The document discusses learning user models from semantic web data. It describes preliminary experiments comparing learning from plain text vs semantic metadata, as well as an application called GraniteNights that uses RDF to learn user preferences for scheduling visits. Future work is proposed to further user modeling techniques and address issues with using RDF.

1. Learning Knowledge Rich User Models from the Semantic Web Gunnar Aastrand Grimnes User Modeling 2003 – Doctoral Consortium 24th June, 2003

2. Presentation Overview Motivation Part I - Preliminary Experiments Part II - 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 user model should be expressed in a Semantic Web Language. Such a learned model should be re-usable across domains and applications.

4. Part I - 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, although it generated lots of non-compressing rules. % Classifying Machine Learning papers: inClass(A,’ML’) :- publisher(A,'Morgan Kaufmann'), booktitleword(A,learning).

7. Part II Agentcities @ Aberdeen & GraniteNights

8. GraniteNights Raison d’être: Agentcities Agent Technology Competition. Need a Semantic Web framework for learning user models. 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 Accepted for 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 >, &quot; Brown &quot; )

12. QbEx with constraints <q: Query > <q: template > <r: R estaurant > <r: type rdf: resource =“ r#Tandoori &quot; /> <r: open-time > <cif: V ariable rdf: ID =&quot; x &quot;> <cif: varname > x </cif: varname > </cif: V ariable > </r: open-time > </r: R estaurant > </q: template > <q: constraints > <cif: C omparison > <cif: comparison O perator > &gt; </cif: comparison O perator > <cif: comparison O p1 > <cif: V ariable rdf: about =&quot; #x &quot;/> </cif: comparison O p1 > <cif: comparison O p2 > <cif: I ntegerconst > <cif: constant V alue > 1900 </cif: constant V alue > .. . . Give me a Restaurant that is open after X, where X > 1900. i.e. Give me a restaurant open after 7 pm.

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 – RDF Issues Must strike a balance: RDF purely for representation: Not very interoperable. Not very ground-breaking. Using RDF data-model: RDF expressive power is limited. Extend using Rule-ML? OWL? Lots of unsolved RDF problems for free: Anonymous nodes / graph isomorphism Software support is immature. Inference

16. The Future – Problems Future work on GraniteNights: Real users for real user modelling: Substantial software engineering efforts required to make a system that attracts users. Simulating user interactions is an option. Adding features: Reviews / Recommendations (This pub has good guinness)

17. The Future – Plans Near future plans: Further empirical investigations: Inductive logic programming contd. Case-based reasoning. User modelling in a broader scope: User roles, commitments etc.

18. Questions ?

19. Rule-ML Example <rule: Imp ><rule: head > <rule: Atom > <rule: rel rdf: resource =“ ~ggrimnes/dev/exp#inClass ”/> <rule: atomArg > <rule: Var rdf: about =“ #A ”> <rule: varName > A </rule: varName > </rule: Var > </rule: atomArg > <rule: atomArg > ML </rule: atomArg > </rule: Atom > </rule: head > <rule: body > <rule: And > <rule: arg > <rule: Atom > <rule: rel rdf: resource =“ ~ggrimnes/dev/exp#booktitleword ”/> <rule: atomArg > <rule: Var rdf: about =“ #A ”> <rule: varName > A </rule: varName > </rule: Var > </rule: atomArg > <rule: atomArg > learning </rule: atomArg > </rule: Atom > </rule: arg > <rule: arg > <rule: Atom > <rule: rel rdf: resource =“ ~ggrimnes/dev/exp#publisher# ”/> <rule: atomArg > <rule: Var rdf:about=“ #A ”> <rule: varName > A </rule: varName > </rule: Var > </rule: atomArg > <rule: atomArg > Morgan Kaufman </rule: atomArg > </rule: Atom > </rule: arg > </rule: And > </rule: body > </rule: Imp >

20. Agentcities & the Evening Scenario EU funded – 5 th F.W. In Aberdeen since January’02. WeatherAgent online since February’02. Evening Scenario City Nodes Tourist Information Recommendations