1. AQUA: AQUAINT Question Answering System SAIC, San Diego KSL, Stanford

2. Agenda System Architecture System Architecture SAIC – Current Status and goals SAIC – Current Status and goals KSL – Current Status and goals KSL – Current Status and goals Future plans Future plans

3. SAIC-KSL-NMSU Collaborative System – Long term goal QUESTION NL Query Interlingua Query KIF Query KIF Answer Interlingua Answer NL Answer ANSWER NMSU Query Processor SAIC Interlingua  KIF Translator KSL Java Theorem Prover SAIC KIF  Interlingua Translator NMSU NL Generator

4. SAIC TASKS

5. Aligning Ontologies: the AQUA OntologyMapper Tool Dynamic semantic alignment tool developed to assist the user in performing this process Dynamic semantic alignment tool developed to assist the user in performing this process Automate mapping of Ontosem (source) ontology to Ontolingua (target) ontology Automate mapping of Ontosem (source) ontology to Ontolingua (target) ontology –Map classes and instances –Semantic alignment algorithm »Single-word entries »Multi-word entries Proved to be more difficult than expected. Proved to be more difficult than expected.

6. Single word matching algorithm Exact Matches Exact Matches Same Stem Same Stem Synonyms Synonyms –Exact Matches –Stem Matches 1-Generation Child Match 1-Generation Child Match No Match No Match – Display Ontolingua Ontology Tree » Parent Match  Child Match » Parent Match  3 levels if no Child Match Multiple Matches Multiple Matches – Display and Allow User To Select

7. Multi-word matching algorithm Exact Stem Match (A-B-C) Exact Stem Match (A-B-C) Exact Permutation (B-C-A, B-A-C, …) Exact Permutation (B-C-A, B-A-C, …) Ontolingua Term Contains Subset of Ontosem Words Ontolingua Term Contains Subset of Ontosem Words –Constrained to Parent Match Subtree Ontosem Term Contains Subset of Ontolingua Term Ontosem Term Contains Subset of Ontolingua Term –Constrained to Parent Match Subtree 1-Generation Child Match 1-Generation Child Match No Match No Match –Display Ontolingua Ontology Tree »Parent Match  Child Match »Parent Match  3 levels if no Child Match Multiple Matches Multiple Matches –Display and Allow User To Select

8. Initial Screen AQUA OntologyMapper

9. No Match – Parent Subtree Displayed AQUA OntologyMapper

10. Multiple Matches Found AQUA OntologyMapper

12. Current Approach to Semantic Alignment Two levels of analysis in SAIC semantic alignment software: Two levels of analysis in SAIC semantic alignment software: –Linguistic analysis: software matches terms by finding common word roots and combinations of words; also searches for synonyms –Structural analysis: software matches terms by finding common organization hierarchy in meaning representations

13. Potential Performance-Boosting Techniques Additional sources of linguistic analysis: Additional sources of linguistic analysis: –Documentation of concepts (often contains linguistic clues) –Ontolingua: “Conveyance -- vehicle for transporting people” –Ontosem: “Vehicle -- artifacts used for transporting people and cargo” Additional sources of structural information Additional sources of structural information –Slots/constraints Additional structure mapping techniques Additional structure mapping techniques –Such as Similarity Flooding* Federation of Matchers Federation of Matchers –Each individual alignment technique returns 15-50% of the terms matched, with little overlap –Individual techniques will be combined to find combined matches * Similarity Flooding – A versatile Graph Matching algorithm and its application to Schema Matching ” Sergei Melnick, Hector Garcia-Molina, Edgar Rahm

14. Translation from TMR to KIF: the AQUA Ontolingua Translator Once alignment has been performed the translation of all TMR output is performed. Once alignment has been performed the translation of all TMR output is performed. Some initial work has been done on this task and several articles have been extracted from text to TMR and TMR to KIF. Some initial work has been done on this task and several articles have been extracted from text to TMR and TMR to KIF. Functioning demonstration illustrating extracted TMR, KIF translation and question answering using JTP over extracted information Functioning demonstration illustrating extracted TMR, KIF translation and question answering using JTP over extracted information

15. Document and TMR

16. Generated KIF

17. Queries

18. Query results 1

19. Query Results 2

20. Future plans Translation: Translation: –Focus on improving the translation of TMR to KIF Extraction: Extraction: –Focus on extraction of temporal relations »Maximum utilization of KSL temporal reasoner development –Work with Onyx to acquire as many extracted TMR articles as possible in order to focus on TMR->KIF translation –Work with AQUAINT specified data sets for extracted articles. Leverage Genoa II work Leverage Genoa II work –Leverage ontologies/kbs developed for GENOA II Leverage Stanford KSL and IBM in the NIMD contract Leverage Stanford KSL and IBM in the NIMD contract –allow a comparison between 2 extraction techniques in the same question answering environment.

