2. 1 Semantic Search Agent System applying Semantic Web Techniques 2004.10.21 Jung-Jin Yang Intelligent Distributed Information System (IDIS) Lab. School of Computer Science & Information Engineering The Catholic University of Korea jungjin@catholic.ac.kr http://idis.catholic.ac.kr/jungjin

3. 2 Agenda Semantic Search Ontology Ontology-based Semantic Search Agent OnSSA Conclusion

4. 3 Searching Semantically How to handle problems in searching for information? Time intensive e.g. for the query “disease and remedy” a user cannot find a relevant result What can be the problem: 1. the query is too ambiguous 2. the used terms do not match the repository 3. the results are not properly ranked …

5. 4 Moreover Cognitive demand on users in a professional domain e.g. for the query “hearing deficit” in searching medical literature through MEDLINE DB a user cannot find adequate results What can be the problem: 1. the query is too ambiguous 2. the used terms do not match the repository 3. the results are not properly ranked 4. the lacking knowledge of professional terms …

6. 5 Semantic Search Information repository I need info. about deafness Tip: There 30330 documents for the desease, BUTonly 23 literatures with relevant gene names Ontology An ontology introduces new possibilities for query/answering Cooperative answering DiseaseName(x) and gene(x,Caused)

7. 6 Semantic Search Develop an intelligent agent system to produce a more precise search result combine search engine and ontology corpus-based & concept-based supports continual improvement of an information retrieval according to its usage

8. 7 It is found by machine agent yes Relevant resource exists Activities in Searching for Information User‘s information need Query yes It is top-ranked User has found a resource relevant for the query yes User‘s request is not satisfied no Refinement Information repository

9. 8 Relevant resource exists It is found by software agent - Information repository contains resources relevant to the user’s need! - Resources are annotated properly ! User has found a resource relevant for the query yes no Query User‘s query is not satisfied Challenges User‘s information need It is top-ranked - Query reflects the user’s need ! - Resources are ranked according to the relevance to the user‘s need ! yes no - Query refinement closes the gap between the query and the user’s information need ! Information repository

10. 9 Agenda Semantic Search Ontology Ontology-based Semantic Search Agent OnSSA Conclusion

11. 10 Sementic Web Modeling RDFRDF SchemaOWL Graph Labeled graph Ontology Data Dictionary Data Schema …... Logic KIF? Ontology Graph + limited logic (figured by Jim Hendler at Semantic Web Conf. 2003)

12. 11 Ontology Philosophy: A systematic account of existence An ontology is a formal conceptualization of the world. (T. R. Gruber) An ontology specifies a set of constraints, which declare what should necessarily hold in any possible world. An ontological commitment is an agreement to use a vocabulary (i.e., ask queries and make assertions) in a way that is consistent (but not complete) with respect to the theory specified by an ontology: Knowledge Sharing An ontology specifies a rich description of the : Terminology Concepts Relationships between the concepts Rules Relevant to a particular domain or area of interest

13. 12 Upper-, Mid-level, Lower-Ontologies An upper-ontology defines very broad, universal Classes and properties Example: Cyc Upper Ontology http://www.opencyc.org A mid-level ontology is an upper ontology for a specific domain A lower-ontology is an ontology for a specific domain, with specific Classes and properties. You can merge into an umbrella, upper-level ontology by defining your ontologies root class as a subClassOf a class in the upper-ontology.

14. 13 Knowledge Representation Representation of knowledge Description of world of interests Usable by machines to make conclusions about that world Intelligent System Computational system Uses an explicitly represented store of knowledge To reason about its goals, environment, other agents, itself Expressiveness vs. tractability tradeoff How to express what we know How to reason with what we express

15. 14 Processing Knowledge = “Reasoning” Representation of Knowledge Access represented knowledge and process it. Access alone is, in general, insufficient Implicit knowledge has to be made explicit deduction methods The results should only depend on the semantics … And not on accidental syntactic differences in representations

16. 15 Ontology Modeling & Technologies A systematic account of existence of knowledge and intelligence for a particular domain Ontology modeling using appropriate Tools and Language e.g., OntoEdit, OilEd, Protégé, VOM (Visual Ontology Modeler) e.g., XML, RDF, OWL Reasoning capabilities: Description Logics Provide theories and systems for expressing structured information and for accessing and reasoning with it in a principled way. Ontology query/update for ontology repositories

17. 16 Ontology Modeling (Protégé 2000): http://protege.stanford.edu

18. 17 Ontology Modeling (VOM): http://www.sandsoft.com/

19. 18 Remark Ontology Standards Integration: Semantic Integration A language for writing data Reaching out onto the Web Ontology Modeling No one correct way to model a domain Iterative ontology development process Natural correspondence to objects and relationships in your domain of interest.

