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The ABC ontology and model

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Abstract

This paper describes the latest version of the ABC metadata model. This model has been developed within the Harmony international digital library project to provide a common conceptual model to facilitate interoperability between metadata ontologies from different domains. This updated ABC model is the result of collaboration with the CIMI consortium whereby earlier versions of the ABC model were applied to metadata descriptions of complex objects provided by CIMI museums and libraries. The result is a metadata model with more logically grounded time and entity semantics. Based on this model we have been able to build a metadata repository of RDF descriptions and a search interface which is capable of more sophisticated queries than less-expressive, object-centric metadata models will allow.
The ABC Ontology and Model
Carl Lagoze
Cornell University
Ithaca, NY
lagoze@cs.cornell.edu
Jane Hunter
DSTC Pty, Ltd.
Brisbane, Australia
jane@dstc.edu.au
Abstract
This paper describes the latest version of the ABC
metadata model. This model has been developed
within the Harmony international digital library
project to provide a common conceptual model to
facilitate interoperability between metadata
ontologies from different domains. This updated
ABC model is the result of collaboration with the
CIMI consortium whereby earlier versions of the
ABC model were applied to metadata descriptions of
complex objects provided by CIMI museums and
libraries. The result is a metadata model with more
logically grounded time and entity semantics. Based
on this model we have been able to build a metadata
repository of RDF descriptions and a search interface
which is capable of more sophisticated queries than
less-expressive, object-centric metadata models will
allow.
Keywords: Metadata, Modeling, Ontologies
1 Introduction
The Harmony Project [10] is an international
digital library project funded by DSTC (Australia),
JISC (U.K.), and the NSF (U.S.). The broad goal of
the project is to research methods and models for
describing the variety of rich content that
increasingly populates the Web and digital libraries.
The paper describes recent results in the
development of a metadata model and ontology. The
so-called ABC Model (a purposely innocuous name)
was first articulated in an early Harmony working
paper [21] and was later documented in a number of
conference papers [25, 26, 30]. The initial and
ongoing goal of work on the ABC model is three-
fold:
To provide a conceptual basis for
understanding and analyzing existing metadata
ontologies and instances.
To give guidance to communities beginning to
examine and develop descriptive ontologies.
To develop a conceptual basis for automated
mapping amongst metadata ontologies.
As such, the ABC ontology is not intended as a
metadata vocabulary per se, but as a basic model and
ontology that provides the notional basis for
developing domain, role, or community specific
ontologies. In this spirit, the ABC model
incorporates a number of basic entities and
relationships common across other metadata
ontologies including time and object modification,
agency, places, concepts, and tangible objects.
Communities wishing to build their own metadata
ontologies and models may then extend the ABC
entities and relationships as needed.
The initial version of the model, described in [21]
benefited from contacts and collaborations with a
number of communities and efforts including the
Dublin Core Metadata Initiative [7], the IFLA
Functional Requirements for Bibliographic Records
[9], the INDECS E-Commerce Metadata Model [12],
and the work among many of those communities to
articulate a common conceptual model [17]. The
updated version of the model, described in this
paper, benefits from collaborations with the museum
community represented by both the CIDOC/CRM
[3] and the CIMI [5] consortium. The modeling
methodology continues to build on concepts from the
Resource Description Framework (RDF) [31] of the
World Wide Web Consortium [14], but should also
be applicable to other modeling paradigms with roots
in first-order logic such as UML [19] or Conceptual
Graphs [36].
The remainder of this paper is structured as
follows:
Section 2 describes the scope and intended
purpose of the ABC model;
Section 3 summarizes the basic components of
the model, paying special attention to its
temporal components that permit modeling the
lifecycle aspects of entities;
Section 4 is a complete reference of the classes
and properties that constitute the ABC model;
Section 5 illustrates the application of the ABC
model on both fictional and actual (CIMI)
examples;
Section 6 describes the metadata repository and
search interface developed to enable
sophisticated queries across all of the CIMI
images from a single user interface;
Section 7 concludes with our anticipated future
work directions.
2 Purpose and Scope of the ABC
Model
2.1. Origins
Work on the ABC data model and ontology is
motivated by the recognition that many existing
metadata efforts often proceed with insufficient
attention to underlying modeling principles. Failure
to pay due attention to more formal principles has
frequently led to attempts to express complex
resource descriptions without a clear exposition of
the entities and relationships necessary for such
descriptions. Such informality may be appropriate
for simple "pidgin" metadata [16], such as Dublin
Core, but lacks precision for detailed descriptions
[29].
We argue that one essential test of a descriptive
model should be the specificity of queries that it
supports. If the intent is to support simple Boolean
queries on fields such as “return all documents
authored by Tom Baker and with ‘Grammar’ in the
title” then it is reasonable to build descriptions as a
record of attributes and their appropriate literal
values [16]. We have found that, especially in our
work with the museum community, creators of
metadata frequently want more advanced query
semantics, which include attributes of multiple
entities and ask questions about “who was
responsible for what, when and where”. In order to
support such queries, a metadata model must provide
a logical foundation for temporal semantics and
consistent attachment points for the agents, actions,
and situations involved in these temporal semantics.
As a parenthetical remark, we emphasize that
support for more advanced queries almost certainly
increases the human effort and the expense of
creating resource descriptions (i.e., the Arms cost-
functionality curve [15]). Therefore, communities
considering building on more complex models, such
as ABC or the CIDOC/CRM [27], should carefully
consider the costs and benefits. In many cases, it
may be more expedient to supply high-volume
pidgin metadata (e.g., simple Dublin Core) rather
than constructing highly expressive but expensive
descriptions for fewer objects. These are decisions
that should be based on careful analysis of desired
functionality and economic realities.
The modeling principles upon which ABC builds
are described in the original strawman document
[21]. The notion of temporality deserves emphasis
here due to its importance in the model and its
implications for its scope. A core intent in ABC is
the ability to model the creation, evolution, and
transition of objects over time. Traditional
bibliographic cataloging has generally assumed that
the objects being described, and therefore their
attributes, are more or less stable. Time and object
transition has generally been relegated to "second
class" status. This has made traditional resource-
based cataloging inadequate in a number of contexts
[28], for example:
museums, where describing the temporal
transitions of an object (e.g., its discovery,
classification, exhibit history) is considered
essential.
archives, where provenance of an object is
fundamental to establishing its integrity.
digital resources, which unlike physical content
are fundamentally malleable and derivable.
rights management, where questions about "who
did what, when, where, and of what nature?" are
essential to assigning proper attribution.
2.2. Targeted Objects
The ability to model change makes ABC
appropriate for describing a wide variety of entities
and the relationships between them. In particular, it
has been designed to model physical, digital and
analogue objects held in libraries, archives, and
museums and on the Internet. This includes objects
of all media types text, image, video, audio, web
pages, and multimedia. It can also be used to model
abstract concepts such as intellectual content and
temporal entities such as performances or lifecycle
events that happen to an object. In addition the
model can be used to describe other fundamental
entities that occur across many domains such as:
agents (people, organizations, instruments), places
and times.
