3
What is “knowledge”? (Please define the concept;
refer to theoretical background.)
Answer 3.3
Knowledge is. . .
Interrelations. “Data,” “information,” and “knowledge”
are interrelated. Discussions among scholars focus on the
nature of the relations among these key concepts, as well as
on their meanings.
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007 491
DOI: 10.1002/asi
Sequential order. Many scholars claim that data, infor-
mation, and knowledge are part of a sequential order. Data
are the raw material for information, and information is the
raw material for knowledge. However, if this is the case,
then “information science” should explore data (informa-
tion’s building blocks) and information, but not knowledge,
which is an entity of a higher order.
Nevertheless, it seems that information science does ex-
plore knowledge since it includes two sub-fields, “knowl-
edge organization”, and “knowledge management”. I am
confused. Should we refute the sequential order? Should we
change the name of the field from “Information Science” to
“Knowledge Science”? Or should we perhaps exclude the
fields of knowledge organization and knowledge manage-
ment from information science?
Question 3.4 Are data, information, and knowledge
part of a sequential order?
(Please explain.) If yes, please explain how it is that
“knowledge organization” and “knowledge man-
agement” are sub-fields of information science?
Answer 3.4
Knowledge vs. information. Another common view is
that knowledge is not conveyed by information. Knowledge
is the product of a synthesis in our mind. If this is the case,
we should exclude the fields of knowledge organization and
knowledge management from information science. Besides,
is Albert Einstein’s famous equation “E MC2” (which is
printed on my computer screen) information or knowledge?
Is “2 2 4” information or knowledge?
Question 3.5 Is knowledge not conveyed by
information? (Please explain and elaborate).
Answer 3.5
Synonyms. The alternative view that “information” and
“knowledge” are synonyms is problematic too. If “informa-
tion” and “knowledge” are synonyms, should not we use the
term “knowledge science” rather than “information science”?
Question 3.6 Are “information” and “knowledge”
synonyms? If yes, how do you explain the name “in-
formation science”? (Please explain and elaborate.)
Answer 3.6
The researcher’s views. At this point, I present my con-
ceptions to the panel. If you want to receive a detailed paper,
please contact me.
Propositional knowledge. In traditional epistemology
there are three kinds of knowledge: practical knowledge
(i.e., skills), knowledge by acquaintance (i.e., knowing a
person or a thing), and propositional knowledge (i.e., in the
form of propositions). Propositional knowledge is divided
into inferential and non-inferential. Inferential knowledge is
a product of inferences, such as induction and deduction. We
are zooming in on inferential propositional knowledge.
Information science, like all academic fields, is composed
of inferential propositional knowledge.
Two approaches. There are two basic approaches to define
“knowledge”: in the subjective domain (i.e., as a thought in the
subject’s mind) and in the objective domain (i.e., as an object).
Note that the terms “subjective” and “objective” are not used
here as we use them in our daily life. “Subjective” means ‘ex-
isting in the mind’ (not ‘arbitrary’). “Objective” means ‘exist-
ing as an independent object (or a thing)’ (not ‘unbiased’).
The subjective domain. The first approach conditions
the knowledge in the individual’s (or subject’s) mind.
Knowledge is a thought. It is characterized as “a justified
true belief.” Generally, we can identify subjective proposi-
tional knowledge by the certainty of the individual that
his/her own thoughts are true, and by his/her ability to base
this certainty on a sound justification (e.g., experiments,
observations, and logical inferences).
(Note that in the subjective realm “knowledge” is the
content of a justified true thought, while “knowing” is the
state of mind that is characterized by three conditions: justi-
fication, belief, and truth.)
The objective domain. The second approach ascribes an
independent objective existence to knowledge. Knowledge
is a collection of concepts, arguments, and rules of infer-
ence. They are true and exist independently of the subjective
knowledge of the knowing individual. This is the case, for
example, of arguments published in books.
The field of information science, like any academic
field, is composed of objective propositional knowledge,
as it is recorded, documented, and represented in the
professional and the academic literature. This is what we
explore and map in this collective research enterprise.
