Pellet Pellet Any questions Thank you as long as listening

Pellet Pellet Any questions Thank you as long as listening www.phwiki.com

Pellet Pellet Any questions Thank you as long as listening

Johnson, Brian, Managing Editor has reference to this Academic Journal, PHwiki organized this Journal Ontologies in addition to the Semantic Web Ian Horrocks In as long as mation Management Group School of Computer Science University of Manchester The Semantic Web Today’s Web Distributed hypertext/hypermedia In as long as mation accessed via (keyword based) search in addition to browse Browser tools render in as long as mation as long as human consumption

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What is the Semantic Web Web was “invented” by Tim Berners-Lee (amongst others), a physicist working at CERN His vision of the Web was much more ambitious than the reality of the existing (syntactic) Web: This vision of the Web has become known as the Semantic Web “ a set of connected applications as long as ming a consistent logical web of data ” “ an extension of the current web in which in as long as mation is given well-defined meaning, better enabling computers in addition to people to work in cooperation ” Hard Work using “Syntactic Web” Find images of Peter Patel-Schneider, Frank van Harmelen in addition to Alan Rector Rev. Alan M. Gates, Associate Rector of the Church of the Holy Spirit, Lake Forest, Illinois Impossible () using “Syntactic Web” Complex queries involving background knowledge Find in as long as mation about “animals that use sonar but are neither bats nor dolphins” Locating in as long as mation in data repositories Travel enquiries Prices of goods in addition to services Results of human genome experiments Finding in addition to using “web services” Given a DNA sequence, identify its genes, determine the proteins they can produce, in addition to hence the biological processes they control Delegating complex tasks to web “agents” Book me a holiday next weekend somewhere warm, not too far away, in addition to where they speak either French or English , e.g., Barn Owl

What is the Problem Consider a typical web page: Markup consists of: rendering in as long as mation (e.g., font size in addition to colour) Hyper-links to related content Semantic content is accessible to humans, but not (easily) to computers What is the (Proposed) Solution Add semantic annotations to web resources Rev. Alan M. Gates, Associate Rector of the Church of the Holy Spirit, Lake Forest, Illinois Dr. Alan Rector, Professor of Computer Science, University of Manchester Dr. Alan Rector, Professor of Computer Science, University of Manchester Rev. Alan M. Gates, Associate Rector of the Church of the Holy Spirit, Lake Forest, Illinois Giving Semantics to Annotations External agreement on meaning of annotations Agree on meaning of a set of annotation tags E.g., Dublin Core Limited flexibility in addition to extensibility Limited number of things can be expressed Use Ontologies to specify meaning of annotations Agree on language used to describe meaning Meanings of vocabularies of terms given by ontologies New terms can be as long as med by combining existing ones Meaning (semantics) of such terms is as long as mally specified Can combine/relate terms in multiple ontologies

Ontologies Ontology: Origins in addition to History In Philosophy, fundamental branch of metaphysics Studies “being” or “existence” in addition to their basic categories Aims to find out what entities in addition to types of entities exist Ontology in In as long as mation Science An ontology is an engineering artefact consisting of: A vocabulary used to describe (a particular view of) some domain An explicit specification of the intended meaning of the vocabulary. Often includes classification based in as long as mation Constraints capturing background knowledge about the domain Ideally, an ontology should: Capture a shared underst in addition to ing of a domain of interest Provide a as long as mal in addition to machine manipulable model

Example Ontology (Protégé) Applications of Ontologies e-Science, e.g., Bioin as long as matics Open Biomedical Ontologies Consortium (GO, MGED) Used e.g., as long as “in silico” investigations relating theory in addition to data E.g., relating data on phosphatases to (model of) biological knowledge Applications of Ontologies Medicine Building/maintaining terminologies such as Snomed, NCI & Galen

Applications of Ontologies Organising complex in addition to semi-structured in as long as mation UN-FAO, NASA, Ordnance Survey, General Motors, Lockheed Martin, Applications of Ontologies Military/Government DARPA, NIST, SAIC, Department of Homel in addition to Security, The Semantic Web in addition to so-called Semantic Grid Ontology Languages