2.3. Intended Use and Users
The ABC model has not been constrained by the
design principle that it be comprehensible by the
standard "user", or creator of metadata. Rather, it is
intended as a conceptual foundation with two
communities of use in mind:
Individual metadata communities might use the
principles demonstrated in ABC as the basis for
building domain or purpose specific metadata
ontologies and models. While these ontologies
might not include all aspects of ABC, our experience
has shown that awareness of the principles in ABC,
especially the clean separation of entities and the
conceptualization of object transition, can prove
valuable in avoiding common pitfalls of metadata
design. ABC has been deliberately designed as a
primitive ontology so that individual communities
are able to build on top of it. A set of base classes
has been provided to act as either attachment points
for domain-specific properties or super classes which
can be sub-classed to create domain-specific classes.
System builders might use the ABC principles as
the basis for implementing tools that permit mapping
across descriptions in multiple metadata formats.
Our experience has shown that the possibility of
mapping automatically is often mitigated by the
undisciplined use of existing metadata formats, or by
the non-regular semantics of many metadata
ontologies. However, it is arguably true that a
foundation model such as ABC may provide a
knowledge framework to assist in metadata mapping.
Our own experiments with ABC [30] have
demonstrated this mapping.
3 Narrative Overview of the ABC
Model
This section introduces the elements of the ABC
model. It is intended as a complement and entry
point to Section 4, which includes a detailed
specification of all ABC classes and Properties,
Appendix A, which presents the ABC model as an
RDF schema, and Appendix B, which illustrate the
ABC class hierarchy in graphical form.
The primitive category at the core of the ABC
ontology is an entity. Three categories lie at the next
level of the ontology: temporality, actuality, and
abstraction.
3.1. Temporality Category
A distinguished aspect of the ABC model is the
manner in which it explicitly models time and the
way in which properties of objects are transformed
over time. Other descriptive models such as AACR2
[23] and Dublin Core [6] imply some time semantics.
For example, the DC date element and its qualifiers
[8] created and modified express events in the
lifecycle of a resource. Note, that expressing these
events in this second-class manner (i.e., not making
the temporal entities ontological entities) makes it
difficult to associate agent responsibility with those
events and connect them with changes in state of the
resource. In contrast, the ABC model makes it
possible to unambiguously express situations in
which object properties exist, the transitions that
demark those situations, and the actions and agency
that participate in those transitions.
The theoretical foundations for the ABC temporal
notions can be found in process models such as Petri
Nets [35] or extensions to first-order logic such as
Situational Calculus [32]. In brief summary, ABC
models time as follows:
A situation provides the context for framing
time-dependent properties of (possibly multiple)
entities. Entities, such as a person or a
document, may have properties that exist only in
the context of a situation and other properties
that are constant across the context of a
description. For example, in a description of the
“my car”, the property “has make Honda” is
constant across the entire description, but the
property “has color red” applies before I paint it,
and the property “has color green” applies after I
paint it. Concurrently, the green paint can has
the property “is full” before I paint the car and
the property “is empty” after I paint it, but
always has the property “produced by Dulux”.
ABC models this through the use of situations to
which are bound existential facets of entities,
which provide the attachment points for
situation-specific properties of entities (the color
of the car and the fullness of the paint can).
These existential facets can co-exist with a
single universal facet of each entity, to which
the time-independent properties are bound (e.g.,
the model of the car or the producer of the paint
can). From the perspective of first-order logic,
the existential facet corresponds to “there exists
a situation in which an instance of the entity
exists with a property set”, and the universal
facet corresponds to “for all situations in the
description the entity exists with certain property
set”. More details on entities and their
contextual and non-contextual properties are
given below in Section 3.2.
An event marks a transition from one situation to
another. Events always have time properties.
The effect is that a situation implicitly has time
duration as defined by its bounding events
(associated via precedes and follows properties).
As an example, the model could express the loan
of the Mona Lisa to the Metropolitan Museum
for a fixed period (e.g., May 1, 2000 through
May 30, 2001) as follows: an existential facet of
the Mona Lisa with a property “located at the
Metropolitan could be associated with a
situation that is related via precedes and follows
properties with two events, one of which gives
the time of the loan, the other the time of the
return. The use of the hasPresence property
with an Event models the fuzzy concept of the
participation of an Agent in the Event more
precise notions of participation require the
Action concept as described below.
An Action provides the mechanism for modeling
increased knowledge about the involvement and
responsibility of agents in events. Specifically,
it denotes a verb in the context of event the
hasAction property connects an Action to an
Event. Returning to the example of painting the
car, one might model this using one Event (the
painting) and two Situations to provide the
context for the existential facets of the paint can
and car as described above. One might also
want to express the fact that John held the paint
can and Sue did the actual painting. Actions
provide the ontological framework for
expressing these “verbs” and associating the
specific agency with them. In addition, the
involves property (and its sub-properties) makes
it possible to explicitly associate actions with
effects on existential facets of entities. Finally,
the hasParticipant property, and its possible
domain-specific sub-properties, makes it
possible to precisely specify the association of
an Agent with an Action. The combination of
these makes it possible to clearly state entity
derivations (e.g., translations, reformatting, etc.)
and modifications and who or what is
responsible for them.
As shown in the examples in Section 5, these
three temporal classes make it possible to
unambiguously express statements like: “In 1998
Quentin Blake acted as Illustrator in the event that
led to a situation where a soft cover edition of
‘Charlie and the Chocolate Factory’ existed”.
3.2. Actuality Category
The Actuality ontology category encompasses
entities that are sensible they can be heard, seen,
smelled, or touched. This contrasts with the
Abstraction category, which encompasses concepts.
As described in Section 3.1 entities that are
Actualities, can have one universal or time-
independent facet and many existential or time-
dependent facets. ABC expresses this notion
through the inContext property that associates an
Actuality with a Situation. For example, an ABC
description of Bill Clinton might have an existential
Actuality with property “President of the United
States” that is related via the phaseOf property to one
universal Actuality with the property “born in
Arkansas”. The existential facet would be related via
the inContext property to a Situation that follows an
Event representing Clinton’s election in 1992. The
result is a statement that expresses the “sameness” of
the two entities (they are both “Bill Clinton”), but the
fact that one is an existential facet and one is a
universal facet.
The ABC model also incorporates intellectual
creation semantics influenced by the IFLA FRBR
[9]. A sub-category of Actuality, Artifact, expresses
sensible entities that are tangible realizations of
concepts, and that can be manifested in multiple
ways; e.g., as Manifestations and Items as expressed
in the FRBR.
3.3. Abstraction Category
The Abstraction category makes it possible to
express concepts or ideas. Entities in this category
have two notable characteristics:
1. They are never in the context of a situation.
While it can be argued that an idea is “born” at
some time, ABC treats the birth of an idea”
when it is manifested in some sensible way;
e.g., when it is told, demonstrated, or shown in
some manner.
