ChapterPDF Available

Abstract and Figures

The chapter shows how minimal assumptions on difficult philosophical questions suffice for an engineering approach to the semantics of geospatial information. The key idea is to adopt a conceptual view of information system ontologies with a minimal but firm grounding in reality. The resulting constraint view of ontologies suggests mechanisms for grounding, for dealing with uncertainty, and for integrating folksonomies. Some implications and research needs beyond engineering practice are discussed.
Content may be subject to copyright.
Semantic Engineering
Werner Kuhn
Institute for Geoinformatics (ifgi), University of Muenster
Robert-Koch-Str. 26-28, D-48149 Muenster (Germany)
The chapter shows how minimal assumptions on difficult philosophical
questions suffice for an engineering approach to the semantics of geospa-
tial information. The key idea is to adopt a conceptual view of information
system ontologies with a minimal but firm grounding in reality. The result-
ing constraint view of ontologies suggests mechanisms for grounding, for
dealing with uncertainty, and for integrating folksonomies. Some implica-
tions and research needs beyond engineering practice are discussed.
1 Introduction
Many computer scientists, geographers, geoscientists, cognitive scientists,
philosophers, and knowledge engineers are concerned today with solving
semantic problems posed by data about the environment. Their work has
diverging goals and heated debates often ensue on foundations. For exam-
ple, in Beyond Concepts: Ontology as Reality Representation, Barry Smith
(2004) exposes some confused uses of the term concept in ontology. He
proposes to replace concepts as the subject matter of ontologies by “the
universals and particulars which exist in reality” and goes on to show that
this choice yields a more precise understanding of foundational ontological
relations, such as is-a or part-of. While he demonstrates the value of dis-
64 Werner Kuhn
tinguishing universals and particulars, his arguments do not support aban-
doning the notion of concept, as elusive or abused as it may be. Debates on
universal and particular are older and not easier to settle than those on
concept. For example, the question what it means for particulars (such as
Lake Constance) or universals (such as lake) to “exist in reality” remains
unsettled. Thus, Smith’s critique of concepts is mainly a (justified) expo-
sure of some sloppy language use and modeling.
In defense of concepts, and in an information system context, this chap-
ter advocates a pragmatic stance and an engineering view of semantics. A
vast body of literature on ontology engineering for conceptual modeling
(see, e.g., Guarino and Welti 2002; Guizzardi and Halpin 2008) shows
how productive it can be to avoid throwing out the baby of concepts with
the bathwater of its abuses. I will argue that this is so because
1. information system ontologies are only meant to constrain the use
and interpretation of terms; they do not specify “the meaning” of the-
se terms, much less “the existence” of universals and particulars in
2. ontological constraint networks are groundable in physical properties
of the environment; for semantics, no other assumptions are needed
about reality.
These two assumptions support a linguistic and an engineering reading of
concepts, make these two views compatible with each other, and anchor
ontologies in reality. They commit to a mind-independent reality, but one
in which no objects, universals or particulars need to be posited, only
stimuli, which humans can detect and build concepts from.
The first assumption recalls Guarino’s characterization of an ontology
as “a set of logical axioms designed to account for the intended meaning of
a vocabulary” (Guarino 1998). However, following the saying that “words
don’t mean, people do” and Putnam’s arguments that meaning is not an
object (Putnam 1975), I consider meaning to be a process. Furthermore, I
treat this process as an engineering artifact. Similar to the processes run-
ning in a chemical plant, meaning processes can then be constrained in
how they run: what people mean when they use a term, and how others in-
terpret the term, can be described and influenced. Dictionaries or feature
attribute catalogues, for example, constrain the uses and interpretations of
words or geodata, respectively.
The second assumption ties ontological constraints to reality. Instead of
a simplistic correspondence between terms and objects in reality, which is
clearly untenable, it suggests a minimal and sufficient grounding of onto-
logical constraint networks in elementary physical properties of the world.
A related paper (Schneider et al. 2009) demonstrates and formalizes this
Semantic Engineering 65
grounding process, drawing heavily on Gibson’s meaningful environment
(Gibson 1986). Here, we will just posit the grounding capability as such
and relate it by analogy to the grounding of geodetic networks.
Based on these two core assumptions, the chapter lays out an engineer-
ing view of semantics. The view has its roots in ontology engineering, but
has a purely semantic purpose. It puts concepts (which are considered to be
always associated with terms) at the center of attention and acknowledges
that their descriptions are necessarily incomplete. Its goal is to enable in-
formation users and providers to constrain the uses and interpretations of
their terms. A semantic engineer designs processes of language use and in-
The chapter first shows that concepts can be treated as symbolic and so-
cial entities subject to constraints (section 2). Then, it explains the result-
ing view of ontologies as constraint networks (section 3), an understanding
of grounding resulting from it (section 4), an integration strategy for folk-
sonomies (section 5), and a mechanism for dealing with uncertainty (sec-
tion 6). It concludes with a discussion of research challenges (section 7).
