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  • Full Name Full Name Comment goes here.
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  • Serge: A good explanation covering basic and advanced concepts of ontologies and their applications.

    I note that 'relation' is considered a property of subject. However the RDF triples show the predicate (the relation or a link) as an independent element. I see a lot of advantage of treating LINK also as an independent full-fledged object with its own properties as in 'Association Class of UML'.

    I am investigating how to model 'meaning' and incorporate it in the ontologies and knowledge bases in a manner that can be 'acted out' or 'executed'. I find this facility is NOT available still except in simple cases.

    Please take a look at my PPTs
    Pentagon of Meaning and Meaning is Mediated
    on slideshare and let me know when we can discuss.
    Regards,
    putchavn@yahoo.com
    04JUL14
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Semantic Web - Ontologies Presentation Transcript

  • 1. 5. Introduction to Ontologies Semantic Web Unit 5: Ontologies Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 2. 5. Introduction to Ontologies 5.1. Why is RDF not sufficient? Semantic Web Roadmap: Controlled growth bottom up according to this architecture. Architecture was (slightly) modified in the last years. Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 2
  • 3. 5. Introduction to Ontologies 5.1. Why is RDF not sufficient? 5.1. Sharing a conceptualization 5.2. Ontologies in Computer-Science 5.3. Ontology Language 5.4. Types of Ontologies 5.5. Linked Data 5.6. Ontology Tools 5.7. References Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 3
  • 4. 5. Introduction to Ontologies 5.1. Sharing a conceptualization Level of knowledge representation and semantics OWL domain knowledge, interconn ections RDF / RDF Schema knowledge about objects, relations between objects XML / XML Schema objects, structure Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 4
  • 5. 5. Introduction to Ontologies 5.1. Sharing a conceptualization Different people, different perceptions collaborative tagging Web 2.0 approach authoritative metadata Semantic Web approach Resource picture artwork Author Users nudity woman woman artwork porn ? photo Need of a shared conceptualization Search engine Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 5
  • 6. 5. Introduction to Ontologies 5.1. Sharing a conceptualization Conceptualization is a picture name length concepts street taken 1 0..* photo 0..* depicted relations between concepts attributes is a is a instances bd. JFC 3 km human 0..* woman Using the same ontology allows two different systems to communicate and to reason over (meta)data man ≠ Louise Ciccone 54 173 cm Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu name age size 6
  • 7. 5. Introduction to Ontologies 5.2. Ontologies in Computer-Science Ontologies in Computer-Science An ontology has a common language (symbols, expressions ) syntax The meaning of the symbols and expressions in an ontology is clear  semantics Symbols and expressions with similar semantics are grouped in classes  conceptualization Concepts are organized in a hierarchical way  taxonomy Implicit knowledge can be made explicit  reasoning An ontology is an explicit, formal specification of a shared conceptualization (Thomas R. Gruber, 1993) Conceptualization Specification Explicit Formal Shared : : : : : abstract model of domain related expressions domain related semantics of all expressions is clear machine-readable consensus (different people have different perceptions) Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 7
  • 8. 5. Introduction to Ontologies 5.2. Ontologies in Computer-Science Structure of ontologies Classes: (concepts) are abstract groups, sets, or collections of objects (individuals and classes). Here: Thing, Human, Father, etc are classes. Individuals: (instances) are the basic, "ground level" components of an ontology. For example: SerLinck is an individual of the class Man, formally: Man(SerLinck) Taxonomy: hierarchical representation of classes Attributes: (properties) describing objects (individuals and classes) in the ontology. Here, the class Human has an attribute hasName and the individual SerLinck has the attribute value "Serge Linckels" for the attribute hasName Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 9. 