2. Correspondingly, ideas cannot exist in
isolation in the model. They must be bound to
some Actuality through the hasRealization
property.
The main use of the Abstraction category is to
express the notion of Work in the FRBR sense; that
is, as a means of binding together several
Manifestations of an intellectual Expression. For
example, an ABC description of the Hamlet might
instantiate a Work that binds the folio manifestation,
a Stratford performance, and a Penguin edition.
4 ABC Classes and Properties
This section describes the elements of the ABC
ontology, expressed using RDF primitives [20, 31].
The section is divided into a list of the basic classes
of the model and the properties that relate instances
of those classes. The notions of SubClass,
SubProperty, Domain, and Range are used in the
manner of RDF. An RDF schema representation of
this class and property structure is available in
Appendix A.
4.1. Classes
The figure in Appendix B shows the hierarchical
relationships between the ABC classes, which are
described below. Classes are shown in rectangles
and sub-class relationships are indicated by solid
lines.
Name Entity
Subclass of none
Description
The primitive category having no differentiae.
Name Temporality
Subclass of Entity
Description
A primitive ontology category for sub-classing
categories of entities that provide time existential
contexts.
Name Actuality
Subclass of Entity
Description
A primitive ontology category for sub-classing
categories of entities that have a tangible
existence in some world view. Actualities as
identities and properties associated with those
properties have a duality as universal - entities
whose identity as a set of
properties/characteristics are time independent, or
universal, relative to the world view of a model -
and existential - entities whose identity as a set of
properties/characteristics are local, or existential,
to situations/contexts in a model. The phaseOf
and inContext properties are means of co-relating
an existential facet of an Actuality and its
universal facet and of associating the existential
facet with a specific situation.
Name Abstraction
Subclass of Entity
Description
A primitive ontology category for sub-classing
categories of entities that are pure information or
concepts (stands in contrast to the Actuality
category).
Name Artifact
Subclass of Actuality
Description
A type of Actuality that is the tangible realization
of some consciously conceived Abstraction - a
prototypical example is intellectual content. The
primary distinguishing characteristics of Artifacts
is that they can be manifested in a number of
ways and copied - for example the book
"Hamlet" is an Artifact (of the Abstraction
Hamlet) since it is one of many possible
Actualities. This contrasts to the Actuality
William Shakespeare who admittedly may have
been pre-conceived by his parents but can not be
manifested in various ways. Similarly a
historical museum object such as a dinosaur bone
is an Actuality but not an Artifact.
Name Event
Subclass of Temporality
Description
An Event marks a transition between Situations,
one that is associated with the event through a
precedes property and another through a follows
property. The time granularity of the transition is
variable - for example some Events are truly an
instant (a point in time). However, an Event may
have coarser granularity such as span of time
during which some situation change was
undertaken (for example, the painting of the
Sistine Chapel Ceiling). The granularity of the
snapshot is associated with the Event via a single
atTime property.
Name Situation
Subclass of Temporality
Description
A Situation is a context for making time-
dependent or existential assertions about
Actualities. Each Situation can act as context for
existential facets of multiple Actualities. The
time certainty for a Situation is implicitly within
the time contexts for the events that enclose it
(i.e. a Situation can serve as the precedes of one
Event and the follows of another). However, the
time certainty can be explicitly stated via a
atTime property on the Situation. The purpose of
this is to make the model as closed as possible -
where the time certainty of Events and Situations
is known.
Name Action
Subclass of Temporality
Description
An activity or verb performed by some Agent or
Agents in the context of an Event. Actions may
involve an Actuality, which may be in its
existential or universal facet, and may have result
that is another Actuality, which always must be in
its existential facet.
Name Agent
Subclass of Actuality
Description
An Actuality that is present during an Event or is
the party of some Action. Agents may be
persons, instruments, organizations, etc.
Name Work
Subclass of Abstraction
Description
An Abstraction that is intellectual property in the
IFLA FRBR sense. A Work is an abstract concept
which can not exist in a model in isolation, but is
only revealed when it has been actualized in
some Manifestation.
Name Manifestation
Subclass of Artifact
Description
A form of an Artifact that stands as the sensible
realization of a Work. Works and Manifestations
stand in a one to many relationship. The
hasRealization property associates a Work with
its Manifestation(s). Associating several
Manifestations with a Work through the
hasRealization property defines those
Manifestations as members of a (fuzzy)
equivalence class implicitly identified by the
common Work.
Name Item
Subclass of Artifact
Description
A form of an Artifact used to establish a set of
identical copies. Manifestations and Items stand
in a one to many relationship. The hasCopy
property associates a Manifestation with its
Items. Associating several Items with a
Manifestation through the hasCopy property
defines those Items as members of an exact
equivalence class.
Name Time
Subclass of Entity
Description
An entity which represents either a time span or
point in time and which can be used to confine
the temporal extent of Temporalities (Events or
Situations). The Time entity provides the range
constraint for the atTime property.
Name Place
Subclass of Entity
Description
An entity which represents spatial location. It can
be used to specify the location of either
Temporalities (Events and Situations) or
Actualities. The Place entity provides the range
constraint for the inPlace property.
4.2. Properties
The figure in Appendix B illustrates the
relationship of the ABC properties to the ABC
classes. Classes are shown in rectangles and solid
lines indicate sub-class relationships. Properties are
down as dashed lines directed from their domain
class(es) to their range class. A property that does
not have a defined range is indicated by an oval at
the end of a dashed arc. Finally, property/sub-
property relationships are indicated by dotted arcs
with an oval of the sub-property at the end of the arc.
The definitions of the ABC properties illustrated in
Appendix C are provided below.
Name precedes
Subproperty of none
Domain Event
Range Situation
Description
Binds a Situation and the Actualities within its
context as existing before an Event.
Name follows
Subproperty of none
Domain Event
Range Situation
Description
Binds a Situation and the Actualities within its
context as existing after an Event. There is no
explicit implication of causality between the
Event and Actualities existing in the Situation
that is the value of the follows property.
Causality between Events or Actions in Events
and Actualities is established through the
hasResult property and its sub-properties.
Name isPartOf
Subproperty of none
Domain Entity
Range Entity
Description
Establishes an "is Part Of" relationship between
one Entity and another (it is the inverse of the
contains Relationship).
Name contains
Subproperty of none
Domain Entity
Range Entity
Description
Establishes a "contains" relationship between one
Entity and another (inverse of isPartOf). The
inverse properties are explicitly expressed due to
the need to model the notion of an Actuality that
does not makes sense without its “parts” and,
equally, the notion of an Actuality that does not
makes sense without its containment.