2 An Engineering View of Concepts
As Smith (2004) states, the lack of convincing definitions of concept and
related terms like conceptualization is partly due to “
the fact that these
terms deal with matters so fundamental to our cognitive architecture
(comparable in this respect to terms like ‘identity’ or ‘object’) that at-
tempts to define them are characteristically marked by the feature of
” Replacing concept by universal and particular, however,
does not solve this problem, as the age-old debates on realism, nominal-
ism, and conceptualism show. For solving semantic problems, it may be
more productive to agree on minimal requirements imposed on the notion
of concept. This is what I attempt to do here, limiting foundational claims
to relatively uncontroversial ones, and not attempting a formal definition
of concept. This section states these claims and proposes an interpretation
of the popular semantic triangle capturing them.
2.1 A triadic notion of concepts
For the purposes of semantic engineering, it is necessary and sufficient to
posit a threefold nature of concepts, involving
terms (symbols, words, expressions),
66 Werner Kuhn
which evoke and express ideas (thoughts) and
are used to refer to reality.
For example, in speakers of English, the word lake evokes an idea of a wa-
ter body connected to other water bodies and having properties like a rela-
tively flat surface and a water depth. The word may be used to refer, for
instance, to that large amount of blue, wet substance near Constance, as an
instance of the kind (
lake) or as a named individual (Lake Constance). For
German speakers, the words
See and Bodensee play the same roles, re-
Concepts are considered here to be associated with terms in a language,
not detached from them, so that the English word
lake and the German
See belong to two different concepts, regardless of whether they are
used to refer to the same parts of reality or not (see also Mark 1993). The
German term Begriff may make this close association between words and
thoughts more explicit than the more abstract English term concept; its
root begreifen (touch) furthermore points to the embodied nature of con-
2.2 The Semantic Triangle Revisited
The triadic notion of concepts goes back as far as Aristotle (Sowa 2000)
and is often represented by a semantic (or semiotic, or meaning) triangle
(Ogden and Richards 1923), with one corner for each of the three aspects
(Fig. 1).
Fig. 1. A form of the meaning triangle, adapted from (Ogden and Richards 1923)
Semantic Engineering 67
Note that Ogden and Richards (1923) avoid the term “concept” in their tri-
angle altogether and point to many imprecise uses of the term. For the pur-
pose of semantic engineering, it is sufficient to posit that the corners of the
meaning triangle represent the three aspects of concepts identified above.
Many discussions of the semantic triangle make assumptions about each
corner that are difficult to justify. For example, they label the top corner
“Concept”, claim that the REFERENT corner represents “Objects”, and
define SYMBOLs such as to carry some fixed meaning in and of them-
selves. These and other assumptions about meaning are unnecessarily
strong and probably wrong. The following discussion relaxes some as-
sumptions, makes the remaining ones more precise, and emphasizes the
social embedding of the triangle, which is normally neglected in its discus-
First, the top corner of the triangle shall, for our purposes, remain a
black box to which semantic engineers have no access, except by assuming
that the use of symbols expresses and evokes some thoughts in some peo-
ple, and that these thoughts are shaped by observations of reality.
Second, the right corner of the triangle is understood here as anything
external to minds that is shared in an information (or language) commu-
nity. It provides physical stimuli, which people can observe and agree on.
Reality, in this context, is what we observe and what we talk about using
symbols. Objects, particulars, and universals do not need to be assumed to
exist in a mind-independent reality; they can be cognitively or socially
constructed. This position does not exclude stronger claims about reality,
but avoids the two-fold circularity of defining universals “as that in reality
to which the general terms used in making scientific assertions corre-
spond” and particulars as “the instances of such universals” in (Smith
Third, the left corner contains the symbols of the language whose se-
mantics is in question, including symbols denoting relationships (such as
flowing into). For artificial languages, like those of information systems,
one can design these symbols as well as the conditions for their use. This
language design capacity reinforces the idea of semantic engineering.
The edges of the triangle represent relations between the corners, gener-
ated by human activities:
1. People observe reality and form thoughts; for example, the repeated
occurrence of extended horizontal water surfaces may suggest a cate-
gory of lakes.
2. People express thoughts through symbols; for example, one may no-
tice a water surface, while flying over it, and say “we are flying over
a lake”.
68 Werner Kuhn
3. Communication succeeds when others interpret how the symbols re-
fer to reality; for example, a seat neighbor on the plane might respond
“it must be Lake Constance”.
With the edges of the triangle representing many-to-many relations, per-
spectivalism and polysemy are fully admitted, as they should be. Reality
induces all kinds of thoughts, depending on perspectives taken, which are
in turn expressed in multiple ways as symbols, and the symbols are used to
refer to reality in many ways, even within a single language community.
The triadic notion of concepts avoids their reduction to purely linguistic
or purely mental entities. The symbolic and mental sides are necessary and
inseparable components of concepts and the reference to reality grounds
them. The concepts constructed by a language community are not arbi-
trary, and they are not just entities created by modelers. Rather, they are
what Smith calls “
tools (analogous to telescopes or microscopes) which
we can use in order to gain cognitive access to corresponding entities in
reality” – except that some of the corresponding entities are mentally
and socially constructed, while others are directly observable.
