1 / 31

Knowledge Management through Ontologies

Knowledge Management through Ontologies. Richard Benjamins - U. Amsterdam Dieter Fensel - U. Karlsruhe Asuncion Gomez Perez - U. Madrid. Large distributed (multinational) enterprise Thousands employees Right person at the right place Who knows what?. Who is expert on certain topic?

birch
Download Presentation

Knowledge Management through Ontologies

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Knowledge Managementthrough Ontologies Richard Benjamins - U. Amsterdam Dieter Fensel - U. Karlsruhe Asuncion Gomez Perez - U. Madrid

  2. Large distributed (multinational) enterprise Thousands employees Right person at the right place Who knows what? Who is expert on certain topic? Who was responsible for project Y? All persons who worked on project X between D1 and D2? Example HRM

  3. Overview • Knowledge management • Ontologies • Approach + proof of concept • Versus keyword-based retrieval • Discussions

  4. KM goals • Ensure accessibility of knowledge • Keep knowledge up to date • hard in central DB • Get intelligent access to enterprise knowledge • complex queries • intelligent answers

  5. Knowledge Management • Knowledge gathering • Knowledge organization and structuring • Knowledge refinement (maintenance) • Knowledge distribution

  6. Two types of KM systems • Vertical KM systems • enterprise specific • in-house • effective • Horizontal KM systems • general approach • applied for different companies • contracted

  7. Overview • Knowledge management • Ontologies • Approach + proof of concept • Versus keyword-based retrieval • Discussions

  8. Ontology • Representational vocabulary for some domain (classes, attributes, relations, axioms, inheritance) • Sharable across systems and organizations • As generic as possible (reusable) • Consensual (group agreement)

  9. Ontology example HRM • Classes • project-leader, employee, manager, skill, area-of-expertise • Relation • project-leader is-a employee • Axiom: • only managers can be project-leaders

  10. Overview • Knowledge management • Ontologies • Approach + proof of concept • Versus keyword-based retrieval • Discussions

  11. Proof of concept • (KA)2 • Knowledge Annotation Initiative of the Knowledge Acquisition Community • http://www.aifb.uni-karlsruhe.de/WBS/broker/KA2.html • Virtual organization • Similarities to HRM

  12. Annotating Ontology building web pages Experts Distributive or Joint effort Users Users centralized support IT-ers <html> <a onto= Ontology of Annotated "page: employee"> Web pages subject matter </a> </html> Intelligent query answer webcrawler How does it work?

  13. Technical infrastructure • Intranet/Internet • Browser • Relevant knowledge in HTML pages • or in format from which HTML can be generated

  14. Annotating Ontology building web pages Experts Distributive or Joint effort Users Users centralized support IT-ers <html> <a onto= Ontology of Annotated "page: employee"> Web pages subject matter </a> </html> Intelligent query answer webcrawler How does it work? - 1

  15. The KA ontology • Illustrate with browser: • Sub-ontologies in Ontolingua • Ontology Server of Stanford

  16. A consensual ontology • How to get agreement? • General part of ontology • reuse existing (university, publication) • KA specific ontology • Several groups of experts work on particular topic of knowledge acquisition (research topics)

  17. Annotating Ontology building web pages Experts Distributive or Joint effort Users Users centralized support IT-ers <html> <a onto= Ontology of Annotated "page: employee"> Web pages subject matter </a> </html> Intelligent query answer webcrawler How does it work? - 2

  18. Annotation of Web pages • HTML is only syntax • only keyword-based retrieval • Add some semantics • new attribute to anchor tag: onto • contains ontological information • Accessible for Intelligent webcrawler

  19. Annotation example <html> <head><TITLE> Mr. Paton </TITLE> <a ONTO="page:ProjectLeader"> </a> </head> <body> ..... <a ONTO="page[lastName=body]">Paton</a> ..... </body> </html>

  20. Annotation of Web pages

  21. <html> <head><TITLE> Richard Benjamins </TITLE> <a ONTO="page:Researcher"> </a> </head> <H1> <A HREF="pictures/id-rich.gif"> <IMG align=middle SRC="pictures/richard.gif"></A> <a ONTO="page[photo=href]" HREF="http://www.iiia.csic.es/~richard/pictures/richard.gif" ></a> <a ONTO="page[firstName=body]">Richard</a> <a ONTO="page[lastName=body]">Benjamins </a> </h1> <p> <A ONTO="page[affiliation=body]" HREF="#card"> Artificial Intelligence Research Institute (IIIA)</A> - <a href="http://www.csic.es/">CSIC</a>, Barcelona, Spain <br> and <br> <A ONTO="page[affiliation=body]" HREF="http://www.swi.psy.uva.nl/"> Dept. of Social Science Informatics (SWI)</A> - <A HREF="http://www.uva.nl/uva/english/">UvA</A>, Amsterdam, the Netherlands

  22. Annotating Ontology building web pages Experts Distributive or Joint effort Users Users centralized support IT-ers <html> <a onto= Ontology of Annotated "page: employee"> Web pages subject matter </a> </html> Intelligent query answer webcrawler How does it work? - 3

  23. Intelligent reasoning • Ontobroker • webcrawler • providers have to register • inference engine • query interface • Ontobroker: AIFB, University of Karlsruhe

  24. Demo • Illustrate in browser • registration and updating • facts • some queries • researchers (implicit knowledge) • editors • ….

  25. Overview • Knowledge management • Ontologies • Approach + proof of concept • Versus keyword-based retrieval • Discussions

  26. Ontology-based retrieval

  27. As opposed to • Keyword-based search

  28. Ontology versus keywords • No nonsense answers • Find exactly the piece you are looking for • Collect distributed information • research interests of a research group • Collect implicit information • axioms, e.g. cooperates-with is symmetric • Turn Intranet into a KBS

  29. Technical risks • No tools • ontology construction and maintenance • annotation support • query support • Maintenance of HTML annotations (instances) • Scaling up

  30. Social risks • Amount of participants • Competitive mentality • Incentive system

  31. In conclusion, KM • Knowledge gathering: HTML annotations • Knowledge organization and structuring: ontology • Knowledge refinement (maintenance): update HTML pages/annotations • Knowledge distribution: pull by intelligent crawler

More Related