From Question-Answering to Information-Seeking Dialogs

From Question-Answeringto Information-Seeking Dialogs

Jerry R. Hobbs

Artificial Intelligence Center

SRI International

Menlo Park, California

(with Douglas Appelt, Chris Culy, David Israel, David Martin, Srini Narayanan, Mark Stickel, and Richard Waldinger)

12/03/01Principal Investigator: Jerry R. Hobbs, SRI International 2

Key Ideas

1. Logical analysis/decomposition of questions into component questions, using a reasoning engine

2. Use of component questions to drive subsequent dialogue, for elaboration, revision, and clarification

3. Bottoming out in variety of web resources and information extraction engine

4. Use of analysis of questions to determine, formulate, and present answers.


Composition of Informationfrom Multiple Sources

How far is it from Muscat to Kandahar?

What is the lat/longof Muscat?

What is the distancebetween the two lat/longs?

What is the lat/longof Kandahar?

AlexandrianDigital Library

Gazetteer Geographical Formula

orwww.nau.edu/~cvm/latlongdist.html

QuestionDecomposition

via Logical Rules

ResourcesAttached toReasoning

Process

AlexandrianDigital Library

Gazetteer

GEMINI

SNARK


An Information-Seeking ScenarioHow safe is the Muscat harbor for refueling US Navy ships?

What recent terroristincidents in Oman?

Are relations betweenOman and US friendly?

How secure is the Muscat harbor?

IR + IE Enginefor searching

recent news feeds

Find map of harborfrom DAML-encoded

Semantic Web/Intelink

Ask Analyst

QuestionDecomposition

via Logical Rules

ResourcesAttached toReasoning

Process

Asking Useris one suchResource


A Complex Query

What recent purchases of suspicious equipment has XYZ Corp or its subsidiaries or parent firm made in foreign countries?


A Complex Query


Purchase: Agent: Patient: Date: Location:

Open DomainInformationExtraction

System


A Complex Query


subsidiary(x,y)

parent(y,x)

Purchase: Agent: XYZ, ABC, DEF, ... Patient: Date: Location:

Subsidiaries:XYZ: ABC, ...DEF: ..., XYZ, ...

DB


A Complex Query


illegal

biowarfarePurchase: Agent: XYZ, ABC, DEF, ... Patient: anthrax, ... Date: Location:

DB of bio-equip

User Model


A Complex Query


Ask User

Purchase: Agent: XYZ, ABC, DEF, ... Patient: anthrax, ... Date: since Jun01 Location:


A Complex Query


not USA

Purchase: Agent: XYZ, ABC, DEF, ... Patient: anthrax, ... Date: since Jun01 Location: --

FilterInformation Extraction

System invoked


A Complex Query


subsidiary(x,y)

parent(y,x)


illegal

biowarfare

DB of bio-equip

Ask User not USA

Purchase: Agent: XYZ, ABC, DEF, ... Patient: anthrax, ... Date: since Jun01 Location: --


A DialogWhat recent purchases of suspicious equipment has XYZ Corp made?

illegal

biowarfare

DB of bio-equip

Ask User

Purchase: Agent: XYZ Patient: anthrax, ... Date: since Jun01 Location:


A DialogWhat recent purchases of suspicious equipment has XYZ Corp made?How about its subsidiaries or parent firm?

illegal

biowarfare

DB of bio-equip

Ask User

Purchase: Agent: ABC, DEF, ... Patient: anthrax, ... Date: since Jun01 Location:

subsidiary(x,y)

parent(y,x)



Parsing Queries

Use GEMINI Parser, Grammar, and Semantic Interpretation

Complete for fronted wh-questions, yes-no questions

Minor augmentations may be necessary

Map current logical forms into SNARK expressions


Resolving Indeterminacies

Determine precise interpretation of vague query:

Specific interpretations for general predicates

Resolving coreference and syntactic ambiguities

Expanding metonymies

Interpreting simple metaphors

Use abduction capabilities of SNARK: Proof of query is answer, but also more precise interpretation of query


Decomposing Questions

Decompose questions into subquestions via inference: P1 & P2 --> Q

Axioms direct system toward specific available resources


Articulating with Resource Ontologies

Axioms link language of general reasoning with language used by specific resource:

Pspec --> Pgen

Possible resources: Specific highly useful web sites DAML-encoded web sites in general FASTUS-based open-domain information extraction engine Quick&dirty Q&A system


Invoking Resources

Resources will be invoked through

Procedural attachment features of SNARK

Open Agent Architecture (OAA)


Encoding Axioms

Multiple knowledge bases will be used and linked to through the same kind of articulation axioms.

Possible knowledge bases: CIA World Fact Book (partially axiomatized) DAML ontologies Structural Evidential Argumentation System (SEAS) (Genoa program) HPKB and RKF knowledge bases Subsets of CYC Core theories in some critical domains will be developed in this project


Filtering Responses from Resources

Often an analyst requires specific information, but the resource can only be queried on general criteria

Inference engine will filter answers from resource by more specific constraints


Structuring Answers

Recompose answers to subquestions into answer to whole, using the structure of the proof graph that decomposed the question

Construct hierarchically organized answers

Enables drill-down and explanation


Structuring Dialog

Proof structure of query is likely to be mirrored by structure of subsequent dialog -- details, fine modifications, clarifications, and elaborations

Query may be a subquestion in a larger query that emerges in subsequent dialog -- proof tree of subsequent queries locates place for earlier query


Contributions:AQUAINT Areas of

Interest

Question-Answering: by inferential question-decomposition, bottoming out in calls to web and other resources. Information-seeking dialogs with subtopics driven by question-decomposition.

Determining the Answer: by using the proof graph to recompose the information discovered in question analysis.

Formulating and Presenting the Answer: by using the proof graph to structure the responses.


Contributions:Enabling Technologies

Advanced Reasoning for Question-AnsweringInteractive Question-AnsweringRole of Context

Sharable Knowledge SourcesContent RepresentationRole of Knowledge

Language Processing

Our Primary Fociin AQUAINT

Related SRI Projects+ Strong Background

Strong Background+ Strong Software Base+ Secondary Focus in

AQUAINT


Summary

Inference provides a uniform framework for

Analyzing questions into their components

Linking to external resources

Recomposing answers

Structuring information-seeking dialogs

From Question-Answering to Information-Seeking Dialogs

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