Name isSubEventOf
Subproperty of isPartOf
Domain Event
Range Event
Description
Establishes an “is Part of” relationship between
one Event and another; e.g., the relationship
between “D-Day” and “World War II”. The
relationship does not imply semantic constraints
such as containment of the atTime property of
one Event within another or relationships
between the associated Situations. Note the
distinction between two Events related by the
isSubEventOf property and an Event and an
Action related by the hasAction property. In the
former case, each Event acts as transition points
between different preceding and following
Situations. In the latter case, there is a single
Event with one preceding and following
Situation, but individual verbs or Actions within
that Event.
Name inContext
Subproperty of none
Domain Actuality
Range Situation
Description
Establishes an Actuality as an existential, which
means that its property set exists within the
context of Situation that is associated as the value
of this property.
Name phaseOf
Subproperty of none
Domain Actuality
Range Acuality
Description
Establishes the relationship between an
existential facet and a universal facet of an
Actuality.
Name hasRealization
Subproperty of none
Domain Work
Range Manifestation
Description
Binds a Manifestation within the conceptual
umbrella of a Work. A Work may have several
hasRealization properties, which establishes a
fuzzy equivalence set among Manifestations,
implicitly stating that the properties of the subject
Work are shared across the object
Manifestation(s).
Name hasCopy
Subproperty of none
Domain Manifestation
Range Item
Description
Binds an Item as one of several copies of a
Manifestation. A Manifestation may have
several hasCopy properties, which establishes an
exact equivalence set among Manifestations,
implicitly stating that the properties of the subject
Manifestation are shared across the object
Item(s).
Name involves
Subproperty of none
Domain Action, Event
Range Actuality
Description
Expresses the involvement of an Actuality in the
performance of an Action or an Event. There is
no implication of transformation or lack thereof
in this use. (Such specialization of involves is
expressed using the hasPatient and usesTool
properties.)
Name hasPatient
Subproperty of involves
Domain Action ,Event
Range Actuality
Description
Strengthens the notion of involves to the classic
patient sense stating that the Actuality that is the
value of this property is transformed by the
Action or Event. For example, the action
expressing the rebinding of a book might have
the book related via a hasPatient property.
Name usesTool
Subproperty of involves
Domain Action, Event
Range Actuality
Description
A specialization of involves that in effect
weakens the notion of involvement of the
Actuality in the Action or Event - e.g., it is used
but not transformed as in the case of a camera in
the production of a picture.
Name hasResult
Subproperty of none
Domain Action, Event
Range Actuality
Description
Expresses the result of an Actuality, which
always must be in an existential facet, in the
performance of an Action (in the context of an
Event).
Name destroys
Subproperty of hasPatient
Domain Action, Event
Range Actuality
Description
A specialization of hasPatient that indicates that
the value Actuality ceases to exist in Situation(s)
that follow the Event. Any Actuality that is not
explicitly destroyed can be assumed to exist in
subsequent Situations even though it might not be
explicitly represented.
Name creates
Subproperty of hasResult
Domain Action, Event
Range Actuality
Description
Specializes hasResult to mean the coming into
existence of the Actuality that is the value of this
property. This means that the Actuality can be
assumed to not exist in Situations prior to the one
in which the created instance of the Actuality
appears.
Name hasAction
Subproperty of none
Domain Event
Range Action
Description
An Event can have one or more Actions, which
are verbs performed by Agents in the context of
the Event.
Name hasPresence
Subproperty of none
Domain Event
Range Agent
Description
Associates an Agent as being present in the
context of an Event. The notion of “presence” is
purposely weak there is no implication that the
target Agent is an active participant in the
transition marked by the Event.
Name hasParticipant
Subproperty of hasPresence
Domain Event, Action
Range Agent
Description
Refines hasPresence to associate an Agent as an
active participant in an Event or Action.
Combined with the hasPatient and hasResult
properties attached to an Action, this permits
definite statements of subject and causality – e.g.,
“John did the painting that turned the car from
red to green”. The inverse property is
participatesIn.
Name atTime
Subproperty of none
Domain Temporality
Range Time
Description
Associates a time with an entity that is a sub-
category of a Temporality. The range for this
property is the Time entity.
Name inPlace
Subproperty of none
Domain Actuality, Temporality
Range Place
Description
Associates a location with an entity. The entity
can be an Actuality or Temporality. The range for
this property is the Place entity.
5 ABC Modeling Experiments and
Examples
As a result of a collaboration between the
Harmony project and the CIMI Consortium [5], a
Call for Participation (CfP) [34] was issued to CIMI
members in October 2000. Interested CIMI members
were invited to contribute approximately 100
museum metadata records and the associated
multimedia digital objects. The goal of the
experiment was to evaluate the ABC model and its
usability as means of mapping among disparate
metadata ontologies. Four organizations responded
to the CfP:
1. Australian Museums Online (AMOL);
2. Natural History Museum of London;
3. Research Libraries Group/Library of
Congress;
4. National Museum of Denmark.
A detailed description of the images and data
provided by the CIMI members is available at [11].
Detailed results of the experiment are available at
[4].
This section summarizes some of those results.
The complexity of many of the CIMI examples
makes them impractical as introductory examples of
the application of the ABC model. Instead, we have
chosen to illustrate three fictional, but realistic,
examples in Sections 5.1, 5.2, and 5.3 and then
include one of the simpler CIMI examples in Section
5.4. All of the examples are illustrated as RDF-like
node and arc diagrams. An XML serialization of
these graphs would also be possible, but space
limitations prevent including these.
5.1. Children’s Book
Example Narrative: The book, "Charlie and the
Chocolate Factory" was written by Roald Dahl in
1964. The first edition (a hardcover, illustrated by
Joseph Shindleman) was published in 1985 by
Knopf. A second edition was published in 1998 by
Puffin. It was a paperback illustrated by Quentin
Blake. In 1995, a 3 hour audiocassette recording of
the book was produced by Caedmon. It was narrated
by Robert Powell and the caterer during production
was "Samn Ella’s Catering".
The graphical representation corresponding to the
ABC model for this example is shown in Appendix
C.
5.2. Dinosaur Bone
Example Narrative: A dinosaur bone was
discovered by Richard Leakey in 1995 in Kenya. In
1971 it was acquired by the British Museum in
London and added to its collection. In 1991, Jean
Smith, the curator of the British Museum, classified
the bone as part of a plesiosaur. In 1998, Richard
Hill photographed the bone using a digital camera (a
Nikon 990). In 1999, this image of the dinosaur bone
was published on the museums web site.
The graphical representation corresponding to the
ABC model for this example is shown in Appendix
D.
5.3. Birth
Example Narrative: On June 14 2001 at the
Wesley Hospital, an 8lb 11oz baby girl was delivered
to parents Jill and John Smith. The obstetrician at the
delivery was Jane Jekyl and the midwife was Carl
Hyde.
The graphical representation corresponding to the
ABC model for this example is shown in Appendix
E. This example demonstrates how the Action type
refines or more narrowly specifies the actions which
occur within an Event. The Agent’s
ParticipationType further refines or more narrowly
specifies the role of the specific agents within an
action.