Finally, the proposed view of concepts also acknowledges their social
aspects. In information systems, as well as in communication in general,
terms can only be used to refer to something if there is a language commu-
nity establishing and sustaining this use. The semantic triangle does not
make this social aspect explicit, which is one of its weaknesses. Implicitly,
however, all its corners and edges require concepts to be situated in a
community sharing a language (or parts of it).
3 Ontologies as Networks of Constraints
Ontologies, in our semantic engineering view, constrain the use and inter-
pretation of terms in an information community. For example, a hydrology
ontology constrains how terms like
lake or waterbody should be used and
interpreted. The non-logical symbols of an ontology stand for concepts and
relations and its logical sentences constrain these. For instance, the con-
stants lake and waterbody in the sentence
lake is-a waterbody
stand for the concepts
lake and waterbody, respectively. By committing to
the ontology, a hydrological information community constrains these two
concepts through the is-a relation. The consequence is that anything
stated about water bodies applies also to lakes. For example, the ontology
could state that
Semantic Engineering 69
waterbody has-a waterdepth
and thereby also constrain all lakes to have a water depth quality. By add-
ing more and more sentences, such as
river is-a waterbody
a network of constraints is incrementally being built up, narrowing the
possible interpretations and uses of terms.
Some symbols may be introduced in the ontology for completeness or
convenience, without necessarily expressing a domain notion. For exam-
ple, in a sentence
every river flows_into a waterbody
the relation flows_into may express a notion of flowing into used in the
hydrology community, but it may also be an auxiliary concept used in the
ontology only.
While the notion of concept is typically reserved for universals in the
literature, ontologies can also constrain terms for particulars, in sentences
LakeConstance instance-of lake
Rhine flows_into LakeConstance.
This generalization allows for reasoning on individuals in the ontology, not
just in a database or GIS, where this kind of reasoning is typically (and of-
ten more efficiently) performed. Gazetteers are a good example for the
need of a combined reasoning on universals and particulars (Janowicz and
Keßler 2008). Also, ontological specifications of geographic kinds, like
lake or mountain, may refer to the (individual) surface of the Earth, of
which all their instances are parts.
The semiotic function of ontologies themselves (representing concepts
in logical languages) does not require the second meaning triangle that is
sometimes proposed (Sowa 2000). The symbols of the ontology can be
taken to be the symbols of the object language (for example, of hydrology
terms), or syntactic variants of them, expressing the same thoughts and re-
ferring to the same reality.
4 Grounding Constraint Networks
Treating ontologies as networks of constraints can give us an understand-
ing of what it means to ground them. The nodes and edges of a network of
70 Werner Kuhn
concept specifications can be further constrained by observations. For ex-
ample, the node lake in the ontology can be tied to polygons representing
lakes in a GIS database, as proposed in (Bennett et al. 2008), or the node
waterdepth can be tied to an observation procedure, as in (Schneider et al.
2009). Such observational information is then propagated through the net-
work and further restricts possible interpretations of all connected terms. If
it is supplied in symbols that are grounded in physical reality, this ground-
ing propagates through the network.
Grounding is a process of adding information on the variables of a con-
ceptual network through observations anchored in physical stimuli. This
idea concurs with Quine’s notion of observation sentences (Quine 1960).
Anchoring typically occurs in measurement units, other reproducible con-
ventions about measurement (such as agreements on zero values), and fun-
damental observable properties of the environment (like the fact that two
different media are separated by a surface). While a complete theory of on-
tology grounding remains to be worked out, I will explain the main idea
here using a geodetic analogy. A worked out example and the relation to
environmental psychology are presented in (Schneider et al. 2009).
Geodesists are familiar with the idea of grounding a constraint network:
using triangulation networks, they compute coordinates from observations
of distances and directions. The distance and directions are expressed as
constraints, which are parameterized in the coordinates and thereby map
the coordinate space to an observation space (Vaníček et al. 1982). The
networks are grounded through measurement units and externally supplied
coordinate values, which are both anchored in the physics of the earth.
The grounding of triangulation networks is called a geodetic datum. It
ties the social constructions of coordinate systems (in particular, their
equator and zero meridian) to the body of the earth. Broadly speaking, the
earth’s shape determines the ellipsoid on which the coordinates are de-
fined, the mass center anchors it in space, and the rotation axis orients it.
The essence of this grounding scheme is to achieve reproducible interpre-
tations of coordinates: one can take any coordinates and reconstruct the
corresponding real-world location, at least in principle.
A geodetic datum determines the interpretations of the coordinate con-
cepts used to describe location, i.e. of coordinates as such, not just of par-
ticular coordinate values. It explains the notions of latitude and longitude
operationally, by giving a recipe of how they are measured. Seen as net-
works constraining concepts, triangulation networks constrain the interpre-
tation of coordinates, distances and directions. Practically, these pose no
semantic problems, since methods exist to compute the necessary interpre-
tations. For coordinates, these methods are the coordinate reference sys-
tems (ISO 2002) commonly used in GIS and other geospatial information
Semantic Engineering 71
technology. For distance and direction measurements, they are the SI sys-
tem of measurement units (SI). The former, of course, are themselves an-
chored in the latter.
