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Semantic Data Integration in Humanities. Mark Greengrass (University of Sheffield) Oscar Corcho (University of Manchester). A+H e-Science Lecture Edinburgh, June 18th 2007. Contents. Introduction to ontologies Definitions Ontology development Ontologies and the Semantic Web

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Slide1 l.jpg

Semantic Data Integration in Humanities

Mark Greengrass (University of Sheffield)

Oscar Corcho (University of Manchester)

A+H e-Science Lecture

Edinburgh, June 18th 2007


Contents l.jpg
Contents

  • Introduction to ontologies

    • Definitions

    • Ontology development

    • Ontologies and the Semantic Web

  • Ontologies for humanities

    • TGN, ULAN, HASSET, CULTOS, VICODI

  • Projects using ontologies for humanities

    • Historillo

    • Cultural tour (Residencia de Estudiantes)

  • Semantic data/information integration approaches

  • Open issues and discussion

Semantic Data Integration for Humanities


Reuse and sharing l.jpg
Reuse and Sharing

Areas:

  • Software

  • Knowledge

  • Communications

  • Interfaces

  • ---

Reuse means to build new applications

assembling components already built

Sharing is when different

applications use the some resources

Advantages:

  • Less money

  • Less time

  • Less resources

Semantic Data Integration for Humanities


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The Knowledge Sharing Initiative

  • Building Knowledge Based Systems usually entails constructing new knowledge bases from scratch.

  • It could instead be done by:

    • Assembling reusable components.

    • System developers would then only need to worry aboutcreating the specialized knowledge and reasonersnew to the specific task of their systems.

    • New systems wouldinteroperate with existing systems.

  • Declarative knowledge, problem-solving techniques, and reasoning services could all be shared between systems.

Neches, R.; Fikes, R.; Finin, T.; Gruber, T.; Patil, R.; Senator, T.; Swartout, W.R. Enabling Technology for Knowledge Sharing.

AI Magazine. Winter 1991. 36-56.

Semantic Data Integration for Humanities


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Reusable Knowledge Components

Interaction Problem

Representing Knowledge for the purpose of solving some problem

is strongly affected by the nature of the problem

and the inference strategy to be applied to the problem

Ontologies

Problem Solving Methods

Describe domain knowledge in a generic way

and provide agreed understanding of a domain

Describe the reasoning process of a KBS in

an implementation and domain-independent manner

Bylander Chandrasekaran, B. Generic Tasks in knowledge-based reasoning.: the right level of abstraction for knowledge acquisition.

In B.R. Gaines and J. H. Boose, EDs Knowledge Acquisition for Knowledge Based systems, 65-77, London: Academic Press 1988.

Semantic Data Integration for Humanities


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Definitions of Ontologies

1. “An ontology defines the basic terms and relations comprising the vocabulary of a topic area, as well as the rules for combining terms and relations to define extensions to the vocabulary”

Neches R, Fikes RE, Finin T, Gruber TR, Senator T, Swartout WR (1991) Enabling technology for knowledge sharing. AI Magazine 12(3):36–56

2. “An ontology is an explicit specification of a conceptualization”

Gruber TR (1993a) A translation approach to portable ontology specification. Knowledge Acquisition 5(2):199–220

Studer R, Benjamins VR, Fensel D (1998) Knowledge Engineering: Principles and Methods.

IEEE Transactions on Data and Knowledge Engineering 25(1-2):161–197

3. “An ontology is a formal, explicit specification of a shared conceptualization”

Guarino N, Giaretta P (1995) Ontologies and Knowledge Bases: Towards a Terminological Clarification. In: Mars N (ed)

Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing (KBKS’95). University of Twente,

Enschede, The Netherlands. IOS Press, Amsterdam, The Netherlands, pp 25–32

4. “A logical theory which gives on explicit, partial account of a conceptualization”

Guarino N (1998) Formal Ontology in Information Systems. In: Guarino N (ed) 1st International Conference on

Formal Ontology in Information Systems (FOIS’98). Trento, Italy. IOS Press, Amsterdam, pp 3–15

5. “A set of logical axioms designed to account for the intended meaning of a vocabulary”