5.4. AMOL Vase Example
This final example is based on a metadata record
from the Powerhouse Museum in Sydney, describing
the physical characteristics, life history and digital
surrogates of a vase in the collection. The actual
metadata record is available at [2]. This record
demonstrates the difficulty of automated mappings
from existing metadata descriptions. As shown, the
value for each metadata tag is a natural language
paragraph, in which is embedded complex
information on the lifecycle events of the vase.
Automated processing of this record would require
natural language processing techniques.
The graphical representation corresponding to the
ABC model for this record is shown in Appendix F.
6 Searching over the model
The ABC model allows users to ask much more
sophisticated queries than is possible via less
expressive metadata models such as Dublin Core
e.g., "Tell me all of the previous owners of an
object", "Give me all of those objects which were
acquired as gifts and the donor’s name and address".
By using the ABC model, one is able to record and
retrieve the history of an object from its creation,
through to its use, change of ownership, relocation,
modification, digitization and repurposing.
The ABC model as described heretofore provides
an abstract, syntax-neutral conceptual framework for
modeling metadata. However in order to create, store
and query the metadata descriptions, a concrete
syntax is required. RDF provides one possible XML
syntax for encoding and exchanging metadata
descriptions. Although alternate XML encodings of
the data models are possible, we have chosen to
encode the graphical ABC models of the CIMI data
at [4] in RDF/XML syntax to maximize
interoperability, since RDF provides a well-defined
mechanism for encoding in XML instances of
ontologies. An RDF/XML representation of the
AMOL example in 5.4 is provided at [1].
Although fully-automated mapping of the
existing CIMI metadata records to the ABC model is
an unrealistic goal, we are currently working on
organization-specific XSLT programs capable of
mapping each set of CIMI records into RDF
descriptions based on the ABC model. Using XSLT,
combined with the semantic knowledge provided by
MetaNet, a metadata term ontology [24], it is
possible to streamline the generation of the ABC
metadata descriptions; making use of automatic
facilities where possible and augmenting them with
some human effort.
Given the resulting collection of RDF/XML
descriptions, we then use the Squish RDF query
engine developed at ILRT as part of the Harmony
project, to query the RDF files directly. Squish is a
generalizable Java RDF query engine [33] which can
run SQL-like query strings over many instances of
RDF descriptions. For example, the Squish query
below requests all of the events (and their type, time
and place) that occur in the previous AMOL example
[1]:
SELECT ?event, ?type, ?time, ?place FROM
http://ilrt.org/discovery/harmony/amol.rdf
WHERE
(web::type ?event abc::Event)
(abc::context ?event ?context)
(dc::type ?event ?type)
(abc::time ?context ?time)
(abc::place ?context ?place)
USING web FOR
http://www.w3.org/1999/02/22-rdf-syntax-ns#
abc FOR http://ilrt.org/discovery/harmony/abc-
0.1#
dc for http://purl.org/dc/elements/1.1/
A Squish web search interface demo to the AMOL
images is available at: [13]. Once the RDF
descriptions for all of the image/data sets have been
generated, then this search interface will be extended
to search across all of the CIMI images, as illustrated
in Figure 1.
7 Future work and Conclusions
Because the ABC model has been specifically
designed to model the creation, evolution and
transition of objects over time, we are particularly
interested in investigating its application to
multimedia asset management metadata within
organizational workflows. Hence, a future goal is to
design a workflow management system that
automatically invokes the appropriate metadata
editing and generation tools as objects proceed
through an organization’s workflow, from creation or
acquisition, to editing, reuse, copying, resale and
preservation. Such a tool would realize some of the
record-keeping goals articulated by Bearman and
Trant in [18]. In theory, the ABC model should
provide the ideal underlying schema for modeling,
validating, storing, navigating and searching the
different types of metadata generated from the
sequence of event-triggered metadata input tools.
Finally, in recognition of the extensive overlap
of the goals of the Harmony project and the
CIDOC/CRM, a DELOS Working Group on
Ontology Harmonization has been established. The
first workshop was held in Rome in May, 2001 [22].
A second workshop is planned for September, 2001
in Darmstadt. The objective of this working group is
to investigate merging the concepts of the ABC
model and the CIDOC CRM into a single ontology
and in the process, to determine:
methodologies for comparing, merging and
sharing ontologies;
representational alternatives for ontologies;
the optimum approach to the management of
sharable or merged ontologies and the future
merging of additional ontologies.
In closing, our work on developing the ABC
model has been extremely useful in elevating our
understanding of the metadata landscape and in
AMOL
Metadata
NHM
Metadata
NMD
Metadata
RLG/LoC
Metadata
XSLT
MetaNet
Metadata Term
Ontology
Metadata
Repository
ABC Data Model
HTTP
Server
AMOL
Images
NHM
Images
NMD
Images
RLG/LoC
Images
Search
Interface
Client Web
Browser
comprehending what people are trying to accomplish
with their resource descriptions. For instance, work
with Dublin Core records from the CIMI community
demonstrates a desire to represent relatively complex
lifecycle information for which the simple Dublin
Core model is inadequate. As mentioned earlier, the
appropriateness of any metadata model must be
measured by balancing the specificity of the
knowledge that can be represented in it and queried
from it and the expense of creating the descriptions.
Our experiments with ABC demonstrate the
usefulness of metadata models with temporal
semantics for the class of descriptions where that
level of knowledge representation is deemed
appropriate.
8 Acknowledgements
The work described in this paper has been carried
out within the Harmony International Digital Library
Project. It has been funded by NSF Grant 9905955,
JISC Grant 9906 and the Cooperative Research
Centre for Enterprise Distributed Systems
Technology (DSTC) through the Australian Federal
Government’s CRC Programme. The authors wish to
acknowledge the valuable contributions made to this
work by Dan Brickley and Libby Miller from ILRT.
9 References
[[1] AMOL RDF Record, 2001
http://metadata.net/harmony/amol.rdf.
[[2] AMOL Vase Description, 2001
http://www.metadata.net/harmony/amol.htm
l.
[[3] CIDOC Conceptual Reference Model
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[[4] CIMI ABC Modeling Examples, 2001
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[[5] CIMI Consortium, 2001
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[[6] Dublin Core Metadata Element Set, Version
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[[8] Dublin Core Qualifiers, 2000
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[[12] INDECS Home Page: Interoperability of
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[[14] Semantic Web Activity: Resourse
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[[22] M. Doerr and J. Hunter, DELOS Working
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[[23] M. Gorman, The concise AACR2, 1988
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hunter/OralHistory/paper.html.
[[26] J. Hunter and C. Lagoze, “Combining RDF
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nterLagozeWWW10.pdf.
[[27] ICOM/CIDOC Documentation Standards
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Washington DC, 2000,
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_paper.html.
[[29] C. Lagoze, “Keeping Dublin Core Simple:
Cross Domain Discovery or Resource
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http://www.dlib.org/dlib/january01/lagoze/0
1lagoze.html.