Mutual dependency. Paradoxically, the subjective and the
objective domains are complementary. On the one hand, ob-
jective knowledge is the product of outputting (externalizing,
recording, or documenting) subjective knowledge. (One
might say, “this questionnaire is an output of my brain”.) On
the other hand, the realization of objective knowledge necessi-
tates the consciousness of at least one individual knower.This
is crucial. The term “objective domain” is equivalent here to
“collective domain”. Objective knowledge is collective, in the
phenomenological sense, not in the metaphysical sense.
Six concepts. Having established the distinction between
the subjective and the objective domains, we have six con-
cepts to define, divided into two distinctive sets of three.
One set relates to the subjective domain, the other to the
objective (i.e., collective) domain.
The subjective domain. In the subjective domain, “data”
and “information” acquire two alternative meanings. The
first option:
Data” are the sensory stimuli that we perceive through
our senses. “Information” is the meaning of these sensory
492 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007
DOI: 10.1002/asi
stimuli (i.e., the empirical perception). Example: The noises
that I hear are data. The meaning of these noises, for exam-
ple, a running car engine, is information.
The second option (which I personally prefer):
Data” are the sense stimuli, or their meaning (i.e., the
empirical perception). Accordingly, in the example above
the perception of a running car engine, as well as the noises
of a running car engine, are data.
Information” is empirical knowledge. Accordingly, in
the example above the knowledge that the engine is now on
is information, since it is empirically based. As one can see,
information is a type of knowledge (i.e., empirical knowl-
edge), rather than an intermediate stage between data and
knowledge.
Knowledge”, as mentioned above, is a thought in the in-
dividual’s mind, which is characterized by the individual’s
justifiable belief that it is true. It can be empirical (e.g., “It is
a rainy day”) and non-empirical, as in the case of logical and
mathematical knowledge (e.g., “Every triangle has three
sides”), religious knowledge (e.g., “God exists”), philosoph-
ical knowledge (e.g., “Cogito ergo sum”), and the like.
The objective domain. Objective data, objective infor-
mation, and objective knowledge mirror their cognitive
counterparts. They are represented by empirical symbols,
and can have diversified forms such as engraved signs,
painted forms, printed words, digital signals, light beams,
sound waves, and the like.
Data” are sets of symbols that represent empirical per-
ceptions.
Information” is a set of symbols that represent empiri-
cal knowledge.
Knowledge” is a set of symbols that represent thoughts
that the individual justifiably believes are true.
Question 3.7
Do you accept these conceptions? If you have com-
ments, observations, or critical reflections, please
share them with the panel. Thanks.
Answer 3.7
Question 3.8
If you have different and elaborate conceptions, please
share them with the panel. Thanks.
Answer 3.8
. . .
Knowledge Map of Information Science:Issues,
Principles, Implications
(Third Round, October 8, 2004)
. . .
2: Data, Information, Knowledge, Message
Data, Information, Knowledge, Message. In the first
and the second rounds I received 20 pages of definitions.
While analyzing the responses I found inconsistencies
among the definitions, the conceptions of IS and the IS clas-
sification schemes (see section 4).
However, if one analyzes the 20 pages, one can iden-
tify and formulate several distinct models for defining each
of these four key concepts.
Question 2.1 If you want to revise your definitions,
please do so. Thanks.
Answer 2.1
A. Data is..
B. Information is..
C. Knowledge is..
A. Message is..
. . .
7: Selected Responses
In this section I present selected responses on various
topics.
I received hundreds of detailed answers. I will relate to all
of them in future publications. Evidently, I can present here
only few of your invaluable contributions.
Ad hoc definitions.
7.1
Acomment on “ad hoc definitions”: not all data in my view
are empirical perceptions. For example within computer
science “input data” can be anything like: names, numbers
(totally unrelated to any empirical perceptions, like series of
prime numbers and similar).