Conceptually, this interpretation procedure for coordinates supplies an
analogy for interpreting terms like
lake or waterdepth. Both kinds of inter-
pretation processes, geodetic and general, first map the symbols to obser-
vations and then ground this mapping in physical reality. Such an analogy
is at the heart of the notions of a semantic reference system and semantic
datum introduced in (Kuhn 2003; Kuhn and Raubal 2003). Here, we have
extended it to a constraint view of ontologies, to clarify the notion of on-
tology grounding.
It should be noted that grounding can never be absolute. A geodetic da-
tum rests on geophysical models (e.g., for the mass distribution of the
earth) and on astronomical frames of reference (star positions). Strictly
speaking, these assumptions harm the reproducibility of coordinate posi-
tions. More generally, a semantic datum can only shift the need for inter-
pretation to a reference frame at the next level. Practically, this shift should
(by design) solve most semantic problems. Philosophically, however, the
caveat may be useful to consider by ontologists making stronger assump-
tions about reality.
5 Integrating Folksonomies with Ontologies
A further gain of a constraint view of ontologies is that it connects ontolo-
gies to folksonomies. Folksonomies are non-hierarchical lists of keywords
(tags) linked to information resources. For example, a web site describing
a bicycle tour around Lake Constance
has been tagged with the terms cy-
, tour, austria, bregenz, lake, constance by a user of the social book-
marking site
Folksonomies are not related to taxonomies, despite their name, but
provide data about the terms people associate with contents. They do not
contain logical axioms, but tuples linking terms to resource identifiers (and
to the tagging users). These tuples constrain interpretations of the terms
used as tags, by showing their use and its evolution over time. They con-
strain the interpretations bottom-up, complementing the prescriptive top-
down constraints of ontologies. For example, reveals what
contents are tagged by terms like
lake and/or river.
72 Werner Kuhn
Folksonomies are easy to generate and use, do not burden users or pro-
ducers with difficult modeling tasks, and clearly have something to tell us
about the semantics of their terms. They are, in fact, an increasingly popu-
lar form of empirical semantic data. Other such forms come from data
mining and similar knowledge extraction methods. All these inductive ap-
proaches to semantics play a key role in the automated learning and main-
tenance of terminological constraint systems.
6 Accommodating Uncertainty
Constraint networks provide great flexibility in information handling, by
admitting any number of constraints on any of their variables. As a conse-
quence, they have to provide methods to accommodate uncertainty, since
the stated constraints may over- or underdetermine an exact solution. In
the case of triangulation networks, as well as for many other cases, one
considers all variables and observables to be stochastic, i.e., having a prob-
ability density distribution (Vaníček 1982). Relative weights on the con-
straints can then be derived from knowledge or assumptions about this dis-
tribution, i.e., about the precision of the constraints.
Zadeh’s Generalized Constraint Language (Zadeh 2008) provides the
formal framework to extend this idea to general cases of “computing with
words”. Perception-based statements, i.e., observations, can be precisiated
by any suitable means (for example, by probability distributions or fuzzy
set membership curves), and their impact on a solution for the network
variables can be computed.
This methodology of Zadeh bridges the traditional two-valued logic on-
tologies to the constraint-based view suggested here, where semantic in-
formation is by default considered to be uncertain. The main difference to
geodetic or other geometrically well-defined cases is that conceptual net-
works have no clear-cut degrees of freedom. Grounding can therefore not
easily be determined to be sufficient, but the geodetic ideas that
grounding spreads through the network;
arbitrary observational information can be added;
assumptions on the relative precision of this information serve to weigh
its impact;
remain valid in the “computing with words” scenario of semantic engi-
Semantic Engineering 73
7 Conclusions
Semantic engineering constrains interpretations of terminologies. It im-
proves mechanisms for information sharing by using semantic web and so-
cial web technology to formulate and evaluate constraints on interpreta-
tions. It makes only minimal assumptions about difficult philosophical
issues (reference, realism vs. nominalism, cognitive processes), in order to
allow for pragmatic solutions to semantic problems.
The proposed engineering view of semantics avoids the pitfalls of treat-
ing ideas decoupled from language (and thereby treading on thin ice re-
garding testability of its hypotheses) or treating terminology decoupled
from its use (and thereby limiting semantics to linguistic relations). In-
stead, it rests on a notion of concepts that necessarily involves expressions
in a language and ties them to reality. It specifies concepts in constraint
networks and grounds ontological constraints in observations of reality.
Thereby, it admits conceptual theories, but avoids engaging in psychologi-
cal speculation about what ideas people may have about the world. The
matter of study (and of engineering design) is how people apply terms to
refer to something in the world that is either commonly observable in an
information community or traceable to something that is.
What these observable aspects exactly are is a question discussed else-
where (Schneider et al. 2009). It constitutes one of the core research ques-
tions raised by semantic engineering. Frank has proposed ontological tiers
to capture references to reality at multiple levels of abstraction (Frank
2001). Here, I refrain from assuming anything about such levels (e.g. about
their ordering or about the role of objects) and only posit that observation
sentences (in the sense of Quine) exist, so that primitive symbols can be
interpreted through ostension. While there may be philosophical quibbles
against this position as a general requirement, it appears to rest on solid
ground in the context of geospatial information, which is per definition
rooted in observations of the environment.