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Event-Aware Model for Metadata
Interoperability,” presented at ECDL 2000,
Lisbon, 2000,
http://archive.dstc.edu.au/RDU/staff/jane-
hunter/harmony/harmony_ECDL2000.zip.
[[31] O. Lassila and R. R. Swick, “Resource
Description Framework: (RDF) Model and
Syntax Specification,” World Wide Web
Consortium, W3C Proposed
Recommendation PR-rdf-syntax-19990105,
January 1999. http://www.w3.org/TR/PR-
rdf-syntax/.
[[32] J. McCarthy, “Programs with common
sense,” in Semantic Information Procession,
M. Minsky, Ed.: MIT Press, 1968, pp. 403-
418.
[[33] L. Miller, RDF querying using Squish, 2000
http://swordfish.rdfweb.org/rdfquery/.
[[34] J. Perkins, ABC/Harmony CIMI
Collaboration Project Description, 2000
http://www.cimi.org/public_docs/Harmony_
long_desc.html.
[[35] J. L. Peterson, Petri net theory and the
modeling of systems. Englewood Cliffs N.J.:
Prentice-Hall, 1981.
[[36] J. F. Sowa, Conceptual Graphs, 2001
http://www.bestweb.net/~sowa/cg/.
Appendix A ABC Model Expressed as an RDF Schema
<?xml version="1.0" encoding="UTF-8"?>
<!-- edited with XML Spy v4.0 U (http://www.xmlspy.com) by Carl Lagoze (Cornell University) -->
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-
schema#">
<rdfs:Class rdf:ID="Entity"/>
<rdfs:Class rdf:ID="Temporality">
<rdfs:subClassOf rdf:resource="Entity"/>
</rdfs:Class>
<rdfs:Class rdf:ID="Actuality">
<rdfs:subClassOf rdf:resource="Entity"/>
</rdfs:Class>
<rdfs:Class rdf:ID="Abstraction">
<rdfs:subClassOf rdf:resource="Entity"/>
</rdfs:Class>
<rdfs:Class rdf:ID="Time">
<rdfs:subClassOf rdf:resource="Entity"/>
</rdfs:Class>
<rdfs:Class rdf:ID="Place">
<rdfs:subClassOf rdf:resource="Entity"/>
</rdfs:Class>
<rdfs:Class rdf:ID="Artifact">
<rdfs:subClassOf rdf:resource="Actuality"/>
</rdfs:Class>
<rdfs:Class rdf:ID="Event">
<rdfs:subClassOf rdf:resource="Temporality"/>
</rdfs:Class>
<rdfs:Class rdf:ID="Situation">
<rdfs:subClassOf rdf:resource="Temporality"/>
</rdfs:Class>
<rdfs:Class rdf:ID="Action">
<rdfs:subClassOf rdf:resource="Temporality"/>
</rdfs:Class>
<rdfs:Class rdf:ID="Agent">
<rdfs:subClassOf rdf:resource="Actuality"/>
</rdfs:Class>
<rdfs:Class rdf:ID="Work">
<rdfs:subClassOf rdf:resource="Abstraction"/>
</rdfs:Class>
<rdfs:Class rdf:ID="Manifestation">
<rdfs:subClassOf rdf:resource="Artifact"/>
</rdfs:Class>
<rdfs:Class rdf:ID="Item">
<rdfs:subClassOf rdf:resource="Artifact"/>
</rdfs:Class>
<rdf:Property rdf:ID="precedes">
<rdfs:domain rdf:resource="Event"/>
<rdfs:range rdf:resource="Situation"/>
</rdf:Property>
<rdf:Property rdf:ID="follows">
<rdfs:domain rdf:resource="Event"/>
<rdfs:range rdf:resource="Situation"/>
</rdf:Property>
<rdf:Property rdf:ID="isPartOf">
<rdfs:domain rdf:resource="Entity"/>
<rdfs:range rdf:resource="Entity"/>
</rdf:Property>
<rdf:Property rdf:ID="contains">
<rdfs:domain rdf:resource="Entity"/>
<rdfs:range rdf:resource="Entity"/>
</rdf:Property>
<rdf:Property rdf:ID="isSubEventOf">
<rdfs:domain rdf:resource="Event"/>
<rdfs:range rdf:resource="Event"/>
<rdfs:subPropertyOf rdf:resource="isPartOf"/>
</rdf:Property>
<rdf:Property rdf:ID="inContext">
<rdfs:domain rdf:resource="Actuality"/>
<rdfs:range rdf:resource="Situation"/>
</rdf:Property>
<rdf:Property rdf:ID="phaseOf">
<rdfs:domain rdf:resource="Actuality"/>
<rdfs:range rdf:resource="Actuality"/>
</rdf:Property>
<rdf:Property rdf:ID="hasRealization">
<rdfs:domain rdf:resource="Work"/>
<rdfs:range rdf:resource="Manifestation"/>
</rdf:Property>
<rdf:Property rdf:ID="hasCopy">
<rdfs:domain rdf:resource="Manifestation"/>
<rdfs:range rdf:resource="Item"/>
</rdf:Property>
<rdf:Property rdf:ID="involves">
<rdfs:domain rdf:resource="Action"/>
<rdfs:domain rdf:resource="Event"/>
<rdfs:range rdf:resource="Actuality"/>
</rdf:Property>
<rdf:Property rdf:ID="hasPatient">
<rdfs:domain rdf:resource="Action"/>
<rdfs:range rdf:resource="Actuality"/>
<rdfs:subPropertyOf rdf:resource="involves"/>
</rdf:Property>
<rdf:Property rdf:ID="usesTool">
<rdfs:domain rdf:resource="Action"/>
<rdfs:range rdf:resource="Actuality"/>
<rdfs:subPropertyOf rdf:resource="involves"/>
</rdf:Property>
<rdf:Property rdf:ID="hasResult">
<rdfs:domain rdf:resource="Action"/>
<rdfs:domain rdf:resource="Event"/>
<rdfs:range rdf:resource="Actuality"/>
</rdf:Property>
<rdf:Property rdf:ID="destroys">
<rdfs:domain rdf:resource="Event"/>
<rdfs:domain rdf:resource="Action"/>
<rdfs:range rdf:resource="Actuality"/>
<rdfs:subPropertyOf rdf:resource="hasPatient"/>
</rdf:Property>
<rdf:Property rdf:ID="creates">
<rdfs:domain rdf:resource="Action"/>
<rdfs:domain rdf:resource="Event"/>
<rdfs:range rdf:resource="Actuality"/>
<rdfs:subPropertyOf rdf:resource="hasResult"/>
</rdf:Property>
<rdf:Property rdf:ID="hasAction">
<rdfs:domain rdf:resource="Event"/>
<rdfs:range rdf:resource="Action"/>
</rdf:Property>
<rdf:Property rdf:ID="hasPresence">
<rdfs:domain rdf:resource="Event"/>
<rdfs:domain rdf:resource="Action"/>
<rdfs:range rdf:resource="Agent"/>
</rdf:Property>
<rdf:Property rdf:ID="hasParticipant">
<rdfs:domain rdf:resource="Action"/>
<rdfs:range rdf:resource="Agent"/>
<rdfs:subPropertyOf rdf:resource="hasPresence"/>
</rdf:Property>
<rdf:Property rdf:ID="atTime">
<rdfs:domain rdf:resource="Temporality"/>
<rdfs:range rdf:resource="Time"/>
</rdf:Property>
<rdf:Property rdf:ID="inPlace">
<rdfs:domain rdf:resource="Actuality"/>
<rdfs:domain rdf:resource="Temporality"/>
<rdfs:range rdf:resource="Place"/>
</rdf:Property>
</rdf:RDF>
Appendix B ABC Class Hierarchy with Property Relationships
Entity
Abstraction
Temporality
Event
Actuality
Situation
Artifact
Agent
Work
Manifestation
Item
Time
Place
Action
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Appendix D Dinosaur Bone Example
ST0
precedes
MN0
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O
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x
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"Dinosaur Bone"
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"British Museum"
l
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x
t
"British Museum"
h
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AG1
EV1
"museum"
p
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Y
p
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"acquisition"
"1971"
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"London"
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f
:
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u
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T
i
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follows
ST1
precedes
"plesiosaur"
s
p
eci
e
s
"Jean Smith"
h
a
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P
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r
t
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AG2
EV2
"curator"
p
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c
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T
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"classification"
"1991"
i
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P
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"British Museum"
r
d
f
:
t
y
p
e
r
d
f
:
v
a
l
u
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a
t
Ti
m
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follows
ST2
precedes
p
h
a
s
e
O
f
i
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C
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t
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x
t
EV3
follows
"Richard Hill"
h
a
s
P
a
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c
i
p
a
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t
AG3
AC3
hasAction
"photographer"
p
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"1998"
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"British Museum"
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"Image of Dinosaur Bone"
d
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p
h
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O
f
h
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n
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MN2
"Nikon 990"
EV4
follows
ST4
precedes
h
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P
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AG4
"webmaster"
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T
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"1999"
i
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l
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"British Museum"
a
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"publishing"
