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Learning Descriptions from the Semantic Web

Learning Descriptions from the Semantic Web. Gunnar Aastrand Grimnes Supervisors: Pete Edwards & Alun Preece Agents@Aberdeen Away Day 30/4/2004. Introduction. Semantic incompleteness: Personal classifications Time/context dependent Granularity difference => Machine Learning

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Learning Descriptions from the Semantic Web

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  1. Learning Descriptions from the Semantic Web Gunnar Aastrand Grimnes Supervisors: Pete Edwards & Alun Preece Agents@Aberdeen Away Day 30/4/2004

  2. Introduction • Semantic incompleteness: • Personal classifications • Time/context dependent • Granularity difference • => Machine Learning • Identify classes • Learn descriptions • Feed back into ontologies or use on the spot.

  3. Friend of a Friend (FOAF) • “The Friend of a Friend (FOAF) project is about creating a Web of machine-readable homepages describing people, the links between them and the things they create and do.” • 8908 people – 1980 know at least one person. • 147527 triples – 201 namespaces and 1066 unique properties. • FOAF data was also enriched by mapping AKT to FOAF.

  4. Example Profile <foaf:Person> <foaf:mbox rdf:resource=“mailto:ggrimnes@csd.abdn.ac.uk” /> <foaf:name>Gunnar AAstrand Grimnes</foaf:name> <foaf:projectHomepage rdf:resource=“.../research/agentcities”/> <foaf:groupHomepage rdf:resource=“.../research/agentsgroup” /> <foaf:depiction rdf:resource=“.../~ggrimnes/gfx/me.jpg” /> <foaf:interest rdf:resource=“http://www.w3.org/2001/sw/” /> <foaf:interest rdf:resource=“http://www.agentcities.net” /> <foaf:made rdf:resource=“.../research/AgentCities/GraniteNights” /> <contact:nearestAirport> <airport:Airport rdf:about=“http://www.daml.org/airport?ABZ” /> </contact:nearestAirport>

  5. Example Profile cont. <foaf:knows> <foaf:Person> <foaf:mbox rdf:resource=“mailto:maym@foobar.lu” /> <rdfs:seeAlso rdf:resource=“http://martinmay.net/foaf.rdf”/> </foaf:Person> </foaf:knows> <foaf:knows> <foaf:Person> <foaf:mbox rdf:resource=“mailto:apreece@csd.abdn.ac.uk” /> </foaf:Person> </foaf:knows> <foaf:knows> <foaf:Person foaf:name=“Sonja A Schramm”> <foaf:mbox_sha1sum>83276f91273f2900cf0b6657b3708b736276ef81</foaf:mbox_sha1sum> </foaf:Person> </foaf:knows> </foaf:Person> <rdf:Description rdf:about=“”> <wot:assurance rdf:resource=“foaf.rdf.asc” /> </rdf:Description>

  6. Foafnaut Fun! & Instant gratification!

  7. Pre-processing FOAF • FOAF is far from heterogeneous: • Human errors, i.e. foaf:knows • Wrong namespace, i.e. rdf:seeAlso, (not rdfs) • foaf:knows resource vs. literal • No uniform way of specifying foaf:interest • Copy and Paste culture  islands using specific properties.

  8. Clustering • Hierarchical Agglomerative Clustering CSD A3 AKT … … ? Pete Gunnar Alun Derek Dave

  9. Distance Metric • Hamming distance was unsatisfactory. • Need to consider graph surrounding person. • Distance metric for comparison of conceptual graphs (Montes-y-Gómez et al., 2000) • Given two (sub)graphs, considers node and edge overlap.

  10. Similarity Measure Gc • Sc = Node overlap for graph G1 & G2 : G2 G1 A A x y x y B C B C B x z x x z z D F D F/A D A z z E E

  11. The ILP System Aleph • Evaluating new classifications: • Qualitative assessment • Real life correspondence • Utility • Results were better when learned without foaf:knows. • Sloooow • Weeks to learn descriptions of clusters for only 10% of full data. • Identify “interesting clusters” based on distance:

  12. Results member(A) :- dc___creator(B,A), dc___title(B,”Managing Reference: Ensuring Referential Integrity of Ontologies for the Semantic Web”). member(A) :- contact___nearestAirport(A,”http://www.daml.org/airport?ABZ”). member(A) :- foaf___groupHomepage(A,”http://www.aktors.org”). member(A) :- trust___trustsHighly(B,A). member(A) :- dc___creator(B,A), dc___format(B,”application/postscript”).

  13. Conclusion • Learned Prolog descriptions converted into OWL class descriptions, or rules expressed in SWRL. • Need evaluation function – currently relies on visual inspection. • Applications of good descriptions: • Ontology evaluation / engineering guidance. • Personalisation?

  14. More: • Two papers: • Learning from Semantic Flora and Fauna Accepted forSemantic Web Personalisationworkshop atAAAI’04. • Learning Meta-Descriptions of the FOAF Network Submitted toThe International Semantic Web Conference’04. • Questions?

  15. Interesting cluster measure distc - distance between children or current node. dista & distb - distance between each pair of grand-children.

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