Semantic Data Integration for Humanities


Example of domain ontology prot g l.jpg
Example of Domain Ontology. Protégé

Semantic Data Integration for Humanities


Example of a domain ontology webode l.jpg
Example of a domain ontology. WebODE

Semantic Data Integration for Humanities


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Components of an Ontology

Concepts are organized in taxonomies

Relations

R: C1 x C2 x ... x Cn-1 x Cn

Subclass-of: Concept 1 x Concept2

Connected to: Component1 x Component2

Functions

F: C1 x C2 x ... x Cn-1 --> Cn

Mother-of: Person --> Women

Price of a used car: Model x Year x Kilometers --> Price

Instances

Elements

Gruber, T. A translation Approach to portable

ontology specifications. Knowledge Acquisition.

Vol. 5. 1993. 199-220.

Axioms

Sentences which are always true

Semantic Data Integration for Humanities


Vocabulary l.jpg
Vocabulary

  • “Class”  “Concept”  “Category”  “Type”

  • “Instance”  “Individual”

  • “Entity”  “object”, Class or individual

  • “Property”  “Slot”  “Relation”  “Relationtype”  “Attribute”  Semantic link type”  “Role”

    • but be careful about “role”

      • Means “property” in description logics

      • Means “role played” in most ontologies

        • E.g. “doctorrole”, “student role” …

Semantic Data Integration for Humanities


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Using Frames and First Order Logic for Modeling Ontologies

(define-functionPays (?room ?discount) :-> ?finalPrice

"Price of the room after applying the discount"

:def (and (Room ?room) (Number ?discount)

(Number ?finalPrice)

(Price ?room ?price))

:lambda-body

(- ?price (/ (* ?price ?discount) 100)))

(define-classTravel (?travel)

"A journey from place to place"

:axiom-def

(and (Superclass-Of Travel Flight)

(Template-Facet-Value Cardinality

arrivalDate Travel 1)

(Template-Facet-Value Cardinality

departureDate Travel 1)

(Template-Facet-Value Maximum-Cardinality

singleFare Travel 1))

:def

(and (arrivalDate ?travel Date)

(departureDate ?travel Date)

(singleFare ?travel Number)

(companyName ?travel String)))

(define-relation connects (?edge ?source ?target)

"This relation links a source and a target by an edge. The source and destination are considered as spatial points. The relation has the following properties: symmetry and irreflexivity."

:def (and (SpatialPoint ?source)

(SpatialPoint ?target)

(Edge ?edge))

:axiom-def

((=> (connects ?edge ?source ?target)

(connects ?edge ?target ?source)) ;symmetry

(=> (connects ?edge ?source ?target)

(not (or (part-of ?source ?target) ;irreflexivity

(part-of ?target ?source))))))

(define-instanceAA7462-Feb-08-2002 (AA7462)

:def ((singleFare AA7462-Feb-08-2002 300)

(departureDate AA7462-Feb-08-2002 Feb8-2002)

(arrivalPlace AA7462-Feb-08-2002 Seattle)))

Semantic Data Integration for Humanities


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Using Description Logics for Modeling Ontologies

(defrelationPays

:is

(:function (?room ?Discount)

(- (Price ?room) (/(*(Price ?room) ?Discount) 100)))

:domains (Room Number)

:range Number)

(defconceptTravel

"A journey from place to place"

:is-primitive

(:and

(:all arrivalDate Date)(:exactly 1 arrivalDate)

(:all departureDate Date)(:exactly 1 departureDate)

(:all companyName String)

(:all singleFare Number)(:at-most singleFare 1)))

(defrelationconnects

"A road connects two different cities"

:arity 3

:domains (Location Location)

:range RoadSection

:predicate

((?city1 ?city2 ?road)

(:not (part-of ?city1 ?city2))

(:not (part-of ?city2 ?city1))

(:or (:and (start ?road ?city1)(end ?road ?city2))

(:and (start ?road ?city2)(end ?road ?city1)))))

(tellm (AA7462 AA7462-08-Feb-2002)

(singleFare AA7462-08-Feb-2002 300)