20. 19 Agenda Semantic Search Ontology Ontology-based Semantic Search Agent OnSSA Conclusion

21. 20 Architecture of Intelligent Information Agent (by Enrico Franconi, Univ. of Manchester, UK) An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors. (by Russell & Norvig)

22. 21 Architecture of Intelligent Information Agent

23. 22 Architecture of Intelligent Information Agent

24. 23 Architecture of Intelligent Information Agent

25. 24 Architecture of Intelligent Information Agent

26. 25 Architecture of Intelligent Information Agent

27. 26 Architecture of Intelligent Information Agent

28. 27 Architecture of Intelligent Information Agent

29. 28 Architecture of Intelligent Information Agent

30. 29 Architecture of Intelligent Information Agent

31. 30 Architecture of Intelligent Information Agent

32. 31 Semantic IR System

33. 32 Agenda Motivation Ontology Ontology-based Semantic Search Agent OnSSA Conclusion

34. 33 OnSSA : Ontology-based Semantic Search Agent 1. Users are reluctant/unable to provide explicit feedback about the „quality“ of the ontology => use implicit relevance feedback suggested lists of broader/narrower terms Requirements: 2. There are many types of related information and represented in different forms. => Distributed information Agent with different search strategies

35. 34 OnSSA The System requery IR Agent Query Engine Search/ Output Ranking Information Agent 1 Search engine & Ontology Query Models PubMed OMIM HUGO Ensemble Mining Engine User query Result Ranking Search Result Consulting Agent GUI User Information Agent 2 Information Agent 3 Information Agent 4

36. 35 OnSSA Consulting Agent 1. Query Refinement 2. Ranking Management Query management: What is a user searching for? Note: A user‘s query is just an approximation of the, often ill- defined, user‘s information need[Saracevic75]

37. 36 QueryModel is a concept-based rule engine consist of Jena, SweetJess and Jess ontology Translation(Jena)Translation(SweetJess) Logic(Jess) RDF+rdfschema XML+ns+xmlschema Restrict(Jena)RuleML UMLS QueryModels Architecture

38. 37 Jena Store a data of RDF and represent RDF graphs and write in N-Triples format Load a Daml+OIL ontology in Java using Jena Navigate an RDF graph within Jena using RDQL Jena Architecture RDQL Grammar

39. 38 Jess is a rule engine and scripting environment written entirely in JAVA uses the Rete algorithm to process rules, a very efficient mechanism for solving the difficult many- to-many matching problem

40. 39 SweetJess is a new system for Semantic Web rules to be used in Jess provides translation (DamlRuleML, RuleML, JessRule) Provided by UMBC

41. 40 UMLS What ’ s it? develops and distributes multi-purpose, electronic "Knowledge Sources" and associated lexical programs

42. 41 OnSSA The QueryModel Ontology Consulting Agent GUI MetaRule SweetJess Corpus-based (UMLS) Concept-based Search Engine Jena JessRule

43. 42 Let’s Go! GUI (UserInterface) UMLS Ontology QueryModel MetaRule RuleJess SweetJess Jena UMLS Search Engine deafness Jena Semantic Web Toolkit (deffacts data(http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Total_transitory_deafness) (http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Middle_ear_deafness) (http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Bilateral_Deafness) (http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Deafness_permanent_partial) (http://idiscatholicackr/umlsRetrieveOtherRelation DEAFNESS Cockayne_Syndrome). (http://idiscatholicackr/umlsRetrieveOtherRelation DEAFNESS Lipreading) (http://idiscatholicackr/umlsRetrieveNarrower DEAFNESS Hearing_Loss_Sensorineural) (http://idiscatholicackr/umlsRetrieveBroader DEAFNESS Disability_NOS) (UserInput DEAFNESS) ) ① GUI (UserInterface) UMLS Ontology QueryModel MetaRule RuleJess SweetJess Jena UMLS Search Engine rule1 GeneDisease type query UserInput query Result query gene RuleML (reset) (defrule rule1 (GeneDisease ?type ?query) (UserInput ?query) => (assert (Result ?query gene)) ) ② GUI (UserInterface) UMLS Ontology QueryModel MetaRule RuleJess SweetJess Jena UMLS Search Engine (deffacts data(http://idis … (reset) (defrule rule1 … (run) New fact & ReQuery QueryModel Processing

44. 43 Introduction about Databases MEDLINE A database of indexed journal citations and abstracts. Pubmed a service of the National Library of Medicine, includes over 14 million citations for biomedical articles back to the 1950's. These citations are from MEDLINE and additional life science journals. OMIM Online Mendelian Inheritance in Man is a database of human genes and genetic disorders. HUGO Human gene nomenclature

45. 44 OnSSA The System

46. 45 OnSSA Information Agents

47. 46 OnSSA Agent Ontology

48. 47 Agenda Semantic Search Ontology OnSSA Conclusion

49. 48 Conclusion Results of OnSSA in publications Marriage of Semantic Web and Agent technology promising for more intelligent search strategy

50. 49 Future: Agent-based Service Ontology Structure ■ Server API ■ Server ■ Agent Other Agent ■ Other ■ Agent Agent Platform Other Agent Platform Web Service Space ■ Gateway ■ WS ■ Application Server API Server Agent Other Agent Gateway Ontology Repository WS Application

51. 50 Conclusion Semantic Web + Web Service + Agent Technology The real benefit is yet to come or already..

52. 51 Thank You..