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Learning Knowledge Rich User Models from the Semantic Web

Learning Knowledge Rich User Models from the Semantic Web. Gunnar Aastrand Grimnes. First Year Talk 14 th May, 2003. Presentation Overview. Motivation Preliminary Experiments Agentcities & GraniteNights The Future. Motivation. The Semantic Web should: Facilitate learning from the Web.

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Learning Knowledge Rich User Models from the Semantic Web

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  1. Learning Knowledge Rich User Models from the Semantic Web Gunnar Aastrand Grimnes First Year Talk 14th May, 2003

  2. Presentation Overview • Motivation • Preliminary Experiments • Agentcities & GraniteNights • The Future

  3. Motivation The Semantic Web should: • Facilitate learning from the Web. • Facilitate reuse of learning outcomes. Hypothesis : • Learning from data annotated with semantic mark-up should outperform learning from traditional (HTML) Web. Goals: • The learned model should be expressed in a Semantic Web Language. • Such a learned model should be re-usable across domains and applications.

  4. Preliminary Experiments • Compare performance of learning from plain text and from semantic meta-data. • Using traditional ML algorithms as baseline approach: • Naïve Bayes • K-Nearest Neighbour • Explore application of more knowledge intensive approaches, such as ILP (Progol). An Empirical Investigation of Learning From the Semantic Web, Pete Edwards, Gunnar AA. Grimnes and Alun Preece – Presented at Semantic Web Mining Workshop at ECML/PKDD, Helsinki, 2002

  5. Issues • Datasets in a Semantic Web language were very hard to come by. • We used two datasets: • ITTalks (Seminars described using HTML vs. DAML+OIL). • Citeseer (Full text of Academic Papers vs. BibTex converted to RDF). • How does RDF map to an instance representation suitable for learning?

  6. Results • Largely negative. • K Nearest Neighbour on plain-text had best accuracy. • … but: 10 lines of RDF vs. 6000 words of full-text paper. • Reasons for failure: • Shallow and artificial RDF. • Statistical methods used. • Progol results were the most interesting: % Classifying Machine Learning papers: inClass(A) :- publisher(A,'Morgan Kaufmann'), booktitleword(A,learning).

  7. Agentcities & the Evening Scenario • EU funded – 5th F.W. • In Aberdeen since January’02. • WeatherAgent online since February’02. Evening Scenario • City Nodes • Tourist Information • Recommendations The fun has just started: • OpenNET

  8. GraniteNights • Raison d’être: • Agentcities Agent Technology Competition. • Need a Semantic Web framework for learning user profiles. • Bring together different people/research areas in the department: agents, learning, scheduling, constraints, etc. • Proof that RDF is usable! GraniteNights - A Multi-Agent Visit Scheduler Utilising Semantic Web Technology, Gunnar AA. Grimnes, Stuart Chalmers, Pete Edwards and Alun Preece Submitted to CIA2003

  9. GraniteNights - Example

  10. GraniteNights - Architecture

  11. Query By Example • RDQL too complicated to write by hand. • Query by example is very intuitive. • Internal conversion to RDQL. • Could be “smarter” than RDQL. <q:Query> <q:template> <akt:Academic> <akt:family-name> Brown </akt:family-name> </akt:Academic> </q:template> </q:Query> SELECT ?x WHERE (?x, ?y, ?z), ( ?x, <rdf # type>, <akt # Academic> ), ( ?x, <akt # family-name>, "Brown" )

  12. QbEx with constraints <q:Query> <q:template> <r:Restaurant> <r:type rdf:resource=“r#Tandoori" /> <r:open-time> <cif:Variable rdf:ID="x"> <cif:varname>x</cif:varname> </cif:Variable> </r:open-time> </r:Restaurant> </q:template> <q:constraints> <cif:Comparison> <cif:comparisonOperator>&gt;</cif:comparisonOperator> <cif:comparisonOp1> <cif:Variable rdf:about="#x"/> </cif:comparisonOp1> <cif:comparisonOp2> <cif:Integerconst> <cif:constantValue>1900</cif:constantValue> .. . .

  13. GraniteNights Profiling <ep:User rdf:about=“profileagent#gunnar” ep:name=“gunnar” ep:pword=“****”> <ep:preference> <q:Query> <q:template> <pub:EnglishPub> <pub:servesBeer rdf:resource=“#flowers”/> </pub:EnglishPub> ... <ep:interactions> <rdf:Seq><rdf:li> <ep:Interaction ep:timestamp=“20030508T135013”> <ep:pref> <q:Query> <q:template> <pub:EnglishPub> <pub:servesBeer rdf:resource=“#flowers”/> </pub:EnglishPub> ... <pub:EnglishPub> <pub:servesBeer rdf:resource=“#hobgoblin”/> ... <pub:EnglishPub> <pub:servesBeer rdf:resource=“#flowers”/> ...

  14. GraniteNights Profiling II • Current implementation: • Most frequently specified constraint. • Possible improvements: • Super/Sub-class inference in the ontology, i.e. Flowers and Hobgoblin are both sub-classes of Real Ale. • Combination of constraints important, i.e.Pete likes Lager when eating Curry, but Ale for his occasional pub-visit. • Requires more sophisticated techniques than counting.

  15. The Future • User modelling in a broader scope: • User roles, commitments etc. • Learning from RDF: • Generalisation. • Case-based reasoning. • RDF as model language. Learning Knowledge Rich User Models from the Semantic Web, Gunnar AA. Grimnes To appear in Doctoral Consortium, User Modeling 2003, Pittsburgh, July 2003.

  16. Questions ?

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