(departureDate AA7462-08-Feb-2002 Feb8-2002)

(arrivalPlace AA7462-08-Feb-2002 Seattle))

Semantic Data Integration for Humanities


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Using UML for Modeling Ontologies

Semantic Data Integration for Humanities


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Using the Entity Relationship Model for Modeling Ontologies

Semantic Data Integration for Humanities


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A semantic continuum

Pump: “a device for moving a gas or liquid from one place or container to another”

(pump has

(superclasses (…))

Semantics hardwired;

used at runtime

Semantics processed and used at runtime

Text descriptions

Informal

(explicit)

Formal

(for humans)

Formal

(for machines)

Shared human consensus

Implicit

 Further to the right 

  • Less ambiguity

  • Better inter-operation

  • More robust – less hardwiring

  • More difficult

Uschold M

Semantic Data Integration for Humanities


Types of vocabularies formality l.jpg
Types of vocabularies. Formality

Add your vocabularies here 

Lassila O, McGuiness D. The Role of Frame-Based Representation on the Semantic Web.

Technical Report. Knowledge Systems Laboratory. Stanford University. KSL-01-02. 2001.

Semantic Data Integration for Humanities


Contents17 l.jpg
Contents

  • Introduction to ontologies

    • Definitions

    • Ontology development

    • Ontologies and the Semantic Web

  • Ontologies for humanities

    • TGN, ULAN, HASSET, CULTOS, VICODI

  • Projects using ontologies for humanities

    • Historillo

    • Cultural tour (Residencia de Estudiantes)

  • Semantic data/information integration approaches

  • Open issues and discussion

Semantic Data Integration for Humanities


Principles for the design of ontologies i l.jpg
Principles for the Design of Ontologies (I)

Clarity:

To communicate the intended meaning of defined terms

Coherence:

To sanction inferences that are consistent with definitions

Extendibility:

To anticipate the use of the shared vocabulary

Minimal Encoding Bias:

To be independent of the symbolic level

Minimal Ontological Commitments:

To make as few claims as possible about the world

  • Gruber, T.; Towards Principles for the Design of Ontologies.

  • KSL-93-04. Knowledge Systems Laboratory.

  • Stanford University. 1993

Semantic Data Integration for Humanities


Principles for the design of ontologies ii l.jpg
Principles for the Design of Ontologies (II)

  • The representation of disjoint and exhaustive knowledge. If the set of subclasses of a concept are disjoint, we can define a disjoint decomposition. The decomposition is exhaustive if it defines the superconcept completely.

  • To improve the understandability and reusability of the ontology, we should implement the ontology trying to minimize the syntactic distance between sibling concepts.

  • The standardization of names. To ease the understanding of the ontology the same naming conventions should be used to name related terms.

Arpírez JC, Gómez-Pérez A, Lozano A, Pinto HS (1998) (ONTO)2Agent: An ontology-based WWW broker to select ontologies.

In: Gómez-Pérez A, Benjamins RV (eds) ECAI’98 Workshop on Applications of Ontologies and Problem-Solving Methods.

Brighton, United Kingdom, pp 16–24

Semantic Data Integration for Humanities


Types of ontologies l.jpg
Types of Ontologies

Issue of the

Conceptualization

Content Ontologies

Domain O.

Representation O.

Scalpel, scanner

anesthetize, give birth

  • Conceptualization

  • of KR formalisms

Task O.

goal, schedule

to assign, to classify

Generic O.

  • Reusable across D.

General/Common O.

Domain O.

Things, Events, Time, Space

Causality, Behavior, Function

  • Reusable

Application O.

Mizoguchi, R. Vanwelkenhuysen, J.; Ikeda, M.

Task Ontology for Reuse of Problem Solving Knowledge.

Towards Very Large Knowledge Bases:

Knowledge Building & Knowledge Sharing.

IOS Press. 1995. 46-59.

Van Heist, G.; Schreiber, T.; Wielinga, B.

Using Explicit Ontologies in KBS

International Journal of Human-Computer Studies.