Ontology Languages as long as the Web Semantic Web ef as long as t led to development of “resource description” language(s) E.g., RDF, in addition to later RDF Schema (RDFS) RDFS is recognisable as an ontology language Classes in addition to properties Sub/super-classes ( in addition to properties) Range in addition to domain (of properties) But RDFS too weak to describe resources in sufficient detail, e.g.: No existence/cardinality constraints No transitive, inverse or symmetrical properties No localised range in addition to domain constraints And RDF(S) has “higher order flavour” with non-st in addition to ard semantics Difficult to provide reasoning support From RDFS to OWL Two languages developed to address deficiencies & problems of RDFS: OIL: developed by group of (largely) European researchers DAML-ONT: developed by group of (largely) US researchers Ef as long as ts merged to produce DAML+OIL Development carried out by “Joint EU/US Committee on Agent Markup Languages” DAML+OIL submitted to as basis as long as st in addition to ardisation Web-Ontology (WebOnt) Working Group as long as med WebOnt developed OWL language based on DAML+OIL OWL now a W3C recommendation (i.e., a st in addition to ard) OIL, DAML+OIL in addition to OWL based on Description Logics OWL is effectively a “Web-friendly” syntax as long as SHOIN What Are Description Logics A family of logic based Knowledge Representation as long as malisms Descendants of semantic networks in addition to KL-ONE Describe domain in terms of concepts (classes), roles (properties, relationships) in addition to individuals Operators allow as long as composition of complex concepts Names can be given to complex concepts, e.g.: HappyParent ´ Parent u 8hasChild.(Intelligent t Athletic)

Semantics in addition to Reasoning Distinguished by: Formal semantics (typically model theoretic) Decidable fragments of FOL (often contained in C2) Closely related to Propositional Modal & Dynamic Logics, in addition to to Guarded Fragment [Quillian, 1967] Semantics in addition to Reasoning Distinguished by: Formal semantics (typically model theoretic) Decidable fragments of FOL (often contained in C2) Closely related to Propositional Modal & Dynamic Logics, in addition to to Guarded Fragment Provision of inference services Decision procedures as long as key problems (satisfiability, subsumption, etc) Implemented systems (highly optimised) Why Description Logic OWL exploits results of 15+ years of DL research Well defined (model theoretic) semantics

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Why Description Logic OWL exploits results of 15+ years of DL research Well defined (model theoretic) semantics Formal properties well understood (complexity, decidability) [Garey & Johnson. Computers in addition to Intractability: A Guide to the Theory of NP-Completeness. Freeman, 1979.] I can’t find an efficient algorithm, but neither can all these famous people. Why Description Logic OWL exploits results of 15+ years of DL research Well defined (model theoretic) semantics Formal properties well understood (complexity, decidability) Known reasoning algorithms Why Description Logic OWL exploits results of 15+ years of DL research Well defined (model theoretic) semantics Formal properties well understood (complexity, decidability) Known reasoning algorithms Implemented systems (highly optimised) Pellet

Why Description Logic Foundational research was crucial to design of OWL In as long as med Working Group decisions at every stage, e.g.: “Why not extend the language with feature x, which is clearly harmless” “Adding x would lead to undecidability – see proof in [ ]” Why the Strange Names Description Logics are a family of KR as long as malisms Mainly distinguished by available operators Available operators indicated by letters in name, e.g., S : basic DL (ALC) plus transitive roles (e.g., ancestor R+) H : role hierarchy (e.g., hasDaughter v hasChild) O : nominals/singleton classes (e.g., {Italy}) I : inverse roles (e.g., isChildOf ´ hasChild–) N : number restrictions (e.g., >2hasChild, 63hasChild) Basic DL + role hierarchy + nominals + inverse + NR = SHOIN SHOIN is the basis as long as W3C’s OWL Web Ontology Language SHOIN is very expressive, but still decidable (just) Class/Concept Constructors C is a concept (class); P is a role (property); x is an individual name

Resources FaCT++ system (open source) http://owl.man.ac.uk/factplusplus/ Protégé http://protege.stan as long as d.edu/plugins/owl/ W3C Web-Ontology (WebOnt) working group (OWL) http://www.w3.org/2001/sw/WebOnt/ DL H in addition to book, Cambridge University Press http://books.cambridge.org/0521781760.htm DL & KR, Windermere, 30th May – 5th June Any questions Thank you as long as listening

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