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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 Gunnar Aastrand Grimnes First Year Talk 14th May, 2003
Presentation Overview • Motivation • Preliminary Experiments • Agentcities & GraniteNights • The Future
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.
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
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?
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).
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
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
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" )
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>></cif:comparisonOperator> <cif:comparisonOp1> <cif:Variable rdf:about="#x"/> </cif:comparisonOp1> <cif:comparisonOp2> <cif:Integerconst> <cif:constantValue>1900</cif:constantValue> .. . .
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”/> ...
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.
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.