5. Introduction to Ontologies 5.2. Ontologies in Computer-Science Structure of ontologies Relationships: (associations) expressing how objects in the ontology are related to each other. Typically a relation is an attribute whose value is another object in the ontology. There are two common types of relations: the vertical "subsumption" and (normally) horizontal user defined relations. hasChild subsumption relation: (is-superclass-of) defines which objects are members of classes. Here Man subsumes Father. user defined: defines any kind of relation between objects. E.g., hasHusband is a relation from the class Woman to the class Man. Relations can be recursive, e.g, a human has a child that is human. hasHusband Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 10. 5. Introduction to Ontologies 5.2. Ontologies in Computer-Science Structure of ontologies Restrictions can be attached to relations: quantified restrictions, e.g., • a woman can have 0 or 1 husband • a human can have 0 or n children • every mother must have at least one child hasChild difference, e.g., a woman is not a man (a human can be either a woman or a man, not both) hasHusband Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 11. 5. Introduction to Ontologies 5.2. Ontologies in Computer-Science Reasoning over ontologies Axioms are knowledge definitions in the ontology that was explicitly defined and that have not to be proven true Examples: • SerLinck is an individual of the class Father • MagLinck is an individual of the class Woman • MagLinck has BobLinck as child hasChild Implicit knowledge can be made explicit by logical induction  reasoning over the ontology Examples: • Because SerLinck is an individual of the class Man, he is human (because Human subsumes Man) • Because MagLinck has a child, she is an individual of the class Mother • The class Wife can be inductively defined as being all the women who have at least one husband hasHusband Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 12. 5. Introduction to Ontologies 5.3. Ontology Language Knowledge representation Informal representation of knowledge: Examples of natural language: • a woman can have 0 or 1 husband • every mother must have at least one child • a woman is not a man (a human can be either a woman or a man, not both) How to represent such expressions in a computer-readable way, in order to reason over that knowledge? Ontology Language Beside the structural dimension of an ontology, an ontology uses a common language to formalize its specifications and conceptualizations Examples of ontology languages: • Web Ontology Language (OWL) • Ontology Interface Layer (OIL) • DARPA Agent Markup Language (DAML) • CycL • Knowledge Interchange Format (KIF) Most of these languages are based on a subset of First Order Logic (FOL) Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 12
  • 13. 5. Introduction to Ontologies 5.3. Ontology Language Example of knowledge representation Formal representation: computer-readable and free of ambiguities (only one interpretation possible), e.g., code in a programming language Informal representation: not formal, meaning something not characterized by a clear and unambiguous interpretation, e.g., natural language Informal Formal A woman can only have male husband Woman  hasHusband.Man Every mother must have at least one human child Mother A human can either be a woman or a man, not both Human Woman Man Woman  Man Man  Woman Woman  hasChild.Human Description Logics Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 14. 5. Introduction to Ontologies 5.4. Types of Ontologies Upper Ontologies An upper ontology (or world ontology) is a model of the common objects that are generally applicable across a wide range of domain ontologies It contains a core glossary in whose terms objects in a set of domains can be described Examples: Dublin Core metadata element set is a standard for cross-domain information resource description. In other words, it provides a simple and standardized set of conventions for describing things online in ways that make them easier to find. The General Formal Ontology (GFO) integrates processes and objects. GFO provides a framework for building custom, domain-specific ontologies OpenCyc includes hundreds of thousands of terms along with millions of assertions relating the terms to each other. One stated goal is that of providing a completely free and unrestricted semantic vocabulary for use in the Semantic Web. Suggested Upper Merged Ontology (SUMO) was developed within the IEEE Standard Upper Ontology Working Group. The goal is to develop a standard ontology that will promote data interoperability, information search and retrieval, automated inferencing, and natural language processing. Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 14