Vol. 46. (2/3). 183-292. 1997

  • Non reusable

  • Usable

Semantic Data Integration for Humanities


Libraries of ontologies i l.jpg
Libraries of Ontologies (I)

Reusability

Usability

-

+

Application

Domain O. : heart-diseases

Application Domain

Task O.:surgery heart

Domain Task O.:plan-surgery

Domain O.:body

Generic Task O.:plan

Generic Domain O.: components

General/Common Ontology: Time, Units, Space, ...

-

+

Representation Ontology: Frame-Ontology, OWL KR Ontology

Semantic Data Integration for Humanities


Relationship between ontologies in the library l.jpg
Relationship between Ontologies in the Library

Environmental Pollutants

Monoatomic-Ions

Poliatomic-ions

Chemical-Elements

Standard-Units

Standard-Dimensions

Physical-Quatities

Kif-Numbers

Frame-Ontology

Semantic Data Integration for Humanities


How to reuse ontologies from a library l.jpg
How to Reuse Ontologies from a Library

Top level ontologies

Knowledge representation ontologies

REPRESENTATION ENTITY

CONCEPT

Subclass of

ENTITY

Subclass of

Subclass of

Instance of

PHYSICAL SUBSTANTIAL

ATTRIBUTE

Subclass of

Instance of

Instance of

ELEMENT

Oxidation-State (0, N)

Atomic-Weight (1, 1)

Atomic-Number (1, 1)

Domain ontologies

Subclass of

Subclass of

Partition

REACTIVELESS

REACTIVENESS

Semantic Data Integration for Humanities


Slide24 l.jpg

Seamark Demo:

ID new drug candidates for BRKCB-1

GO2Keyword.rdf

Keywords.rdf

ProbeSet.rdf

Keyword

GO2OMIM.rdf

GO2UniProt.rdf

Protein

Gene

Probe

MIM Id

OMIM.rdf

IntAct.rdf

GO.rdf

GO2Enzyme.rdf

UniProt.rdf

Enzyme

Organism

Citation

Compound

Taxonomy.rdf

Enzymes.rdf

PubMed.xml

KEGG.rdf

Pathway

Courtesy Joanne Luciano

Semantic Data Integration for Humanities


Libraries of ontologies ii l.jpg
Libraries of Ontologies (II)

OWL ontologies

Protégé ontology library http://protege.stanford.edu/download/ontologies.html

OWL ontology library http://www.daml.org/ontologies/

SWOOGLE http://swoogle.umbc.edu/

Oyster http://oyster.ontoware.org/oyster/oyster.html

Other ontologies

SHOE ontology library http://www.cs.umd.edu/projects/plus/SHOE/onts/index.html

Ontolingua ontology library http://ontolingua.stanford.edu/

WebOnto ontology library http://webonto.open.ac.uk

WebODE ontology library http://webode.dia.fi.upm.es/

(KA)2 ontology library http://ka2portal.aifb.uni-karlsruhe.de/

Semantic Data Integration for Humanities


Ontology development process i l.jpg
Ontology development process (I)

Semantic Data Integration for Humanities


Ontology development process ii l.jpg
Ontology development process (II)

Semantic Data Integration for Humanities


Ontology development process iii l.jpg
Ontology development process (III)

Semantic Data Integration for Humanities


Ontological engin e ering l.jpg
Ontological Engineering

Applications

Build Ontologies

Methodologies and methods

Tools

Reasoners

Languages

Semantic Data Integration for Humanities


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Contents

  • Introduction to ontologies

    • Definitions

    • Ontology development

    • Ontologies and the Semantic Web

  • Ontologies for humanities

    • TGN, ULAN, HASSET, CULTOS, VICODI

  • Projects using ontologies for humanities

    • Historillo

    • Cultural tour (Residencia de Estudiantes)

  • Semantic data/information integration approaches

  • Open issues and discussion

Semantic Data Integration for Humanities


What is the semantic web l.jpg
What is the Semantic Web?

“The Semantic Web is an extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. It is based on the idea of having data on the Web defined and linked such that it can be used for more effective discovery, automation, integration, and reuse across various applications.”

Hendler, J., Berners-Lee, T., and Miller, E.

