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

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. 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 R.Weber, NCARAI-NRL, U. of Wyoming Knowledge Management Systems KMS manipulate knowledge to... ….storing, distribute, collect, validate, apply, create, sharing & leveraging knowledge CORPORATE MEMORY DOCUMENTS KNOWLEDGE ARTIFACTS

4. 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 4 R.Weber, NCARAI-NRL, U. of Wyoming Knowledge artifacts are structured formalisms that imply essential elements of knowledge for reuse (e.g., when to reuse, what to reuse) well understood and accepted lessons learned alerts best practices incident reports Alert systems Lessons Learned systems: our current focus

5. 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 5 R.Weber, NCARAI-NRL, U. of Wyoming why? what was the originating event success/failure/advice cause when to reuse? task/contextual info about the process main index guiding distribution 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 what to reuse? what to repeat or avoid under which conditions? what is required for the lesson to be applicable? reuse components indexing solution Weber et al., 2001 Intelligent lessons learned systems. International Journal of Expert Systems Research & Applications, Vol. 20, No. 1, Jan 2001, 17-34. Weber et al., 2001 Intelligent lessons learned systems. International Journal of Expert Systems Research & Applications, Vol. 20, No. 1, Jan 2001, 17-34.

6. 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 6 R.Weber, NCARAI-NRL, U. of Wyoming 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. 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 7 R.Weber, NCARAI-NRL, U. of Wyoming Motivation (ii) in terms of reuse elements artifacts disseminated in the context of external distribution systems (DS) reusing knowledge artifacts share knowledge Knowledge artifacts as cases text extraction toolelicitation tool Domain Ontology + Subset of NL Challenge: Natural language human userstext

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

9. 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 9 R.Weber, NCARAI-NRL, U. of Wyoming Noncombatant Evacuation Operations-NEO: military operations to evacuate noncombatants whose lives are in danger to a safe haven Assembly Point HQ ISB safe haven Noncombatant Evacuation Operations : military operations to evacuate noncombatants whose lives are in danger to a safe haven

10. 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 10 R.Weber, NCARAI-NRL, U. of Wyoming Noncombatant Evacuation Operations (NEO) Assembly Point NEO site safe haven ISB HQ

11. 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 11 R.Weber, NCARAI-NRL, U. of Wyoming Case Representation Elicitation Tool Extraction tool Monitored Distribution Domain Ontology TCBR Methodology for Knowledge Management Systems that manipulate knowledge artifacts

12. 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 12 R.Weber, NCARAI-NRL, U. of Wyoming 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. 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. 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 13 R.Weber, NCARAI-NRL, U. of Wyoming Requirements: Indentify the audience style Identify reuse & retrieve components: knowledge, process, conditions of applicability, explanation Identify the format of components Identify relationships Requirements: Indentify the audience style Identify reuse & retrieve components: knowledge, process, conditions of applicability, explanation Identify the format of components Identify relationships Case Representation

14. 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 14 R.Weber, NCARAI-NRL, U. of Wyoming 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 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. 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 15 R.Weber, NCARAI-NRL, U. of Wyoming 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 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 Example: Example:

16. 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 16 R.Weber, NCARAI-NRL, U. of Wyoming 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 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 Extraction Tool

17. 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 17 R.Weber, NCARAI-NRL, U. of Wyoming 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 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 Extraction Tool

18. 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 18 R.Weber, NCARAI-NRL, U. of Wyoming 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.” 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.” Extraction Tool

19. 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 19 R.Weber, NCARAI-NRL, U. of Wyoming 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 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 Example:

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

21. 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 21 R.Weber, NCARAI-NRL, U. of Wyoming 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 analysis to learn lexicons, concepts, and relations from about 40,000 lessons 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 analysis to learn lexicons, concepts, and relations from about 40,000 lessons

22. 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 22 R.Weber, NCARAI-NRL, U. of Wyoming Domain Ontology Example: Condition complement: “ it is a disaster relief operation.” Operation cause: “ disaster relief” Operation hostility level: “permissive” to the “hostility level”. 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 Requirements : Knowledge acquisition from domain experts Automatic acquisition

23. 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 23 R.Weber, NCARAI-NRL, U. of Wyoming 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 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 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