Ontology research has made limited use of the idea that ontologies are
networks of constraints on concepts. Yet, concept networks have been a
central idea in dealing with semantics for a long time, both in linguistics
(Langacker 1987) and in computing (Woods 1985). Networks of con-
straints are a standard device in many areas of engineering and computing.
The chapter has shown that ontologies seen as constraint networks supply
mechanisms for grounding, for accommodating uncertainty, and for inte-
grating folksonomies.
Mechanisms for the formal treatment of ontological constraint networks,
including their grounding and uncertainty, remain to be refined and im-
74 Werner Kuhn
plemented. It appears that model theory is a sufficient formal basis, if ob-
servable aspects of reality are admitted as models, as proposed, for exam-
ple, in (Hayes 1985). These models are then algebraic, consisting of ob-
servable qualities and their changes, and transcend the naïve set-based
model theory of formal semantics.
Picking out one symbol and considering only one sense of it turns the
relations at the edges of the meaning triangle into functions. This allows
for a categorical formalization of the triangle, where the refer function is
treated as a composition of observe and express functions. Thereby, se-
mantic theories may get connected to theories of change and action (Kuhn
2005), explaining semantics through observable effects of processes. For
example, this would make it possible to explain why the seat neighbor in
the flight over Lake Constance might leave his seat after the above dia-
logue to stretch his legs before an expected landing in Zurich.
The combination of linguistic, mental, empirical, and social aspects of
concepts advocated here allows for constraining how information produc-
ers and consumers interpret terms. It permits agreements on such interpre-
tations in the form of ontologies and it can deal with their evolution over
time. This pragmatic position has nothing to do with “cultural relativism”.
It rests on the basic scientific paradigm of knowledge derived from obser-
vations. It is compatible with, but does not require, a stronger form of real-
ist semantics, but avoids some pitfalls of both, realist and cognitive seman-
tics. For example, it has no need to invoke truth independently of meaning,
or to decide which entities have correspondents in reality and which not,
nor does it have to assume unverifiable cognitive mechanisms. The only
cognitive claim is that humans interpret the terms they use, and that this in-
terpretation is ultimately based on ostension. An appropriate philosophical
basis for such a view is radical constructivism (Glasersfeld 2002), which
treats human conceptualizations and knowledge as constructions, con-
strained by observations and interactions with other individuals and with
the environment.
Smith’s program of ontology as science (Smith 2008) is compatible
with, but cannot replace an engineering approach; at least not in the con-
text of geospatial information, which exhibits multiple and often conflict-
ing conceptualizations of reality. A single reference ontology (which
Smith pursues for biomedical information) is unlikely to emerge any time
soon for geospatial domains.
Meanwhile, putting application terminology on a solid basis in the form
of foundational ontologies (such as DOLCE, BFO, CIDOC-CRM, SUMO)
helps making sensible general distinctions. For example, universals and
particulars, endurants and perdurants, or different types of qualities are
usefully distinguished, though the distinction ultimately rests on human
Semantic Engineering 75
conceptualizations. DOLCE’s distinctions suggest a notion of primitive
qualities, which support grounding in observations. This kind of anchoring
of ontologies is likely to support ontology mappings at least as effectively
as the abstract scientific notions from a reference ontology, which are al-
most guaranteed to be interpreted differently in multiple applications.
Support for this work was provided in part by the European Commission
(IST project SWING, No. FP6-26514). Many discussions in MUSIL
( and with other colleagues (Andrew Frank,
David Mark, Boyan Brodaric and others) as well as comments from two
anonymous reviewers have shaped the ideas expressed and helped me ar-
ticulate them. I am grateful to Lotfi Zadeh for asking how my approach
handles uncertainty, to Andrew Frank for asking where objects come from,
and to Mike Worboys for asking why we need to talk about anything be-
yond terms. The chapter gives some preliminary answers.
Bennett B, Mallenby D, Third A (2008) An Ontology for Grounding Vague
Geographic Terms. In: Eschenbach C, Grüninger M (eds) Formal Ontology in
Information Systems (FOIS 2008), IOS Press, pp 280–293
Frank AU (2001) Tiers of ontology and consistency constraints in geographical
information systems. International Journal of Geographical Information
Science (IJGIS) 15(7): 667–678
Gibson JJ (1986) The Ecological Approach to Visual Perception, LEA Publishers,
Hillsdale, NY
Glasersfeld Ev (2002) Radikaler Konstruktivismus: Ideen, Ergebnisse, Probleme
Guarino N (1998) Formal Ontology and Information Systems. In: Guarino N (ed)
Formal Ontology in Information Systems (FOIS’98). IOS Press, Amsterdam,
Trento, Italy, pp 3–15
Guarino N, Welty C (2002) Evaluating Ontological Decisions with
ONTOCLEAN. Communications of the ACM 45: 61–65
Guizzardi G, Halpin T (eds) (2008) Special Issue: Ontological Foundations for
Conceptual Modeling. Applied Ontology 3(1-2)
Hayes PJ (1985) The Second Naive Physics Manifesto. In: Moore Ha (ed) Formal
Theories of the Common-sense World. Ablex, Norwood, NJ, pp 1–36
ISO (2002) ISO 19111 - Spatial referencing by geographic coordinates, ISO TC
76 Werner Kuhn
Janowicz K, Keßler C (2008) The Role of Ontology in Improving Gazetteer
Interaction. International Journal of Geographical Information Science
(IJGIS) 22(10): 1129–1157
Kuhn W (2003) Semantic Reference Systems. International Journal of Geographic
Information Science (IJGIS, Guest Editorial) 17: 405–409
Kuhn W (2005) Geospatial Semantics: Why, of What, and How? Journal on Data
Semantics: 1–24
Kuhn W, Raubal M (2003) Implementing Semantic Reference Systems. In: Gould
MF, Laurini, R, Coulondre S (eds) 6
AGILE Conference on Geographic
Information Science, Presses Polytechniques et Universitaires Romandes,
April 24-26, 2003, Lyon, France, pp 63–72
Langacker RW (1987) Foundations of Cognitive Grammar, vol. 1: Theoretical
Prerequisites, Stanford University Press, Stanford
Mark DM (1993) Toward a Theoretical Framework for Geographic Entity Types.