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"imaging"
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"British Museum Web Page"
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isPartOf
WK0
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"British Museum"
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Contains
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"Richard Leakey"
h
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"discoverer"
p
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"discovery"
"1965"
i
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"Kenya"
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df:t
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:
v
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"digital camera"
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"JPEG"
f
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"HTML"
t
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"Dinosaur Bone"
Appendix E Birth Example
EV0
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"birth"
p
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d
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"14/06/01"
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"delivery"
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"parenting"
"Jill Smith"
"mother"
AG1
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:
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rd
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"John Smith"
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"Carl Hyde"
"midwife"
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AG3
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:
v
a
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Hospital"
Appendix F AMOL Vase Example
"040794.jpg"
r
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"imaging"
MN1
f
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"040792.jpg"
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MN0
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h
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P
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"potter"
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"manufacturing"
"1984"
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"Sydney"
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"Stephen Bowers"
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t
AG1
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"design"
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"Adelaide"
"1993-1994"
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"used"
n
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"Robin Gibson Gallery"
AG3
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:
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:
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"exhibitor"
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"owned/exchanged"
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i
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P
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a
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"Darlinghurst"
"Nov-Dec94"
a
t
T
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EV1
ST1
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... Ship activity events can modeled using general event models. In current academic research, concept-based event models [24][25][26][27], logic-based hierarchical event models [28][29][30][31], and sextuplet-based event models [32][33][34][35] are used. The modeling of ship activities in this study belongs to the category of domainspecific ontology modeling, where concept-based event models such as ABC ontology [24] model, SEM [25], EO [26] model, and CIDOC-CRM [27] model are primarily used. ...
... In current academic research, concept-based event models [24][25][26][27], logic-based hierarchical event models [28][29][30][31], and sextuplet-based event models [32][33][34][35] are used. The modeling of ship activities in this study belongs to the category of domainspecific ontology modeling, where concept-based event models such as ABC ontology [24] model, SEM [25], EO [26] model, and CIDOC-CRM [27] model are primarily used. ...
Article
Full-text available
This study focused on the construction of a spatiotemporal knowledge graph for ship activities. First, a ship activity ontology model was proposed to describe the entities and relations of ship activities. Then, maritime event text data were utilized as the ship activity dataset, where entities and relations were extracted to form triplets. Thus, the data layer was populated, completing the construction of the ship activity spatiotemporal knowledge graph. The process of extracting triplets involved initially inputting the text sentences into the Bidirectional Encoder Representations from Transformers (BERT) model for pretraining to obtain vector representations of characters. These representations were then fed into a lattice long short-term memory network (Lattice-LSTM) for further processing. The resulting hidden vectors h1,h2,⋯,hn were input into the conditional random field (CRF) to perform named entity recognition. The recognized entities were then labeled in the original sentences and input into another BERT-Lattice-LSTM network. The resulting hidden vectors h′1,h′2,⋯,h′n were fed into a relation classifier, which output the relation between the two labeled entities, completing the extraction of entity–relation triplets. In experiments, the proposed method achieved triplet extraction performance exceeding 90% for three different evaluation metrics: Precision, Recall, and F1-measure.
... More precisely, events are typically modelled using semantic models and ontologies [13] to represent events and their context. Domain-related event ontologies and models have been developed for the needs of specific domains, namely for Music [14], Museum [15], [16], [17], News [18] and Multimedia [19]. More specifically, in the area of PA, [20] developed a life-event model containing the main classes of an ontology to model life events (e.g., child birth) and all public services related to a citizen's new life situation (e.g., obtaining child allowance). ...
... The state of a public servant at particular times [15], [41] Event Everything that happens in a public servant's life and is under the scope of HRM [26], [28], [29] Time When something happens [19], [26] Place Where something happens [19], [26] Action Something performed by an actor (e.g., public organization) [16] Public Service A mandatory or discretionary set of activities performed, or able to be performed, by or on behalf of a public organization, publicly funded and arise from public policy. ...
... At the level of ontology design, we referred to the seven-step method [75] to construct the hierarchical and logical relationships of the ontology. We applied relevant concepts from the ABC ontology model [76], which is a general conceptual model that provides metadata descriptions of complex objects to facilitate interoperability between metadata ontologies in different domains. ...
Article
Full-text available
The outbreak of COVID-19 (coronavirus disease 2019) has generated a large amount of spatiotemporal data. Using a knowledge graph can help to analyze the transmission relationship between cases and locate the transmission path of the pandemic, but researchers have paid little attention to the spatial relationships between geographical entities related to the pandemic. Therefore, we propose a method for constructing a pandemic situation knowledge graph of COVID-19 that considers spatial relationships. First, we created an ontology design of the pandemic data in which spatial relationships are considered. We then constructed a non-spatial relationships extraction model based on BERT and a spatial relationships extraction model based on spatial analysis theory. Second, taking the pandemic and geographic data of Guangzhou as an example, we modeled a pandemic corpus. We extracted entities and relationships based on this model, and we constructed a pandemic situation knowledge graph that considers spatial relationships. Finally, we verified the feasibility of using this method as a visualization exploratory tool in the analysis of spatial characteristics, pandemic development situation, case sources, and case relationships analysis of pandemic-related areas.
