A Textual Case-Based Reasoning Framework for Knowledge Management Applications

1. A Textual Case-Based Reasoning Framework for Knowledge Management Applications Rosina Weber David W. Aha, Nabil Sandhu, Héctor Muñoz-Avila Decision Aids Group Navy Center for Applied Research in Artificial Intelligence Naval Research Laboratory University of Wyoming German Workshop on CBR March 15, 2001

2. Outline • Introduction • Knowledge Management Systems • Knowledge artifacts • Lessons Learned Systems • Motivation • Methodology NEO domain • Case Representation • Elicitation Tool • Extraction Tool • Monitored Distribution • Domain Ontology • Problems vs. Solutions • Next Steps

3. Knowledge Management Systems KMS manipulate knowledge to... CORPORATE MEMORY DOCUMENTS KNOWLEDGE ARTIFACTS ….storing, distribute, collect, validate, apply, create, sharing & leveraging knowledge

4. Knowledge artifacts • are structured formalisms that imply essential elements of knowledge for reuse (e.g., when to reuse, what to reuse) • well understoodand accepted • lessons learned • alerts • best practices • incident reports Alert systems Lessons Learned systems: our current focus

5. Lessons • refer to one task/activity/decision of a process • originate from successes, failures, or advice • teach something about a work practice that has the potential to generate a positive impact in the targeted process when reused reuse components Weber et al., 2001 Intelligent lessons learned systems. International Journal of Expert Systems Research & Applications, Vol. 20, No. 1, Jan 2001, 17-34. when to reuse? • task/contextual info about the process • main index guiding distribution under which conditions? • what is required for the lesson to be applicable? indexing why? • what was the originating event • success/failure/advice • cause what to reuse? • what to repeat or avoid solution

6. Motivation (i) • LLS are not used • lessons are distributed outside the context of reuse • lessons are collected in textual descriptions, so they are: • poorly represented • difficult to be retrieved • & difficult to be interpreted

7. reusing knowledge artifacts artifacts disseminated in the context of external distribution systems (DS) in terms of reuse elements Knowledge artifacts as cases human users text elicitation tool text extraction tool Challenge: Natural language Motivation (ii) share knowledge Domain Ontology + Subset of NL

8. human users textual documents • Elicitation Tool • Extraction tool • Case Representation • Monitored Distribution • Domain Ontology elicitation tool extraction tool domain specific ontology artifacts in the format of ext distribution system format of external distribution system case base TCBR Methodology for Knowledge Management Systems that manipulate knowledge artifacts Why CBR? Why textual? .

9. Noncombatant Evacuation Operations-NEO: military operations to evacuate noncombatants whose lives are in danger to a safe haven Noncombatant Evacuation Operations: military operations to evacuate noncombatants whose lives are in danger to a safe haven HQ Assembly Point ISB safe haven

10. HQ ISB NEO site safe haven Noncombatant Evacuation Operations (NEO) Assembly Point

11. TCBR Methodology for Knowledge Management Systems that manipulate knowledge artifacts • Case Representation • Elicitation Tool • Extraction tool • Monitored Distribution • Domain Ontology

12. Case Representation Example: 1. When/Where to reuse (which task): Registering evacuees Context/Process: NEO operation 2. Under which conditions: The weather is hot and humid. The location is a tropical country. 3. What to reuse: Make sure to avoid registration in 3 steps. 4. Why (originating event): We implemented registration in 3 steps. Success/Failure: It was a failure. Why? It was very time consuming. It caused evacuee discomfort. Additional elements provided by the domain ontology.

13. Case Representation Requirements: • Indentify the audience style • Identify reuse & retrieve components: knowledge, process, conditions of applicability, explanation • Identify the format of components • Identify relationships

14. Elicitation Tool What: • The lesson elicitation tool LET guides and educates users to submit lessons in the CR • It orients with examples and reduces the amount of text to type by giving drop-down lists to select from • It requests confirmations to orient the user to rethink the experience to be communicated • A domain ontology supports disambiguation at run-time (do not store unless relevant) • Uses a subset of NL based on the CRF by using a template-based elicitation with pre-defined grammar structures to overcome NLP problems

15. Example: Elicitation Tool Requirements: • in connectivity with the domain ontology • be supported by lexicons of expressions, domain entities and verbs • support conversation to acquire new concepts for the ontology

16. Extraction Tool What: • converts texts into knowledge artifacts • template mining • variant of Information Extraction • search for specific descriptions in selected excerpts of text (structure) • avoids NLP techniques • uses methods that contain knowledge of where to search and what to extract

17. Extraction Tool Requirements: • Source text must follow stereotypical style • Source text must have some structure that allows identification of a rhetorical structure • Domain of source text is known

18. Extraction Tool Example: Method converting textual lessons into the case representation framework: “In field recommended action, search for expressions such as (in this order): make sure , ensure, should. When (if) one of these is found, extract content beginning right after the expression found until the next period.”

19. Example: Monitored Distribution What: • a framework to solve the lesson distribution gap • disseminate knowledge in the context of targeted processes (just in time knowledge) • infrequent variable experiential knowledge • allows executable implementation of knowledge

20. Evaluation: We have evaluated the monitored distribution in two domains: domain/measure no lessons with lessons reduction 5% travel duration 9h45 9h14 NEO duration 39h50 32h48 18% NEO casualties 11.5 8.7 24% Monitored Distribution Requirements: The conversion of the knowledge artifacts into the format of the external distribution systems.

21. Domain Ontology What: • A hierarchical model of domain knowledge where concepts are organized according to their commonalities and meaning • It supports the CR, similarity assessment, knowledge elicitation, text extraction, and the conversion of artifacts into the format of external distribution systems • We are currently investigating corpus analysisto learn lexicons, concepts, and relations from about 40,000 lessons

22. Domain Ontology Example: Condition complement: “ it is a disaster relief operation.” Operation cause: “ disaster relief” Operation hostility level: “permissive” to the “hostility level”. Requirements: • Knowledge acquisition from domain experts • Automatic acquisition

23. Next Steps • learning ontology • support conversation to acquire new concepts for the ontology • evaluating the elicitation tool • implementing text extraction for all reuse components • evaluating extraction tool

24. Fourth International Conference on CBR 30 July – 2 August 2001 Vancouver, BC (Canada) Premiere CBR meeting Industry Day Exhibition 5 Workshops Great social schedule! www.iccbr.org/iccbr01 Chair: Qiang Yang Program Chairs: David W. Aha, Ian Watson Workshop Chairs Rosina Weber & Cristiane Gresse von Wangenheim 1. Process-Oriented KM Kurt D. Fenstermacher, Carsten Tautz 2. Soft Computing Simon C.K. Shiu 3. Authoring Support David Patterson, Agnar Aamodt, Barry Smyth  4. Creative SystemsCarlos Bento, Amilcar Cardoso  5. CBR in E-Commerce Robin Burke