In: Frank AU, Campari I (eds) Spatial Information Theory: Theoretical Basis
for GIS, Lecture Notes in Computer Science 716, Springer Heidelberg Berlin
New York, pp 270–283
Ogden CK, Richards IA (1923) The Meaning Of Meaning, Harcourt Brace
Putnam H (1975) Mind, Language and Reality, Cambridge University Press,
Cambridge, MA
Quine WVO (1960) Word and Object, The MIT Press, Cambridge, MA
Scheider S, Janowicz K, Kuhn W (2009) Grounding Geographic Categories in the
Meaningful Environment, MUSIL working papers, Institute for Geoiformatics
(ifgi), University of Muenster, Muenster (Germany)
Smith B (2004) Beyond Concepts: Ontology as Reality Representation. In: Varzi
A, Vieu L (eds) Proceedings of FOIS
Smith B (2008) Ontology (Science). In: Eschenbach C, Grüninger, M (eds)
Formal Ontology in Information Systems (FOIS 2008), IOS Press
Sowa JF (2000) Knowledge Representation. Logical, Philosophical, and
Computational Foundations. Brooks Cole, Pacific Grove, CA
Vaníček P, Krakiwsky EJ (1982) Geodesy: The Concepts, North-Holland,
Woods WA (1985) What’s in a link: Foundations for Semantic Networks. In:
Levesque RJBaHJ (ed) Readings in Knowledge Representation. Morgan
Kaufman, pp 218–241
Zadeh LA (2008) Is there a need for fuzzy logic? Information Sciences 178:
... The engineering view of semantics proposed in (Kuhn, 2009b) is adopted in the current work for the development of the ontology of spatial and temporal resolution of sensor observations. This view, as described in (Kuhn, 2009b), makes minimal assumptions about philosophical issues (e.g. ...
... The engineering view of semantics proposed in (Kuhn, 2009b) is adopted in the current work for the development of the ontology of spatial and temporal resolution of sensor observations. This view, as described in (Kuhn, 2009b), makes minimal assumptions about philosophical issues (e.g. realism vs nominalism), to the end of pragmatic solutions to semantic problems. ...
... realism vs nominalism), to the end of pragmatic solutions to semantic problems. Regarding the philosophical basis for such a view, Kuhn (2009b) suggested radical constructivism. A constructivistic approach, as Couclelis (2010) mentions, is philosophically "closest to instrumentalist and pragmatist philosophies of science, which tend to be neutral on the question of external reality but focus instead on seeking the most productive solutions to specific problems". ...
Full-text available
OBSERVATION is a central notion to the field of Geographic Information Science. Monitoring phenomena (e.g. climate change, landslides, demographic movements) happening on the earth’s surface, and developing models and simulations for those phenomena rely on observations. Observations can be produced by technical sensors (e.g. a satellite) or humans. RESOLUTION is an important aspect of observations underlying geographic information. The consequence of using observations at various resolutions is (potentially) different decisions, because the resolutions of the observations influence the patterns that can be detected during an analysis process. Despite the importance of the notion of resolution, and early attempts at its formalization, there is currently no theory of resolution of observations underlying geographic information. The goal and main contribution of this work is the provision of such a theory. The scope of the work is limited to the characterization of the SPATIAL and TEMPORAL resolution of single observations, and collections of observations. The use of ONTOLOGY as formal specification technique helps to produce, not only useful theoretical insights about the resolution of observations, but also computational artifacts relevant to the SensorWeb. At a theoretical level, the work suggests a receptor-based theory of resolution for single observations, and a theory of resolution for observation collections, based on the observed study area and observed study period. The consistency of both theories is tested through the use of the functional language HASKELL. The practical contribution of the work comes from the two ONTOLOGY DESIGN PATTERNS suggested and encoded using the Web Ontology Language. The use of the design patterns in conjunction with the query language SPARQL helps to retrieve observations at different resolution. All in all, the work brings up ideas that are of interest to research on data quality in Geographic Information Science, and in the SensorWeb.