... Indicatively, the ABC Ontology for digital libraries, whose purpose was to facilitate interoperability between metadata vocabularies from different domains. [16]; the Event ontology [17] is an event-centric model for the domain of Music, which defines events as arbitrary classifications of space/time regions by a cognitive agent that may have participating agents, passive factors, products, and a location in space/time. Event-Model-F [18] designed to facilitate interoperability in distributed event-based systems. ...
Article
Full-text available
The article presents the building of an event-centric model for the computational representation of crisis events using an ontology encoded in the Web Ontology Language (OWL). The work presented here is done in collaboration with the Leaders and Crisis Management in Ancient Literature. A Comparative Approach (LACRIMALit) project, (2022–2025) hosted at the Institute for Mediterranean Studies/Foundation for Research and Technology (IMS-FORTH). A key outcome of the project is the LACRIMALit ontology that aims principally at the semantic annotation of ancient Greek historiographical texts in open access via Perseus Digital Library. The ontology will facilitate reasoning on and across these documents and enable their semantic querying. The tagset of annotations, concepts, relations, and terms of the ontology will be both human and machine readable, extensible and reusable. The annotated corpus of texts to be produced will be available for sophisticated queries based on the concepts and relations, defined by the ontologies. This will considerably improve the string-based querying of the texts in their present digital format. This article presents the principles of conceptualization of the domain in the three dimensions: domain knowledge (mainly classes illustrated with some individuals), linguistic dimension (terms, proper names, definite descriptions), and references.
... According to domain characteristics and based on ABC ontology model (Lagoze & Hunter, 2001), the core relationship between toplevel ontology concepts in doing business cases is shown in Fig. 4. The case is composed of a Scenario, Situation, and Solution. The Scenario is the combination of time and space, including Spatial_Location and Temporal_Location. ...
Article
Sound ease of doing business is essential for building a modernized economy and fostering high-quality growth. Research on the ease of doing business should not only focus on the impact and evaluation system, but also have guiding significance for decision-making related to the ease of doing business, but there are still relatively few studies in this area. Due to the complexity of ease of doing business, to save energy and make the most of the experience knowledge of decision-makers, we have introduced case-based reasoning (CBR) into strategy response, as a possible application of the analysis of case histories. The purpose of this paper is to propose a CBR strategy response method for ease of doing business, which can be used to formulate more intelligent and efficient ease of doing business strategy. The innovation of this research focuses on three key parts. Firstly, a CBR strategy response method for ease of doing business is innovatively developed. Secondly, the ontology method is used to build ease of doing business domain model, including city features, ease of doing business indicators, and response strategy. Thirdly, Missing values are also included in the calculation process. During the calculation process, missing values are also processed, and inappropriate response strategies in similar case sets are adjusted in response to differences between scenarios to generate feasible strategies. Finally, a case study of a city shows that this method can extend the CBR approach to ease of doing business strategy response.
... • AIFB's Semantic Web for Research Communities (SWRC) ontology [67] Moreover, MatOnto includes EXPO [65], a taxonomy of scientific experiments, which was extended with the ABC Metadata Ontology [44] to include the concepts of events and processes. ...
Thesis
Full-text available
Industry 4.0 describes a vision of products controlling their own manufacturing process to achieve shortened development periods and higher customization of products for buyers. The extensibility of knowledge graphs complements the dynamic nature of Industry 4.0. Ontologies define the semantic and validation rules for such knowledge graphs. To achieve integration between various information systems with Industry 4.0 within and beyond a corporation, widely accepted ontologies are needed. This thesis gives an overview of available ontologies for Industry 4.0. Upper ontologies cover generic concepts of Industry 4.0 allowing for fluent integration into a cognitive architecture and must not be specific to a particular industry or task. Although we could not find a widely accepted standard ontology, we found some promising ontologies publicly available such as the COMPOSITION framework, which emerged from the older ontologies MASON, MSDL, and GoodRelations. We discuss these upper ontologies as well as some promising approaches not (yet) publicly available. Moreover, we show some practical examples of applications using the discussed ontologies. To complement the upper ontologies, we give an overview of useful task-specific ontologies, filling some gaps in the upper ontologies. Furthermore, this thesis compares various engineering models to build domain-specific ontologies.
... Event Ontology [18], Linking Open Descriptions of Events (LODE [43]), the F-Model [1]. More general models for semantic data organization are CIDOC-CRM [14], the ABC Ontology [23], and the Europeana Data Model [30]. ...
Article
Full-text available
In this article, the Mingei Online Platform is presented as an authoring platform for the representation of social and historic context encompassing a focal topic of interest. The proposed representation is employed in the contextualised presentation of a given topic, through documented narratives that support its presentation to diverse audiences. Using the obtained representation, the documentation and digital preservation of social and historical dimensions of Cultural Heritage are demonstrated. The implementation follows the Human-Centred Design approach and has been conducted under an iterative design and evaluation approach involving both usability and domain experts.
Article
In recent years, safety accidents in university laboratories have occurred frequently. Not only do the accidents result in property damage, but also in injuries. Real-time environmental monitoring of the laboratory through IoT enables early detection of potential safety risks such as high temperatures, high humidity and gas leaks, and timely action to reduce the likelihood of accidents. To ensure laboratory safety, in the paper, an emergency treatment mechanism for laboratory safety accidents was proposed based on IoT and context perception. The mechanism uses sensors to collect environmental information and fill a feature characterization architecture for unified safety management. Subsequently, the meta-rule algorithm is used to discover services in the prior knowledge model to form a workflow engine, so as to drive the security business management. Additionally, based on the standard measurement model, we normalize the fuzzy uncertainty measurement model with different granularities and define the fuzzy uncertainty of different emergency decision-making knowledge. Based on this, a knowledge fusion method for emergency decision-making under different fuzzy uncertainties is proposed, which improves laboratory safety emergency response performance based on situational awareness. The implementation of the proposed mechanism in a chemical laboratory demonstrates its efficacy in optimizing operational processes and discovering operational flow through multi-dimensional information analysis. This capability significantly aids safety administrators in their daily laboratory safety management.
Chapter
Recent developments in data analysis and machine learning support novel data-driven operations. Event data provide social and environmental context, thus, such data may become essential for the workflow of data analytic pipelines. In this paper, we introduce our Business Event Exchange Ontology (BEEO), based on Schema.org that enables data integration and analytics for event data. BEEO is available under Apache 2.0 license on GitHub, and is seeing adoption among both its creator companies and other product and service companies. We present and discuss the ontology development drivers and process, its structure, and its usage in different real use cases.