Knowledge Representation in the Semantic Web Presented by Huiyong Xiao ([email protected]) Wei Zhang ([email protected])
Outline n Traditional knowledge representation n Current world wide web n Semantic web architecture n Ontology n Protégé-2000 as Ontology Editor (Demo) n Ontology representation language: RDF n Protégé-2000 as RDF(S) Editor (Demo) n Extensions of RDF(S)
1 What is Ontology? n Ontology .... philosophical discipline, branch of philosophy that deals with the nature and the organization of reality n Science of Being n Tries to answer the questions: n What is being? n What are the features common to all beings?
What are ontologies in computer science? n An ontology is an explicit specification of a conceptualization [Gruber, 93] n An ontology is a shared understanding of some domain of interest. [Uschold, Gruninger, 96] n There are many definitions. In general, an ontology (in our sense) is n a formal specification => executable n of a conceptualization of a domain => community n of some part of world that is of interest => application
2 Why Ontology? n Lack of a shared understanding leads to poor communication n People, organizations and software systems must communicate between and among themselves n Disparate modeling paradigms, languages and software tools limit n Interoperability n Knowledge sharing & reuse
Application areas n Ontologies have become a popular research topic in various communities
Knowledge engineering Information retrieval (e.g., yahoo!)
Natural language Digital libraries processing WWW applications (Semantic Web) Knowledge management ontology Electronic commerce Information integration (e.g., Amazon.com) Multi-agent systems
3 A concrete ontology example
Protégé-2000 as Ontology Editor n Protégé-2000 is an integrated software tool used by system developers and domain experts to develop knowledge- based systems n Applications developed with Protégé-2000 are used in problem-solving and decision-making in a particular domain n A uniform GUI (graphical user interface) whose top-level consists of overlapping tabs for compact presentation of the parts and for convenient co-editing between them.
4 Protégé-2000 as Ontology Editor
n This "tabbed" top-level design permits an integration of n the modeling of an ontology of classes describing a particular subject, n the creation of a knowledge-acquisition tool for collecting knowledge, n the entering of specific instances of data and creation of a knowledge base, and n the execution of applications. n A Protege knowledge base is a frame-based knowlege base. People generally don't extract rules from a Protege knowledge base. To do rule-based programming using information stored in a Protege knowledge base, you can tabs such as JessTab and Algernon. n (Demo)
Web knowledge representation Namespaces DTDs CSS XSLT DAML Stylesheets Ontobroker Agents Transformations
HornML XQL Rules XML RuleML Queries XQuery
SHOE XML-QL Frames RDF[S] Acquisition
TopicMaps Protégé n XML- & RDF-based markup languages provide a 'universal' storage/interchange format for such Web-distributed knowledge representation
5 Ontology representation language n Various kinds of (formal) languages are used for representing ontologies n LOOM, CyCL, F-Logic, Conceptual Graphs, Ontolingua, and KIF etc. n Nowadays, there are more languages for expressing ontologies n RDF-Schema: Vocabualry for RDF. n UML: Unified Modeling Language n OIL: Ontology Interchange and Inference
Web Languages for knowledge capturing n Human knowledge is (partially) captured on the Web as informal texts, semiformal documents, and structured metadata n Each kind of knowledge has its (preferred) markup language
Knowledge: Informal Semiformal Metadata Language: HTML XML RDF
6 Address example
External Presentation: HTML Markup:
Mary Stewart Mary Stewart 911 S. Miller ST.
Chicago 911 S. Miller ST.
Chicago
Document Type Definition (DTD): XML Markup:
Web Languages for knowledge capturing
n XML (Extensible Markup Language): Semiformal documents range between non-formatted texts and fully formatted databases n RDF (Resource Description Framework): Structured metadata describe arbitrary heterogeneous Web pages/objects in a homogeneous manner n Machines (e.g. search engines) can analyze XML or RDF markups better than full HTML.