... The relationship between language, the things it describes, and human thought have been heavily studied, widely debated, and described by many authors in the form of a triangle (Almeida, Souza & Fonseca, 2011;Dahlberg, 1978;Kuhn, 2009;Nirenburg & Raskin, 2001;Peirce, 1965;Ranganathan, 1937;Saussure, 1967;Steinbring, 1998;Tichy, 1988). We adopt and extend Ogden and Richards (1923) version of the triangle, as shown in Figure 1, as a framework for our review of research into spatial language. ...
... The human conceptualisation of a real world object. Human conceptualisations of landscape (Mark & Turk, 2003;Schubert, 2006) (Beaudoin, 2007;Derungs & Purves, 2016;Dimitrova et al., 2008;Kuhn, 2005Kuhn, , 2009Mocnik et al., 2017;Tudhope et al., 2001) Geographic ontologies, thesauri and folksonomies ...
... Such ontologies and thesauri (a simplified form of ontologies with less formally specified semantics) can be used for various tasks including identification of landscape features in natural language; automated ontology mapping; cross-linguistic data integration and translation using multilingual thesauri (Kavouras, Kokla & Tomai, 2005;Stock & Cialone, 2011a). Kuhn (2009) identifies the need for the classes in formally-specified ontologies to be grounded in physical properties, and proposes the idea of semantic reference systems to avoid formally-specified ontologies being "Islands in a sea of different conceptualisations" (Kuhn, 2005, p. 15). ...
... Kokla et al. [14] offers a comprehensive review of the contributions that represent a progress in geospatial semantics since 2015; it focuses around two main topics, i.e., information modeling (ontologies and their development) and (latent) knowledge elicitation (from unstructured or semi-structured content, based in particular on textual contents). This paper reviews more than 150 works; among them are papers that present categorizations of methods and approaches to geosemantics, such as [15][16][17][18][19]. Other cited contributions report on the efforts for describing the methods at hand: [20][21][22][23][24][25][26][27][28][29][30][31][32]. ...
... Janowicz et al. [125] is a rich overview of the geosemantics landscape focusing on some selected topics that the authors deem of particular interest; the contributions reviewed are organized according to these. With respect to the question on what kinds of Geospatial Classes should be distinguished, they cite [16][17][18]63,65,66,68,[126][127][128][129]; instead, the question on how to reference Geospatial Phenomena is supported by [113,123,[130][131][132]. Discovering events and accounting for geographic change are faced and fostered in [133][134][135][136], Handling places and moving object trajectories is dealt with in [70,77,90,133,[137][138][139][140][141][142][143]. ...
Full-text available
Distinct, alternative forms of geosemantics, whose classification is often ill-defined, emerge in the management of geospatial information. This paper proposes a workflow to identify patterns in the different practices and methods dealing with geoinformation. From a meta-review of the state of the art in geosemantics, this paper first pinpoints “keywords” representing key concepts, challenges, methods, and technologies. Then, we illustrate several case studies, following the categorization into implicit, formal, and powerful (i.e., soft) semantics depending on the kind of their input. Finally, we associate the case studies with the previously identified keywords and compute their similarities in order to ascertain if distinguishing methodologies, techniques, and challenges can be related to the three distinct forms of semantics. The outcomes of the analysis sheds some light on the diverse methods and technologies that are more suited to model and deal with specific forms of geosemantics.
... To integrate the information in different sensor networks, the Semantic Sensor Network (SSN) [4] is proposed, which provides the unified and standard sensor information styles to enhance the semantic connectivity, share sensor data, and improve interoperability. The Semantic Web (SW) provides a knowledge representation of a conceptual reference model, known as an ontology, to restrict domain terms [5,6], and SSN [7] combines the traditional sensor network with SW's knowledge representation, reasoning, and organizational capabilities. As the key component of SSN, sensor ontology is regarded as the advanced knowledge model for exchanging sensor information. ...
Full-text available
Due to the problem of data heterogeneity in the semantic sensor networks, the communications among different sensor network applications are seriously hampered. Although sensor ontology is regarded as the state-of-the-art knowledge model for exchanging sensor information, there also exists the heterogeneity problem between different sensor ontologies. Ontology matching is an effective method to deal with the sensor ontology heterogeneity problem, whose kernel technique is the similarity measure. How to integrate different similarity measures to determine the alignment of high quality for the users with different preferences is a challenging problem. To face this challenge, in our work, a Multiobjective Evolutionary Algorithm (MOEA) is used in determining different nondominated solutions. In particular, the evaluating metric on sensor ontology alignment's quality is proposed, which takes into consideration user's preferences and do not need to use the Reference Alignment (RA) beforehand; an optimization model is constructed to define the sensor ontology matching problem formally, and a selection operator is presented, which can make MOEA uniformly improve the solution's objectives. In the experiment, the benchmark from the Ontology Alignment Evaluation Initiative (OAEI) and the real ontologies of the sensor domain is used to test the performance of our approach, and the experimental results show the validity of our approach.
... For an epistemology of visual culture to offer a viable support for graphic communications work it must account for the playful innovation of functional elements, by users (including designers who, let us not forget, are still users), elements that other users can still regard as meaningful and true, (i.e. signs that are not rendered meaningless by personal playfulness) (see Kuhn, 2009). This paper proposes that emergent effects arising from the very complexity of the large scale systems of interwoven feedback loops and agent choice that is Culture. ...