Raw data (sometimes called source data or atomic data) is
data that have not been processed for use. They might be the
result of empirical perceptions as well as chosen sets of
symbols which are to be processed to obtain some kind of
information. An example is the computer program input
data. They can be any set of symbols chosen for “informa-
tion processing”. Here we explain the concept “data” with
another concept “information processing” that is not
defined. Circularity in definitions seems to be unavoidable.
Within computer science circularity (or recursivity) is ac-
ceptable as long as it ends somewhere in some trivial base
case. To find the analogous situation for those definitions
one might need more time. . .
If “Information” is a set of symbols that represent empirical
knowledge,so that information is knowledge representa-
tion, we use the concept of knowledge that is higher order to
explain information that is a more basic concept. Again there
must be a way to include non-empirical knowledge. Infor-
mation derived from some empirical knowledge might
still be only an information, and nevertheless non-empiri-
cal. Again within computer science there is an abundance of
examples of usage of the term “information” not meaning
any directly empirical knowledge.
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—February 15, 2007 493
DOI: 10.1002/asi
It seems to me that the way of usage defines what is to be
seen as data /information/ knowledge. I can imagine that the
same set of symbols can play any of those roles depending
on the usage.
Researchers comment: Thank you for your clarifica-
tions. I will elaborate the ad hoc definitions in the final
analysis of the panel responses.
Information & truth.
7.2
I wonder if in the definition of “information” you have any
constraints on the truth value of information. Sometimes
claims of the necessity of the strong definition of informa-
tion are made (Luciano Floridi), i.e. the information must
necessarily be TRUE in order to qualify as information.
How do you view that question?
Researchers comment: Since “information” is defined
here as empirical knowledge, and “knowledge” is defined as
“a justified true belief,” information must be perceived
by the informed person, at the relevant time, to be true.
Evidently, s/he might be wrong.
Clarifications. Finally, I would like to clarify several is-
sues raised by the panel.
1. Popper’s World 3. I am not “Popperian.” In fact, I am a
phenomenologist. Generally, I follow Edmund Husserl’s
phenomenology.
2. Subjectivity vs. Objectivity. We always know the objec-
tive through our subjective mind. Meaning is formed
subjectively by individuals.
3. Symbols vs. meaning. There is a fundamental distinc-
tion between documented (i.e., written, spoken or physi-
cally expressed) propositions and meaning. “E MC2”,
“ E MC2”, and “E MC2” are not three different
‘knowledges’ (pl. of knowledge). These are three differ-
ent sets of symbols (or characters) that represent the
same meaning. In other words, these are three different
utterances of the same knowledge. Knowledge, in the
collective domain, is the meaning, which is represented
by written and spoken statements (i.e., sets of symbols).
However, since we cannot perceive with our senses the
meaning itself, we can relate only to the sets of symbols,
which represent the meaning. Note, however, that al-
though the knower ascribes a universal status to the
meaning, s/he cannot be certain if it really exists outside
his/her own mind [As I noted above, I am not “Popper-
ian.” Actually, I hold an agnostic position:‘I don’t
know’]. Apparently, it is more fruitful to define “D”, “I”,
“K”, and “M” as sets of symbols rather than as meanings.
Question 7.1 If you have critical reflections on the
responses, please let me know.
Thanks.
Answer 7.1
... It is common sense that data is the primary building block for both information and knowledge (Zins, 2007b). Data, information, and knowledge are the major components of information science (Zins, 2007a). ...
Article
Full-text available
The environmental sciences work with datasets every day. Recently, data sharing has become a more familiar activity for academic researchers. Records of marine litter are scarce and generally difficult to find worldwide, especially in databases. This work reviews and analyzes data repositories to identify the existence of datasets related to marine litter in Brazil. Only one global repository specializing in marine litter was found, and it is in the early stages of operation. Only two datasets about marine litter in Brazil were found in the generalist repository Figshare that do not follow all the FAIR principles (Findable, Accessible, Interoperable, and Reusable) for data sharing. A few initiatives are being developed aiming to collect and share marine litter data, but only one of them (Our Blue Hands) is already in place and uses a standardized, replicable method, and aims to share the data by design. Our work identified interoperability as the main point to be tackled within our context. In the UN Decade of Ocean Science for Sustainable Development (2021–2030), it is essential that repositories are created, improved, and encouraged to address the specific needs of marine litter data-sharing and researchers' behavioral shift to start sharing the data already collected. Data sharing not only allows for the integrated vision of the academic community but can also contribute to public policies, helping decision-makers and encouraging a more sustainable science regarding financial and natural resource use.