Integrating Applications on the Semantic Web, 2002,

http://www.w3.org/2002/07/swint.html

Ontologies

Annotation

Semantic Data Integration for Humanities


Semantic web languages l.jpg
Semantic Web Languages

URI, HTML, HTTP

WWW

Semantic Web

RDF, RDFS, OWL

Dynamic

Static

Semantic richness

Semantic Data Integration for Humanities


Resource description framework l.jpg
Resource Description Framework

[instanceOf]

urn:data1

SwissProt_seq

[similar_sequence_to]

[input]

urn:hit1…

[performsTask]

[instanceOf]

urn:BlastNInvocation3

urn:hit2….

[contains]

[output]

Find similar sequence

urn:hit50…..

urn:data2

Sequence_hit

urn:data12

[input]

[hasHits]

[instanceOf]

urn:compareinvocation3

Blast_report

[directlyDerivedFrom]

[distantlyDerivedFrom]

[instanceOf]

[output]

urn:hit5…

urn:data:3

urn:hit8….

[contains]

Data generated by services/workflows

[output]

urn:hit10…..

[output]

urn:data:f1

urn:invocation5

[ ]

Properties

[type]

[hasName]

urn:data:f2

Concepts

[type]

[hasName]

Services

Missed sequence

DatumCollection

New sequence

LSDatum

literals

Semantic Data Integration for Humanities


The 3 faces of the semantic web l.jpg
The 3 faces of the Semantic Web

SWRL

Expressive models

Inference

Model fusion

OWL

Controlled vocabularies

RDF(S)

Data fusion

Integration

Integration

Extensible metadata schemas that you don’t have to nail down

RDF

Annotation

XML

  • Semantic Annotation Web

  • Semantic Data (Integration) Web

  • Semantic Knowledge (Reasoning) Web

Semantic Data Integration for Humanities


Slide35 l.jpg

Annotation assert facts using terms (metadata in RDF)

Represent terms and their relationships (ontology in RDFS/OWL)

News

Videocast

Grant Application

Research

Events

Organisation

Gene Database

Semantic Data Integration for Humanities


Ontologies and metadata l.jpg
Ontologies and Metadata

Instance of

Instance of

Ontologies

Belongs_To

Organization

Person

xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#'

xmlns:NS0='http://www.esperonto.net/semanticportal/RDFS/Person_Ontology#' xmlns:NS1='http://www.esperonto.net/semanticportal/RDFS/Organization_Ontology#'

Has_contact_Person

Subclass of

Subclass of

Associate Prof.

Partner

<rdf:Description rdf:about='AsunciónGómez-Pérez'>

<rdf:type rdf:resource=‘Associate Prof'/>

<NS0:Full_Name>A. GomezPerez</NS0:Full_Name>

<NS0:Belongs_To>UPM</NS0: Belongs_To >

<NS0:e-mail>[email protected]</NS0:e-mail>

<rdf:Description rdf:about='UPM'>

<rdf:type rdf:resource='Partner'/>

<NS1:Acronym>UPM</NS1:Acronym>

<NS1:Has_Contact_Person>Asunción Gómez-Pérez

</NS1:Has_Contact_Person >

Annotation

(RDF)

Web Page

URL

http://www.esperonto.net

http://www.esperonto.net

Semantic Data Integration for Humanities


Metadata annotation l.jpg
Metadata annotation

Ontology-based document annotation: trends and open research problems. Corcho, O. International Journal of Metadata, Semantics and Ontologies 1(1):47-57. 2006

  • Different types of annotation depending on the type of vocabulary used

Based on Dublin Core

The contributor and creator is the flight booking service “www.flightbookings.com”.

The date would be January 1st, 2003, in case that the HTML page has been generated on that specific date.

The description would be something like “flight details for a travel between Madrid and Seattle via Chicago on February 8th, 2004”.

The document format is “HTML”.

The document language is “en”, which stands for English

Based on thesauri

Madrid is a reference to the term with ID 7010413 in the thesaurus, which refers to the city of Madrid in Spain.

Spain is a reference to the term with ID 1000095, which refers to the kingdom of Spain in Europe.