21. KSL Tasks

22. Backups

23. KSL/IBM NIMD contract award Stanford university has been awarded a NIMD contract where text extraction into the Ontolingua system Stanford university has been awarded a NIMD contract where text extraction into the Ontolingua system This is a natural fit with the current AQUA system. This is a natural fit with the current AQUA system. The design for the AQUA OntologyMapper is not ontology specific and is extensible to multiple ontologies The design for the AQUA OntologyMapper is not ontology specific and is extensible to multiple ontologies Leverage this design characteristic to incorporate an additional extraction system Leverage this design characteristic to incorporate an additional extraction system

24. Faculty Faculty Richard Fikes Edward Feigenbaum (Director) (Emeritus) (Director) (Emeritus) Knowledge Systems Laboratory Stanford University “In the knowledge is the power.” Senior Scientists Senior Scientists Deborah McGuinness Sheila McIlraith Deborah McGuinness Sheila McIlraith (Associate Director) (Associate Director) Technology for effectively representing and using knowledge in computer systems 12/4/02 Research Staff and Students Jessica Jenkins, Rob McCool, Paulo Pinheiro da Silva, Gleb Frank, …

25. Recent Developments JTP – A hybrid reasoner for query answering JTP – A hybrid reasoner for query answering –Developed reasoners for time-dependent knowledge –Expanded functionality of DAML+OIL reasoner DQL – Agent language and protocol for deductive query answering DQL – Agent language and protocol for deductive query answering Inference Web – Providing understandable explanations for derived query answers Inference Web – Providing understandable explanations for derived query answers

26. Recent Developments JTP – A hybrid reasoner for query answering JTP – A hybrid reasoner for query answering  Developed reasoners for time-dependent knowledge –Expanded functionality of DAML+OIL reasoner DQL – Agent language and protocol for deductive query answering DQL – Agent language and protocol for deductive query answering Inference Web – Providing understandable explanations for derived query answers Inference Web – Providing understandable explanations for derived query answers

27. JTP – A Hybrid Reasoner for Query Answering An architecture for hybrid reasoning An architecture for hybrid reasoning –First-order logic model elimination theorem prover –Suite of special purpose reasoners –Dispatchers and APIs for reasoners Developing special purpose query-answering reasoners Developing special purpose query-answering reasoners –Using time-dependent knowledge –Using Semantic Web knowledge expressed in DAML+OIL Available from the Web as a JAVA program Available from the Web as a JAVA programwww.ksl.stanford.edu/software/JTP

28. Representing Time-Dependent Knowledge A time ontology provides representation vocabulary A time ontology provides representation vocabulary –Objects »Primitive objects: time line, points, intervals, durations, … »Time units: second, minute, hour, … »Calendar objects: Monday, January, 2001, … –Relations »For points: location-of, before, after, equal-point, the-point, … »For intervals: precedes, meets, overlaps, co-starts, during, … –Abstractly specified time point locations E.g., “He was born in 1916” “She arrived in January 2002.”

29. Allen Relations on Time Intervals –Precedes: |—————|End-1 < Start-2 |——————| –Meets: |—————|End-1 = Start-2 |——————| –Overlaps: |————–|Start-1 < Start-2 < End-1 |——————| –Costarts:|————|Start-1 = Start-2 |——————| –During: |————|Start-2 < Start-1 |——————|End-1 < End-2 –Cofinishes:|————|End-1 = End-2 |——————| –Equal:|——————|Start-1 = Start-2 |——————|End-1 = End-2