7 XML n XML offers new general possibilities, from which AI knowledge representation (KR) can profit: n Definition of self-describing data in worldwide standardized, non-proprietary format. n Structured data and knowledge exchange for enterprises in various industries. n Integration of information from different sources (into uniform documents).
XML n Key idea: Separate structure from presentation n XML DTDs or Schemas define document structure n XSL (Extensible Stylesheet Language) specifies the document presentation n Replace HTML with two things
n A domain specific markup language (defined in XML)
n A map from that markup language to HTML (defined using XSLT)
8 RDF –Why XML is Not Enough? n Main advantage of using XML is reusing the parser and document validation n Many different possibilities to encode a domain of discourse (The same semantic may have different structures) n Leads to difficulties when understanding of foreign documents is required ==> Next step: separate semantic from structure
Creator example
“The Creator of the Resource “http://www.w3.org/Home/Lassila” is Ora Lassila
Creator http://www.w3.org/Home/Lassila Ora Lassila
Endless encoding possibilities in XML:
9 Introduction to RDF n RDF beyonds Machine readable to Machine understandable n RDF consists of two parts
n RDF Model (a set of triples)
n RDF Syntax (different XML serialization syntaxes) n RDF Schema defines the vocabularies for RDF.
RDF Data Model n Resources n A resource is a thing you talk about (can reference) n Resources have URI’s n RDF definitions are themselves Resources n Properties n slots, define relationships to other resources or atomic values n Statements n “Resource has Property with Value” n (Values can be resources or atomic XML data) n Similar to Frame Systems
10 An example n Statement n “Ora Lassila is the creator of the resource http://www.w3.org/Home/Lassila” n Structure n Resource (subject) http://www.w3.org/Home/Lassila n Property (predicate) http://www.schema.org/#Creator n Value (object) "Ora Lassila” n Directed graph
http://www.w3.org/Home/Lassila s:Creator Ora Lassila
RDF Syntax
n Data model does not enforce particular syntax n Specification suggests many different syntaxes based on XML n General form: Starts an RDF-Description Subject (OID)
Properties Resource (possibly another RDF-description)
11 RDF Schema (RDFS) n RDF just defines the data model n Need for definition of vocabularies for the data model - an Ontology Language! n RDF schemas are Web resources (and have URIs) and can be described using RDF
Most Important Modeling Primitives n Core Classes n Root-Class rdfs:Resource n MetaClass rdfs:Class n Literals rdfs:Literal n rdfs:subclassOf - property n Inherited from RDF: properties (rdf:Property) n rdfs:domain & rdfs:range n rdfs:label, rdfs:comment, etc. n Inherited from RDF: InstanceOf (rdf:type)
12 Example
Protégé-2000 as XML Editor n (Demo)
13 Protégé-2000 as RDF(S) Editor n (Demo)
Extensions of RDF(S) – DAML+OIL n Ontology Language DAML+OIL: DARPA Agent Markup Language Program. (http://www.daml.org) n Extension of RDF Schema n Class Expressions (Intersection, Union, Complement) n E.g., daml:intersectionOf, daml:complementOf n XML Schema Datatypes n Enumerations n Property Restrictions n Cardinality Constraints n Value Restrictions
14 Resources and References n Web-Ontology (WebOnt) Working Group n http://www.w3.org/2001/sw/WebOnt/ n Resource Description Framework (RDF) n http://www.w3.org/RDF n RDF-Editor: Protégé n http://www-smi.stanford.edu/projects/protégé n RDF-Parser and APIs/Query Engines n http://www-db.stanford.edu/~melnik/rdf n http://www.aifb.uni-karlsruhe.de/~sde/rdf n General Information n RDF Interest Mailing list: [email protected] Archive: http//lists.w3.org/Archives/Public/www-rdf-interest n SemanticWeb.org
Resources and References n Gruber, T. (1993). A translation Approach to Portable Ontology Specifications. Knowledge Acquisition. Vol5. 1993. n Uschold, M.; Grüninger, M. (1996) ONTOLOGIES: Principles, Methods and Applications. Knowledge Engineering Review. Vol. 11; N. 2.
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