  • 15. 5. Introduction to Ontologies 5.4. Types of Ontologies Domain Ontologies A domain ontology models a specific domain, or part of the world It represents the particular meanings of terms as they apply to that domain Examples: One of the most cited ontologies is the wine ontology it is about the most appropriate combination of wine and meals The soccer ontology describes most concepts that are specific to soccer: players, rules, field, supporters, actions, etc. It is used to annotate videos in order to produce personalized summary of soccer matches An ontology library for lung pathology is maintained by the FU-Berlin. The aim of the project "A Semantic Web for Pathology" is to realize a semantic web based retrieval system for the domain of lung pathology. For this purpose the pathology data is annotated with semantic references, and the textual pathology reports are used as descriptions of what the associated images represent The music ontology provides main concepts and properties for describing music, i.e. artists, albums, tracks, but also performances, arrangements, etc. Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 15
  • 16. 5. Introduction to Ontologies 5.4. Types of Ontologies Expressivity of ontologies In General, the more specific the ontology is, the more expressive it becomes – terms glossary catalog ID + Expressivity informal "is-a" thesaurus lightweight ontologies controlled, unambiguous, and finite set of vocabulary in a catalog finite list of terms and meaning in natural language additional semantics with relations between terms (thesaurus) conceptualization in a hierarchy of few top-classes (Lassila/McGuinness, 2001) Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 16
  • 17. 5. Introduction to Ontologies 5.4. Types of Ontologies Expressivity of ontologies In General, the more specific the ontology is, the more expressive it becomes – terms glossary catalog ID + Expressivity informal "is-a" thesaurus formal "instance" formal "is-a" value restrictions frames properties general logic constraints disjointness inverse part-of heavyweight ontologies taxonomies with strict subclass relationships logical induction over instance checking classes include property information more complex restrictions, e.g., disjointnes s complete and complex logical expressions using logical quantifiers to express restrictions Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu (Lassila/McGuinness, 2001) 17
  • 18. 5. Introduction to Ontologies 5.4. Types of Ontologies Expressivity of ontologies In General, the more specific the ontology is, the more expressive it becomes – terms glossary catalog ID + Expressivity informal "is-a" thesaurus formal "instance" formal "is-a" value restrictions frames properties general logic constraints disjointness inverse part-of XML RDF OWL Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 18
  • 19. 5. Introduction to Ontologies 5.4. Types of Ontologies Example: Thesaurus of English words http://www.visualthesaurus.com/ Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 19
  • 20. 5. Introduction to Ontologies 5.4. Types of Ontologies Example: Linnaen Taxonomy Linnaean taxonomy is a method of classifying living things in a taxonomy based on "is-a" relationships By Carl Linnaeus (1707 – 1778) Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 20
  • 21. 5. Introduction to Ontologies 5.4. Types of Ontologies Example: WordNet – lexical database (thesaurus & taxonomy) Semantically equivalent words (synsets) are interlinked by means of conceptual-semantic and lexical relations hyperonym: a word with a more general meaning (e.g., animal is a hyperonym of cat), hyponym: a word with a more specific meaning (e.g., cat is a hyponym of animal), synonym: a word with identical meaning (e.g., car and automobile are synonyms), homonym: words with identical spelling but different meaning (e.g., Ada is a programming language but also a person). http://wordnet.princeton.edu/ Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 21
  • 22. 5. Introduction to Ontologies 5.4. Types of Ontologies Example: KR Ontology (Upper Ontology) Describes general concepts independent from a given context Top level ontology with 27 concepts and interlinked (lattice) http://www.jfsowa.com/ontology/ Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 22
  • 23. 5. Introduction to Ontologies 5.4. Types of Ontologies Example: Cyc (Upper Ontology) Includes hundreds of thousands of terms along with millions of assertions relating the terms to each other Complex queries can be expressed, also in natural language http://www.opencyc.org/ Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 23
  • 24. 5. Introduction to Ontologies 5.4. Types of Ontologies Example: Wine Ontology (Domain Ontology) It is about finding the most appropriate combination of wine and meals http://www.w3.org/TR/owl-guide/wine.rdf Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 24