30. Reasoning With Time-Dependent Knowledge The reasoner maintains a directed graph of time points The reasoner maintains a directed graph of time points –Based on the relations “before”, “after”, and “equal-point” –Includes intervals using their starting and ending points The reasoner operationalizes definitions of relations The reasoner operationalizes definitions of relations –Evaluates instances E.g., (Before A-Point-In-1942 A-Point-In-January-1968) –Infers instances of goals E.g., find intervals ?int such that (During ?int 1942) –Responds to assertions with additional inferred assertions E.g., (=> (and (starting-point ?s1 ?i1) (starting-point ?s2 ?i2) (starting-point ?s2 ?i2) (during ?i1 ?i2)) (during ?i1 ?i2)) (before ?s2 ?s1)) (before ?s2 ?s1))

31. Representing When Events Occur On 8 August 1998 a Taliban military offensive in northern Afghanistan concludes with the occupation of Mazar-e-Sharif. On 8 August 1998 a Taliban military offensive in northern Afghanistan concludes with the occupation of Mazar-e-Sharif. –(equal-point (starting-point August-8-1998) (starting-point August-8-1998) (the-point (year 1998) (month 7) (day 8) (hour 0) (the-point (year 1998) (month 7) (day 8) (hour 0) (minute 0) (second 0))) (minute 0) (second 0))) –(equal-point (ending-point August-8-1998) (ending-point August-8-1998) (the-point (year 1998) (month 7) (day 8) (hour 24) (the-point (year 1998) (month 7) (day 8) (hour 24) (minute 60) (second 60))) (minute 60) (second 60))) –(overlaps Taliban-military-offensive-in-northern-Afghanistan Taliban-military-offensive-in-northern-Afghanistan August-8-1998) August-8-1998) –(meets Taliban-military-offensive-in-northern-Afghanistan Taliban-military-offensive-in-northern-Afghanistan Occupation-of-Mazar-e-Sharif) Occupation-of-Mazar-e-Sharif)---offensive---|---occupation--- |---8/8/98--- |---8/8/98---

32. Begin Time Queries On 9 August Iran accuses the Taliban of taking 9 diplomats and 35 truck drivers hostage in Mazar-e-Sharif. The crisis began with that accusation. On 9 August Iran accuses the Taliban of taking 9 diplomats and 35 truck drivers hostage in Mazar-e-Sharif. The crisis began with that accusation. –(during Iran-accuses-Taliban-of-taking-hostages August-9-1998) August-9-1998) –(costarts Iran-accuses-Taliban-of-taking-hostages Iranian-Taliban-Crisis) Iranian-Taliban-Crisis)|--------8/9/98--------| |---accusation---| |---accusation---| |---crisis--- |---crisis--- “ When did the Iranian-Taliban crisis begin? ” “ August 9, 1998. ” “ When did the Iranian-Taliban crisis begin? ” “ August 9, 1998. ” –Query: (location-of (starting-point Iranian-Taliban-crisis) ?lower-bound ?upper-bound) ?lower-bound ?upper-bound) –Answer: ?lower-bound = Starting-Point-Of-August-9-1998 ?upper-bound = Ending-Point-Of-August-9-1998 ?upper-bound = Ending-Point-Of-August-9-1998

33. Duration Queries On 2 November Iran concludes the Zolfaghar-2 military exercise peacefully, ending the crisis between the two sides. On 2 November Iran concludes the Zolfaghar-2 military exercise peacefully, ending the crisis between the two sides. –(ends-during Zolfaghar-2 November-2-1998) –(cofinishes Zolfaghar-2 Iranian-Taliban-Crisis) ---Zolfaghar---| |---11/2/98---| |---11/2/98---| ---crisis---| ---crisis---| “ How many days did the Iranian-Taliban crisis last? ” “ 84 to 86. ” “ How many days did the Iranian-Taliban crisis last? ” “ 84 to 86. ” –Query: (duration-in-units Iranian-Taliban-crisis day ?lower-bound ?upper-bound) ?lower-bound ?upper-bound) –Answer: ?lower-bound = 84 ?upper-bound = 86 “ How many weeks did the Iranian-Taliban crisis last? ” “ 12. ” “ How many weeks did the Iranian-Taliban crisis last? ” “ 12. ” –Query: (duration-in-units Iranian-Taliban-crisis week ?lower-bound ?upper-bound) ?lower-bound ?upper-bound) –Answer: ?lower-bound = 12 ?upper-bound = 12.29