Conference Paper
Full-text available
... • consistency [14,36] (i.e., incapacity of ge ing contradictory conclusions from valid input data) • expressiveness [15,31] (i.e., number of competency questions that the ontology can answer) • grounding [14,26] (i.e., number of assumptions done by the ontology's underlying philosophical theory about reality) Implementation evaluation: criteria possibly useful for the evaluation of the implementation stage of an ontology development process include: ...
Conference Paper
Full-text available
Ontologies are key to information retrieval, semantic integration of datasets, and semantic similarity analyses. Evaluating ontologies (especially defining what constitutes a "good " or "better" ontology) is therefore of central importance for the Semantic Web community. Various criteria have been introduced in the literature to evaluate ontologies, and this article classifies them according to their relevance to the design or the implementation phase of ontology development. In addition, the article compiles strategies for ontology evaluation based on ontologies published until 2017 in two outlets: the Semantic Web Journal, and the Journal of Web Semantics. Gaps and opportunities for future research on ontology evaluation are exposed towards the end of the paper. CCS CONCEPTS • Computing methodologies →Ontology engineering; • Information systems →World Wide Web
Over the past years, many research projects and initiatives have provided heterogeneous building blocks for the so called Semantic Geospatial Web. The number of proposed architectures and developed components impede a definition of the state of the art, comparisons of existing solutions, and the identification of open research challenges. This chapter provides the missing generic specification of central building blocks. Focusing on service based solutions; VISION (VIsionary Semantic service Infrastructure with ONtologies) is introduced as a means to depict required components at a generalized level. The VISION architecture highlights the most important services for the Semantic Geospatial Web and brings structure to the numerous past and present partial solutions. Model-as-a-Service (MaaS) is introduced as a central concept for encapsulating environmental models. This has great potential to be a major part of future information infrastructures. The German-funded GDI-GRID project serves illustrating examples for MaaS and arising interoperability challenges. This paper will focus on VISION, and compare it with two other recent research projects and conclude by identifying major areas for future research on Semantic Geospatial Web Services and supporting infrastructures.
Full-text available
The present paper provides a review of two research topics that are central to geospatial semantics: information modeling and elicitation. The first topic deals with the development of ontologies at different levels of generality and formality, tailored to various needs and uses. The second topic involves a set of processes that aim to draw out latent knowledge from unstructured or semi-structured content: semantic-based extraction, enrichment, search, and analysis. These processes focus on eliciting a structured representation of information in various forms such as: semantic metadata, links to ontology concepts, a collection of topics, etc. The paper reviews the progress made over the last five years in these two very active areas of research. It discusses the problems and the challenges faced, highlights the types of semantic information formalized and extracted, as well as the methodologies and tools used, and identifies directions for future research.
As a tool dealing with information rather than matter, GIS shares with other information technologies the conceptual challenges of its medium. For a number of years now, ontology development has helped harness the complexity of the notion of information and has emerged as an effective means for improving the fitness for use of information products. More recently, the broadening range of users and user needs has led to increasing calls for “lightweight” ontologies very different in structure, expressivity, and scope from the traditional foundational or domain-oriented ones. This paper outlines a conceptual model suitable for generating micro-ontologies of geographic information tailored to specific user needs and purposes, while avoiding the traps of relativism that ad hoc efforts might engender. The model focuses on the notion of information decomposed into three interrelated “views”: that of measurements and formal operations on these, that of semantics that provide the meaning, and that of the context within which the information is interpreted and used. Together, these three aspects enable the construction of micro-ontologies, which correspond to user-motivated selections of measurements to fit particular, task-specific interpretations. The model supersedes the conceptual framework previously proposed by the author (Couclelis, Int J Geogr Inf Sci 24(12):1785–1809, 2010), which now becomes the semantic view. In its new role, the former framework allows informational threads to be traced through a nested sequence of layers of decreasing semantic richness, guided by user purpose. “Purpose” is here seen as both the interface between micro-ontologies and the social world that motivates user needs and perspectives, and as the primary principle in the selection and interpretation of Information most appropriate for the representational task at hand. Thus, the “I” in GIS also stands for the Individual whose need the tool serves.
A natural language interface can improve human-computer interaction with Geographic Information Systems (GIS). A prerequisite for this is the mapping of natural language expressions onto spatial queries. Previous mapping approaches, using, for example, fuzzy sets, failed because of the flexible and context-dependent use of spatial terms. Context changes the interpretation drastically. For example, the spatial relation “near” can be mapped onto distances ranging anywhere from kilometers to centimeters. We present a context-enriched semiotic triangle that allows us to distinguish between multiple interpretations. As formalization we introduce the notation of contextualized concepts that is tied to one context. One concept inherits multiple contextualized concepts such that multiple interpretations can be distinguished. The interpretation for one contextualized concept corresponds to the intention of the spatial term, and is used as input for a spatial query. To demonstrate our computational model, a next generation GIS is envisioned that maps the spatial relation “near” to spatial queries differently according to the influencing context.