... Zins [80] explains the difference between 'empirical knowledge' (or 'know-how') (the understanding of entrepreneurship) and explicit knowledge ('know-that') (behaving as an entrepreneur) as an epistemology of learning. Both know-how and know-that are embedded in the "learning about" and "learning for" (how to do or be) which shows a typical student's EE journey [3]. ...
Article
Full-text available
This article explores innovations in and pedagogical approaches to Responsible Entrepreneurship Education (REE), with a specific focus on how to advance responsible entrepreneurial competencies (“know-how”) and entrepreneurial practices (“know-that”). Consequently, this article proposes the “4Rs” framework (re-imagining, reconfiguring, reshaping, and reforming) to guide entrepreneurship educators’ actions. Firstly, it is necessary to “re-imagine” the intended and enacted curriculum to develop a contemporary awareness and knowledge of social and environmental enterprises. Secondly, it is essential to “reconfigure” teaching pedagogies to problematize the entrepreneurship environment and outer world. Thirdly, it is required for educators to “reshape” the attained curriculum with the stakeholders to offer learners co-curricular and extracurricular experiences. Finally, pedagogical “reforms” provide an opportunity to incorporate innovations into the discovery of new knowledge and paths of responsibilities. These pedagogical approaches support entrepreneurial learning as “processes” and entrepreneurship as a “process” aligned to the achievement of responsible entrepreneurial behavior.
... Em 2007, Chaim Zins compilou 130 definições de Data-Informação-Conhecimento a partir de um estudo Delphi com 45 académicos, para chegar à conclusão de que a abordagem conceptual mais comum para definir dados, informação e conhecimento é a área da CI e, portanto, a definição mais usada é, indubitavelmente, "characterized as the nonmetaphysical, human-centered, cognitive-based, propositional approach." (Zins, 2007b). ...
Thesis
Full-text available
Com o objetivo de retraçar, analisar e compreender os processos informacionais que possibilitam um entendimento integrado do contexto de produção de conhecimento botânico na Universidade de Coimbra (UC), a presente tese parte de um quadro teórico e conceptual (conceitos operatórios do domínio científico, paradigmas e teorias) relativo às noções de informação e sistema no âmbito da Ciência da Informação (CI). Com base na Teoria Geral dos Sistemas (TGS) e na Teoria da Complexidade (TC), procura-se interpretar a realidade da produção do conhecimento botânico na UC como um todo informacional, holístico e complexo. Os processos de produção de conhecimento botânico em Portugal conheceram forte impulso com a reforma pombalina da UC em 1772 que inauguram formalmente os estudos sistematizados das ciências naturais com a criação da faculdade de Filosofia e os seus estabelecimentos anexos: Gabinete de História Natural, Gabinete de Física Experimental, Laboratório Químico e Jardim Botânico (JBUC). Ao longo dos seus quase 250 anos de história, as mudanças ideológico-políticas, os progressos tecnológicos e as sucessivas reformas do ensino público em Portugal levaram à transformação da faculdade de Filosofia em faculdade de Ciências (1911) com o Instituto Botânico (IBUC) e posteriormente à Faculdade de Ciências e Tecnologia (1972) com o Departamento de Botânica (DBUC). Com base na metodologia qualitativa, assente na revisão da literatura e no estudo de caso, parte-se da realidade complexa representada pela informação botânica gerada na UC, o que implicou a análise diacrónica e o estudo orgânico e funcional dos seus componentes: Departamento de Ciências da Vida (DCV) e seus antecessores (1772-2008), a par do Jardim Botânico da Universidade de Coimbra (JBUC) (1772-2015) e da Sociedade Broteriana (SB) (1881-2010). A visão sistémica e holística de conjuntos (por área de saber) e totalidades (na inter-relação das partes com o todo) aplicada ao processo e fenómeno informacional implica o estudo da relação entre as partes (estruturas ordenadas) e o todo, e entre as partes entre si (suas funções), e entre si e o meio ambiente. Ao considerar a informação botânica da UC como um Sistema de Informação Complexo (SIC) onde cada uma das suas partes, em permanente interligação e interatuação, contribui decisivamente para a prossecução da missão do todo. A análise diacrónica permite observar as mudanças jurídicas como momentos de sucessão de subsistemas, que se individualizam dentro de um sistema e com o qual mantêm fortes relações de dependência, nomeadamente, e no caso em estudo: o Herbário COI, o JBUC, a Biblioteca de botânica, o Museu de botânica, os Laboratórios, a Secretaria, a Contabilidade e os Recursos Humanos.