34. During Queries On 5 September Iran states that it has the right under international law to strike the Taliban after Iranian media sources report that the Taliban have killed 5 Iranian diplomats. On 5 September Iran states that it has the right under international law to strike the Taliban after Iranian media sources report that the Taliban have killed 5 Iranian diplomats. –(during Iran-declares-right-to-strike-Taliban September-5-1998) September-5-1998) –(precedes Iranian-media-reports-diplomats-killed-by-Taliban Iran-declares-right-to-strike-Taliban) Iran-declares-right-to-strike-Taliban) -------9/5/98-------- -------9/5/98-------- ---report---| |---declaration---| “ During what events did Iran declare the right to strike the Taliban under international law? ” “ The Iranian-Taliban crisis. ” “ During what events did Iran declare the right to strike the Taliban under international law? ” “ The Iranian-Taliban crisis. ” –Query: ( during Iran-declares-right-to-strike-Taliban ?evt) (type ?evt Event) (type ?evt Event) –Answer: ?evt = iranian-taliban-crisis

35. Recent Developments JTP – A hybrid reasoner for query answering JTP – A hybrid reasoner for query answering –Developed reasoners for time-dependent knowledge –Expanded functionality of DAML+OIL reasoner  DQL – Agent language and protocol for deductive query answering Inference Web – Providing understandable explanations for derived query answers Inference Web – Providing understandable explanations for derived query answers

36. DQL (DAML Query Language) Language and protocol for agent-to-agent query-answering Language and protocol for agent-to-agent query-answering –From knowledge represented in DAML+OIL (or OWL) –Supports a query-answering dialogue between a client and a server –Supports derivation of answers using automated reasoning –Knowledge may be in multiple distributed knowledge bases –Knowledge bases need not be specified by the client Design Issues Design Issues –The formal properties of queries and answers »How are queries, answers, and knowledge bases related? –Inferring answers may be expensive »Impractical to always try to compute all answers –Answers may only be known to exist –There may be an infinite number of answers –What are justifications and when should they be computed?

37. DQL Query-Answering Dialogue Client Query Answer Bundle (including a process handle) Server Continuation … Server Answer Bundle (including termination token(s)) Server Termination or Answer Bundle (including a process handle)

38. DQL Implementations by KSL XML syntax for DQL query-answering dialogues XML syntax for DQL query-answering dialogues Server for answering queries powered by JTP Server for answering queries powered by JTP Client for asking queries from a Web browser Client for asking queries from a Web browser –Enables humans to query a DQL server

39. Recent Developments JTP – A hybrid reasoner for query answering JTP – A hybrid reasoner for query answering –Developed reasoners for time-dependent knowledge –Expanded functionality of DAML+OIL reasoner DQL – Agent language and protocol for deductive query answering DQL – Agent language and protocol for deductive query answering  Inference Web – Providing understandable explanations for derived query answers

40. Trusting Query Answers Trusting an agent’s answers means that we trust: Trusting an agent’s answers means that we trust: –The input (and their sources) to the agent –The recency of the input –The inference rules in the agent’s reasoner(s) Automated reasoners provide little support for explaining query answers Automated reasoners provide little support for explaining query answers –Justifications are typically: »Unsharable »Difficult to visualize »Monolithic » In inappropriate notations »Difficult to combine » Difficult to refine »Reasoner-specific

41. Inference Web Framework for explaining reasoning results Framework for explaining reasoning results –Objective: Enable proofs and proof fragments provided by reasoners to be stored, exchanged, combined, annotated, filtered, segmented, compared, and rendered The Inference Web is currently composed of: The Inference Web is currently composed of: –Proof interlingua DAML+OIL/OWL specification of proofs that provides –Proof browser for displaying Inference Web proofs »Possibly from multiple reasoners –Proof parser to support segmentation and follow-up question support –Register for reasoners and inference rules –Prototype implementation using the JTP Reasoner

42. Recent Developments JTP – A hybrid reasoner for query answering JTP – A hybrid reasoner for query answering –Developed reasoners for time-dependent knowledge –Expanded functionality of DAML+OIL reasoner DQL – Agent language and protocol for deductive query answering DQL – Agent language and protocol for deductive query answering Inference Web – Providing understandable explanations for derived query answers Inference Web – Providing understandable explanations for derived query answers