Overview • Definition of ontology Ontologies I Fredrik Arvidsson LIBLAB/HCS/IDA Annika Flycht-Eriksson NLPLAB/HCS/IDA • Perspectives on ontologies • Ontologies for the Semantic Web • Design and development of ontologies – methods and tools Ontologies I What is an ontology? What is an ontology? In practice… In theory… • Simple concept hierarchies • Semantic nets Fredrik Arvidsson & Annika Flycht-Eriksson ”An ontology is a formal, explicit specification of a shared conceptualisation.” • Frame systems (Gruber, 1993) • Logical models Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Ontologies I What is an ontology? Types of ontologies In English... From general to specific An ontology provide a shared vocabulary, which can be used to model a domain, that is, the type of objects and/or concepts that exist, and their properties and relations Fredrik Arvidsson & Annika Flycht-Eriksson • Generic • Core • Domain • Task • Application Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson 1 The anatomy of ontologies Perspectives • Terms • Philosophy • Definitions of terms • Library and Information Science • Axioms • Artificial Intelligence • (Inference mechanisms) • Natural Language Processing • The Semantic Web Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Philosophy Library & Information Science (LIS) • Objectives • Objectives • Describe world, classify and categorise • Examples • Organise the bibliographic universe • Model universal & domain knowledge • Usage • Aristotle, …, Sowa • Provide accesspoints to bibliographic entities • Collocation device • Examples • Dublin Core, MARC • LCC, UDC, SAB Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Artificial Intelligence Natural Language Processing • Objectives • Objectives • Model common sense and domain knowledge • Usage • Knowledge representation and reasoning • Examples • Machine Translation, Information Extraction, Q/A • Examples • CYC, ... Ontologies I • Model lexical and domain knowledge • Usage • Wordnet, Generalised Upper Model, Dahlgren Fredrik Arvidsson & Annika Flycht-Eriksson Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson 2 The Semantic Web The Ontology Web Language - OWL • Objectives Goal • Provide semantics for web resources • Usage • Describe resources …and their contents • Go beyond the basic semantics in RDFS Current status: • Examples • • • • • DC, DAML-library, … Ontologies I • Formally describe the semantics of classes and properties used in web documents. Fredrik Arvidsson & Annika Flycht-Eriksson use cases and their requirements on ontologies eight design goals 19 requirements …and some objectives Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Design goals Requirements • Shared ontologies • Ontologies as distinct objects • Ontology evolution • Unambiguous term referencing with URIs • Ontology interoperability • Explicit ontology extension • Inconsistency detection • Commitment to ontologies • Balance of expressivity and scalability • Ontology metadata • Ease of use • Versioning information • XML syntax • Class definition primitives • Internationalisation • Property definition primitives Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Requirements, cont. Requirements, cont. • Datatypes • User-displayable labels • Class and property equivalence • Supporting a character model • Individual equivalence • Supporting a uniqueness of Unicode strings • Local unique names assumption • Attaching information to statements • Classes as instances • Complex data types • Cardinality constraints Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson 3 Design and development Design and development approaches • Design guidelines and principles • Inspirational • Guarino, Gruber,... • Inductive • Methods • Deductive • Methonology, TOVE,... • Synthetic • Tools • Protégé, KAON,... • Collaborative • Libraries • Ontolingua server, DAML-library,... Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson General development methodology Ontology learning • Specify purpose and scope • ML and NLP techniques to facilitate capture and organisation • Capture, define, organise • Extend/Refine existing ontology • Formalise, implement • Integrate existing resources • Free or semi-structured text as source • Evaluate Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Ontologies I Text-To-Onto (OntoEdit) Dublin Core • Import and Reuse Goals • Extract • • • • Lexical entry and concept extraction Hierarchical concept clustering Dictionary parsing Association rules • Provides a semantic vocabulary for describing the “core” information properties of resources (electronic and “real” physical objects) • Proviede enough information to enable intelligent resource discovery systems History • A collaborative effort started in 1995 • Initiated by people from computer science, librarianship, online information services, abstracting and indexing, imaging and geospatial data, museum and archive control. • Prune • Refine Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Fredrik Arvidsson & Annika Flycht-Eriksson Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson 4 Dublin Core - 15 Elements DC - Classes of qualifiers 15 Elements related to a resource: Two broad classes of qualifiers: • Content • Element Refinement • Title, Subject, Description, Type, Source, Relation and Coverage • ”…make the meaning of an element narrower or more specific.” • HTML example from ”www.kb.se”: <META NAME="DC.Date.Modified" CONTENT="1999-06-02"> • Intellectual property • Creator, Publisher, Contributor, Rights • Instantiation • Date, Language, Format, Identifier • Encoding Schema • ”…identify schemas that aid in the interpretation of an element value.” • HTML example form “www.kb.se”: <META NAME="DC.Subject" SCHEME="SAB” CONTENT="Nationalbibliotek"> Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson Questions and Research directions Design & development: • Consensus / Collaborative v.s. Individual • Level of granularity Control & use: • Centralised v.s. distributed • Interoperability The OWL objectives • Layering, Commitment to portions of ontologies • Default values, CWA, Procedural attachment • ... Ontologies I Fredrik Arvidsson & Annika Flycht-Eriksson 5
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