1. 1 On the role of a Librarian Agent in ontology- based Knowledge Management Systems Nenad Stojanovic Institute AIFB WM 2003 Luzern, 2. – 4. 4. 2003

2. 2 A genda Motivation Architecture Implementation Conclusion

3. 3 The Management of the Searching for Information in Semantic Portals Challenges for the ontology-based Information Retrieval: (inherited from the Web IR) - a user posts short queries - a user reads only top-ten results (inherited from the Digital Libraries) - users’ needs vary Query Management Ranking Schema Collection Management

4. 4 Approach: Simulating bricks-and-mortar environment I need something about ontologies and evolution 1. Do I have that in the repository 2. Please, refine the request evolution 4. How? 5. A lot of users search for +Editor 6. +Editor, but visual 7. You got 20 results 8. I have time to see three only. Are these the best ones 9. To notice: one query more for ontologies,... 10. Librarian Agent 1000 results! This query is so ambiguous 3. Collection Management Query Management Do I have that in the repository Please, refine the request evolution To notice: one query more for ontologies,... A lot of users search for +Editor 1000 results! This query is so ambiguous Ranking You got 20 results

5. 5 A genda Motivation Architecture Implementation Conclusion

6. 6 query response query/ response/ action Log file Access mechanism Information repository User Librarian Agent Domain ontology Query Management Collection Management query response query 1. ambiguity 2. refinements 3. feed back 5. change recommendations 6. Librarian Agent - Architecture Ranking 4.

7. 7 1. The Ambiguity of a Query Definition : A query is ambiguous if the information need behind that query cannot be determined uniquely Types: A. semantic ambiguity – regarding the used vocabulary - incompleteness - unclearness - redundancy - un-satisfiable query B. content ambiguity – regarding the information repository - equivalency - generalisation - specialisation

8. 8 1.A. Semantic Ambiguity X:Doctor[worksIn->>KM] and X[inGroup->>KM] and X:Researcher. Compactness: X:Doctor or X:Researcher Researcher Doctor Professor Dozent Clarity: X:Doctor means X:Professor or X:Dozent Redundancy: X[worksIn->>KM] is redundant information Rule: FORALL X, X[worksIn->>KM] >KM].

9. 9 1.B. Content Ambiguity For a Query-Answering pair: (M, O) Structural equivalence (=): Structural subsumption (parent-child) (<): query_terms query_objects Parameters: Largest equivalent query: Smallest equivalent query Uniquness Covering CoveringTerms

10. 10 1.B Content Ambiguity – Query Map Query: “Prof. and Project and KM“ NumberOfTerms:=3 NumberOfObjects:=3... MaxEquality:= 2 LargestEqu:= “Prof. and Project and KM and Group“ MinEquality:= 1 SmallestEqu:=(“Prof. and Project“) or (“Prof. and KM“) Uniquness:=P5 Generalisation:“Lecturer and Group and KM“ NumberOfTerms:=3 NumberOfObjects:=9... Covering = 90% Generalisation:“Researcher and Project and KM“ NumberOfTerms:=3 NumberOfObjects:=8... Covering = 50% Sibling:“PhD and Project and KM“ NumberOfTerms:=3 NumberOfObjects:=1... Specialisation:“Prof. and EU-Project and KM“ NumberOfTerms:=5 NumberOfObjects:=1... Relations generated by FCA

11. 11 2. Query Refinement X:Doctor[worksIn->>KM] and X[inGroup->>KM] and X:Researcher; a) the structure of the ontology X:Researcher -> X:Doctor + X[project->>.] „project“ is an appropriate classifier b) the capacity of the knowledge repository P1[inGroup->>KM;worksIn->>KM;course->>X; project->>Onto1] P2[inGroup->>KM;worksIn->>KM;course->>Y; project->>Onto1]... P3[inGroup->>KM;worksIn->>KM;course->>W; project->>Onto2] P4[inGroup->>KM;worksIn->>KM;course->>Z; project->>Onto2] c) user’s behaviour (how do users refine their queries) QueryLog: Collection Sessionization Query-patterns Analyse

12. 12 A genda Motivation Architecture Implementation Conclusion

13. 13 Librarian Agent in the “Usage Mining Loop” Server Browser Ontology Manager Usage OLAP cube Content vs Usage OLAP cube Ontology + Knowledge WH Ontology information Ontology Evolution Semantic crawler Other Ontology Management tools HTTP Request Inference Engine … Web page + Ontology information semantic Log file SemiPort

14. 14 Librarian Agent in the KAON framework Persistence, Transaction, Security RDF API KAON RDF SERVER KAON API Data layer Middleware KAON PORTAL Applications & Services Query Recommendation Domain ontology Information repository Map Visualizer Vision Portal Usage ontology Usage Log Ambiguity Measurement Query Refinement Query Management Ranking Module Collection Management Map Builder Usage Logging

15. 15 A genda Motivation Architecture Implementation Conclusion

16. 16 Conclusion Librarian Agent simulates the role of a shop assistant in searching in the brick and mortar environement Key issues: measuring ambiguity of a query ordering query space using FCA Very applicable in the Knowledge Portals Implementation in the KAON is on the way Planed large-scale evaluation for the MEDLINE

17. 17 Thank you for the attention !