The Semantic Web and Ontology

The Semantic Web WWW: –syntactic transmission of information –only processible by human – no semantic conservation of the information –can not be processed by machine (e.g.. Machine does not know whether a branch means a part of a tree or a part of an organization)

The Semantic Web Information transmitted conserves semantics information can be processed by machines enable intelligent services: search agents, information brokers, and information filters

What is information Factual data Meta Data about data plans and activities beliefs and doubts Reasoning

What is an Ontology? The conceptualization of a domain Represented as a set of terms and their relationships An example: a car is a type of automobile it has engine, transmission, steering wheel, etc as parts its energy source can be gasoline or electricity and so on

Term: a reference to real-world and abstract objects Relationship: a named and typed set of links between objects Reference: a label that names objects Real-world object: an entity instance with a physical manifestation Abstract object: a concept which refers to other objects

An Example Ontology class-def animal class-def plant subclass-of NOT animal class-def defined carnivore subclass-of animal slot-constraint eats value-type animal class-def lion subclass-of animal subclass-of defined carnivore slot-constraint eats value-type herbivore

Applications of Ontologies Define Terms used in System Construction to Enable Correctness in Understanding designers, implementers, users, maintainers designers = implementers = users = maintainers Define Higher-level Abstractions Needed to Communicate in Large Contexts managers, decision-makers, systems in other domains Share the Cost of Knowledge Acquisition & Maintenance reuse encoded knowledge, remain up-to-date as domains change

Why ontology is crucial for the Semantic Web An existing technique to model the real world and information Describe semantics Understandable by different users Ontology representation languages needs to be encoded into a machine-processable way

Heavy-weight Ontology is needed for the Semantic Web Light-weight Ontology –concepts, atomic types –is-a hierarchy among concepts –associations between concepts Heavy-weight Ontology –cardinality constraints –taxonomy of relations –reified statements –Axioms / semantic entailments of various tastes expressiveness (DL, propositional, horn, or first order logic, higher order) inferences

XML document = labeled tree course teachertitlestudents namehttp node = label + attr/values + contents DTD: simple grammars to describe legal trees So: why not use XML to represent ontologies?

Limitations of XML for Semantic Markup Multiple possibilities to code an ontology No commitment to domain-specific terms Lacks modeling primitives Requires pre-agreement between all users on a specific DTD

RDF: Resource Description Framework Intended for representation “meta-data”, basis for Web-based ontology-language W3C recommendation –Supported by W3C –basis of $ 80M DAML program –Already embraced by some vendors (e.g.Netscape)

Object --Attribute-> Value tuples Objects are web-resources Value is again an Object: –data-model = graph pers05 ISBN... Author-of pers05 ISBN... MIT ISBN... Publ- by Author-of Publ- by

RDF Schema So, RDF : –(very small) commitment to modeling primitives –but: no commitment to domain vocabulary  RDF Schema Define vocabulary for RDF Organize this vocabulary in a typed hierarchy –Class, SubClassOf, type –Property, subPropertyOf, –domain, range

RDF is Better than XML but Still Limited Provides more semantic interoperability the object-attribute structure is natural semantic units easier mapping between two RDF descriptions XML independent Lacks modeling primitives Lacks semantic support (Description Logic) provides syntax

Ontology Inference Layer (OIL) Sponsored by European Union IST programme for Information Society Technologies Frame-based system + Description logic + RDF = OIL

Ontology Inference Layer (OIL)

class-def subclass-of slot-def subslot-of domain range class-def subclass-of slot-def subslot-of domain range class-expressions AND, OR, NOT slot-constraints has-value, value-type cardinality slot-properties trans, symm class-expressions AND, OR, NOT slot-constraints has-value, value-type cardinality slot-properties trans, symm RDF(S) OIL

OIL as RDFS extension <rdf:type rdf:resource=” /#DefinedClass”/>

OIL: currently available tools Definition of language –semantics –XML encoding –RDF encoding Tools: –translators (XSL based) –OntoEdit case-studies –GIS ontology mapping –(KA) 2 ontology –CIA world fact book

DARPA Agent Markup Language (DAML) Sponsored by the Defense Advance Research Project Agency Develop technologies to provide interoperability between agents in semantic manner First stage is DAML, similar to OIL

Conclusion OIL and DAML mark the first effort of using ontology for the Semantic Web Both needs to be further enhanced Tools to use these languages are in urgent need Ontology interoperativity is the next step

Future Directions Tools to build and use ontology using the standard Web ontology language Engineering tools to semantically integrate, migrate, reconciliate and share ontologies Deploy the technology to be used for intelligent services

References Decker et al.(2000) The Semantic Web: the roles of XML and RDF. IEEE Internet Computing 4: Bray et al. Extensible Markup Language (XML) 1.0, World Wide Web Consortium, 1998, current May 2000; Brickley and Guha (2000) Resource Description Framework (RDF) Schima Specification. W3C Candidate Recommendation / / OIL, DAML,