... In relation to personal knowledge and understanding, the effects on product evaluation are also studied in terms of familiarity [35], expectations [36,37], and, more broadly, values [38]. Here, it is to be highlighted that the concept of knowledge differs from mere information acquisition, e.g., [39]. In most of the studies illustrated above with regard to forms of sustainabilityrelated communication, it is hard to infer if the provided information has been properly processed by individuals. ...
Article
Full-text available
Sustainability-related information affects people's choices and evaluation. The literature has made significant efforts to understand the best ways of delivering this kind of information to shape consumer behavior. However, while most studies have focused on packaged products and direct information provided through eco-labels, preferences could be formed differently in other design domains. The paper investigates the effect of the perceived amount of indirect information on the evaluation of an architectural artefact. A sample of 172 participants visited a locally produced mobile tiny house, made with a considerable amount of sustainable materials. The same participants answered a questionnaire about their perceived knowledge, quality, appropriateness and sustainability of the tiny house. The general level of knowledge of the tiny house was used as a proxy of the amount of indirect information received. Although the knowledge of the tiny house was generally low, ratings regarding the other dimensions were overall extremely positive. In particular, no evident relation was found between knowledge of the tiny house and sustainability, while the latter is significantly linked to quality aspects. These outcomes deviate from the evidence from other studies; this might be due to indirect vs. direct information and the peculiarity of the study carried out in the field of buildings. The gathered demographic and background data of the participants make it possible to highlight the role played by gender and age in affecting the evaluations, but the absence of a significant impact of experience in the field, education and origin. The results are compared with findings related to the evaluation of sustainable products and green buildings in particular.
... Acquiring knowledge can be thought of as the individual internal process where the person acquires information and stores it as knowledge through interpretation and perception. Another way to think of the DIKW model is that it describes the process by which what is objective transforms into what is subjective, leading to wise decision-making (Zins, 2007). In that sense, data and information operate on an objective level where information, according to (Floridi, 2005), can be categorized into 4 categories. ...
Preprint
Full-text available
In this survey, we examine Knowledge Graph mining algorithms, methods, and techniques and analyze them based on their capability to process heterogeneous knowledge graphs. First, we start with traditional graph analytics, including similarity and proximity metrics. Then we discuss the latest in deep and representation learning algorithms and methods. Following, we discuss the applications of Knowledge Graph Mining in the biomedical field. Finally, we reflect on several issues and future directions in Knowledge Graph Mining Research.
... Based on initial information and knowledge management frameworks such as the wisdom hierarchy (Rowley, 2007), researchers like Dammann (2018) have illustrated the process of generating knowledge from data as a four-layer hierarchical model (Figure 1). Experts have defined data, information and knowledge through different viewpoints (Zins, 2007) and it is therefore challenging to provide a single valid definition for data. However, it is assumed that data on its own has no meaning which has led to defining information as data with meaning (Hey, 2004). ...