Chicago is a reference to the term with ID 7013596, which refers to the city of Chicago in Illinois, US.

United States of America is a reference to the term “United States” with ID 7012149, which refers to the US nation.

Seattle is a reference to the term with ID 7014494, which refers to the city of Seattle in Washington, US.

Based on ontologies

Concept instances relate a part of the document to one or several concepts in an ontology. For example, “Flight details” may represent an instance of the concept Flight, and can be named as AA7615_Feb08_2003, although concept instances do not necessarily have a name.

Attribute values relate a concept instance with part of the document, which is the value of one of its attributes. For example, “American Airlines” can be the value of the attribute companyName.

Relation instances that relate two concept instances by some domain-specific relation. For example, the flight AA7615_Feb08_2003 and the location Madrid can be connected by the relation departurePlace

Semantic Data Integration for Humanities


Slide38 l.jpg

Integration use a uniform common model in RDF

Connecting through shared terms and shared instances

Preserving context and provenance

Agents

Smart portals

Data mining

Social networking

Smart search

Knowledge Discovery

Information Integration and aggregation

D2R

R2O

BIRN Mediator

OBSERVER

CARNOT

TSIMMIS

Semantic Data Integration for Humanities


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Data Integration in the Semantic Web

  • Flexible and extensible self describing schemas that don’t have to be nailed down

    • “Lets describe my data set, or the output format of my tool, that changes frequently”

  • Open world

    • “I need to comment on that historial document”

    • “That fact is now incorrect because …”

  • Data fusion across different data models

    • cross linked by shared instances and shared concepts

  • Global naming scheme

    • LSID: Life Science Identifiers

    • URIs: Uniform Resource Identifiers

Semantic Data Integration for Humanities


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Inference

Logic-based classification, validity checking with OWL

Rules using SWRL (Semantic Web Rule Language)

RDF queries Just making connections because so much stuff is connected!

Rearrangement of a DNA sequence homologous to a cell-virus junction fragment in several Moloney murine leukemia virus-induced rat thymomas

8q24

PVT1

Semantic Data Integration for Humanities

James Hendler Science and the Semantic Web Science 299: 520-521, 2003


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Conclusion. An Ontology should be just the Beginning

Databases

Declare

structure

Ontologies

Knowledge

bases

The Semantic Web

Provide

domain

description

Software agents

Problem-solving methods

Domain-independent

applications

Semantic Data Integration for Humanities

Source: Alan Rector


Main references l.jpg
Main References

Gómez-Pérez, A.; Fernández-López, M.; Corcho, O. Ontological Engineering. Springer Verlag. 2003

http://www.ontoweb.org

http://knowledgeweb.semanticweb.org

  • Deliverables

  • D1.1

  • D1.2

  • D1.3

  • D1.4

  • D1.5

Research deliverables

Industry deliverables

Neches, R.; Fikes, R.; Finin, T.; Gruber, T.; Patil, R.; Senator, T.; Swartout, W.R. Enabling Technology for Knowledge Sharing.

AI Magazine. Winter 1991. 36-56.

Gruber, T. A translation Approach to portable ontology specifications. Knowledge Acquisition. Vol. 5. 1993. 199-220.

Uschold, M.; Grüninger, M. ONTOLOGIES: Principles, Methods and Applications. Knowledge Engineering Review. Vol. 11; N. 2; June 1996.

Semantic Data Integration for Humanities


Acknowledgements l.jpg
Acknowledgements

  • Asunción Gómez-Pérez and Mariano Fernández-López

    • Most of the slides have been done jointly with them

  • Carole Goble, Pinar Alper

    • Ontologies and the Semantic Web

Semantic Data Integration for Humanities


Contents44 l.jpg
Contents

  • Introduction to ontologies

    • Definitions

    • Ontology development

    • Ontologies and the Semantic Web

  • Ontologies for humanities

    • TGN, ULAN, HASSET, CULTOS, VICODI

  • Projects using ontologies for humanities

    • Historillo

    • Cultural tour (Residencia de Estudiantes)

  • Semantic data/information integration approaches

  • Open issues and discussion

Semantic Data Integration for Humanities


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