  • 25. 5. Introduction to Ontologies "Using the Web to connect related data that wasn't previously linked, or using the Web to lower the barriers to linking data currently linked using other methods." Linked Open Data: DBPedia plays a central role as it makes the content of Wikipedia available in RDF Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 25
  • 26. 5. Introduction to Ontologies 5.5. Linked Data Linking open-data community project Goal: “expose” open datasets in RDF Set RDF links among the data items from different datasets Set up SPARQL endpoints Billions of triples, millions of “links” DBpedia is a community effort (1) to extract structured information from Wikipedia, (2) to provide a SPARQL endpoint to the dataset, and (3) to interlink the DBpedia dataset with other datasets on the Web Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 27. 5. Introduction to Ontologies 5.5. Linked Data DBpedia Extracting structured data from Wikipedia @prefix dbpedia <http://dbpedia.org/resource/>. @prefix dbterm <http://dbpedia.org/property/>. dbpedia:Amsterdam dbterm:officialName "Amsterdam" ; dbterm:longd "4" ; dbterm:longm "53" ; dbterm:longs "32" ; dbterm:website <http://www.amsterdam.nl> ; dbterm:populationUrban "1364422" ; dbterm:areaTotalKm "219" ; dbterm:hometown 2_Unlimited ; dbterm:location Anne_Frank_House ; ... New entry points (resources) Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 28. 5. Introduction to Ontologies 5.5. Linked Data DBpedia Automatic links among open datasets <http://dbpedia.org/resource/Amsterdam> owl:sameAs <http://rdf.freebase.com/ns/...> ; owl:sameAs <http://sws.geonames.org/2759793> ; ... <http://sws.geonames.org/2759793> owl:sameAs <http://dbpedia.org/resource/Amsterdam> wgs84_pos:lat “52.3666667” ; wgs84_pos:long “4.8833333” ; geo:inCountry <http://www.geonames.org/countries/#NL> ; ... Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 29. 5. Introduction to Ontologies 5.5. Linked Data Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 30. 5. Introduction to Ontologies 5.5. Linked Data Music ontology Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 31. 5. Introduction to Ontologies 5.5. Linked Data Music ontology New entry points (resources) Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 32. 5. Introduction to Ontologies 5.5. Linked Data Music ontology Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 33. 5. Introduction to Ontologies 5.5. Linked Data Slideshare ! False positive ! (I promise) Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 34. 5. Introduction to Ontologies 5.5. Linked Data Slideshare Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 35. 5. Introduction to Ontologies 5.5. Linked Data Linked Data – putting it all together Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 36. 5. Introduction to Ontologies 5.5. Linked Data Linked Data – putting it all together Search Engines Sindice Amazon EC2 Marbles Engine HTTP GET Shared Cache FalconS Linked Data on the Web Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu
  • 37. 5. Introduction to Ontologies 5.6. Ontology Tools Example: OntoEdit Supports F-Logic, RDF-Schema and OIL Interface to the F-Logic Inference Engine and FaCT http://www.ontoknowledge.org/tools/ontoedit.shtml Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 37
  • 38. 5. Introduction to Ontologies 5.6. Ontology Tools Example: Protégé The Protégé platform supports two main ways of modeling ontologies via the Protégé-Frames and Protégé-OWL editors Protégé ontologies can be exported into a variety of formats including RDF(S), OWL, and XML Schema Java Application; multiple plug-ins available Interfaces to different reasoners http://protege.stanford.edu/ Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 38
  • 39. 5. Introduction to Ontologies Example: Protégé create the classes of the taxonomy create properties that are related to another class (range) create properties that have literal values create instances of classes and properties Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 39
  • 40. 5. Introduction to Ontologies 5.6. Ontology Tools Others WebOnto : http://kmi.open.ac.uk/projects/webonto/ OilEd : http://oiled.man.ac.uk/ Integrated Ontology Development Environment : http://www.ontologyworks.com/ LinKFactory Workbench : http://www.landc.be/ SymOntos : http:www.symontos.org Many others, e.g., http://xml.com/2002/11/06/Ontology_Editor_Survey.html Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 40
  • 41. 5. Introduction to Ontologies 5.7. References E-Librarian Service User-Friendly Semantic Search in Digital Libraries Serge Linckels, Christoph Meinel Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce Dieter Fensel Handbook on Ontologies Steffen Staab, Rudi Studer Foundations of Semantic Web Technologies Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph Semantic Web ::: Serge Linckels, 2013 ::: www.linckels.lu ::: serge@linckels.lu 41