Article
Full-text available
Practical case studies elaborating end-to-end attempts to improve the quality of information flows associated with athlete management processes are scarce in the current sport literature. Therefore, guided by a Business Process Management (BPM) approach, the current study presents the outcomes from a case study to optimize the quality of strength and conditioning (S&C) information flow in the performance department of a professional rugby union club. Initially, the S&C information flow was redesigned using integral technology, activity elimination and activity automation redesign heuristics. Utilizing the Lean Startup framework, the redesigned information flow was digitally transformed by designing data collection, management and visualization systems. Statistical tests used to assess the usability of the data collection systems against industry benchmarks using the System Usability Scale (SUS) administered to 55 players highlighted that its usability (mean SUS score of 87.6 ± 10.76) was well above average industry benchmarks of similar systems (Grade A from SUS scale). In the data visualization system, 14 minor usability problems were identified from 9 cognitive walkthroughs conducted with the High-Performance Unit (HPU) staff. Pre-post optimization information quality was subjectively assessed by administering a standardized questionnaire to the HPU members. The results indicated positive improvements in all of the information quality dimensions (with major improvements to the accessibility) relating to the S&C information flow. Additionally, the methods utilized in the study would be especially beneficial for sporting environments requiring cost effective and easily adoptable information flow digitization initiatives which need to be implemented by its internal staff members.
Article
Using the theory of social exchange, we investigated the mediating role of a good match between commitment and personal character (independent variables) and achievement of mentorship program objectives (dependent variables). Even though mentorship programs are designed to fulfill their designated objectives, the extent to which they are achieved is often not fully known. The focus of the study is a post-secondary professional mentorship program offered Haskayne School of Business, University of Calgary, which matches undergraduate and graduate students with business professionals. Data were collected primarily through questionnaires. We found that commitment (both the mentees' commitment and the mentees' perception of their mentors' commitment) and the mentors' character are important variables to actuate the exchange mechanism for learning to occur. These input variables are significant in predicting a good match and ultimately in determining whether the mentee's expectations are met, which had not been tested through an empirical analysis in prior literature. Also, our findings suggest that the importance of the mentor's personal character as a role model must be considered in the matching process. Care must be taken to customize the match to the needs of the specific mentor and mentee. Based on the findings, several suggestions are made for improvement of mentorship programs.
Article
Full-text available
This philosophical essay explores the epistemological foundations of knowledge organization and discusses implications for classification research. The study defines the concept of "knowledge," distinguishes between subjective knowledge (i.e., knowledge as a thought in the individual's mind) and objective knowledge (i.e., knowledge as an independent object), establishes the necessity of knowledge organization in the construction of knowledge and its key role in the creation, learning, and dissemination of knowledge, and concludes with implications for the development of classification schemes and knowledge maps.
Article
Full-text available
The field of information science is constantly changing. Therefore, information scientists are required to regularly review—and if necessary—redefine its fundamental building blocks. This article is one of four articles that documents the results of the Critical Delphi study conducted in 2003–2005. The study, “Knowledge Map of Information Science,” was aimed at exploring the foundations of information science. The international panel was composed of 57 leading scholars from 16 countries who represent nearly all the major subfields and important aspects of the field. In this study, the author documents 50 definitions of information science, maps the major theoretical issues relevant to the formulation of a systematic conception, formulates six different conceptions of the field, and discusses their implications. © 2007 Wiley Periodicals, Inc.
Article
The goals of a global liberal arts education, as conjoining both western and eastern sources, focus on 'virtue first', i.e. on pursuing human excellence (aretē). To determine whether such excellence can be taught online, I turn to contemporary research on Computer-Mediated Communication (CMC) and online education. Among other factors, important cultural issues as well as the real costs of online education have moderated 1990s enthusiasm for online learning as 'revolutionary'. I then take up Hubert Dreyfus' pedagogical taxonomy as it emphasizes the role of embodiment in learning. Expanding on his analysis, I argue that the most important goals of a global liberal arts education - precisely the goals of becoming excellent human beings capable of Aristotelian phronēsis, a key form of judgment crucial to not only professional success but also ethical and political life - require human teachers who incarnate the skills and judgment students need to acquire. These analyses, finally, support what is in fact a recent turn in online education towards blended classrooms that seek to exploit the distinctive advantages of both embodied and disembodied teaching.