From Question-Answering to Information-Seeking Dialogs Jerry R. Hobbs USC/ISI Marina del Rey, CA with Douglas Appelt, David Israel, Peter Jarvis, David

From Question-Answeringto Information-Seeking Dialogs

Jerry R. Hobbs

USC/ISI

Marina del Rey, CA

with Douglas Appelt, David Israel, Peter Jarvis, David Martin,Mark Stickel, and Richard Waldinger of SRI

Chris Culy

SRI International

Menlo Park, CA

12/04/02Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs 2

Decomposing Questions

Could Mohammed Atta have met with an Iraqi official between 1998 and 2001?

IE Engine

GeographicalReasoning

QuestionDecomposition

via Logical Rules

ResourceAttached toReasoning

Process

meet(a,b,t) & 1998 t 2001

at(a,x1,t) & at(b,x2,t) & near(x1,x2) & official(b,Iraq)

go(a,x1,t) go(b,x2,t)

IE Engine

TemporalReasoning

Logical Form

SNARK


The Problem

Inference in large knowledge bases is required for competent question-answering

Many rich but heterogeneous knowledge bases exist today

How do we make use of them in a single system?


Outline

Three Resources:

1. The Semantic Web: Teknowledge’s search engine ASCS

2. An Information Extraction Engine: SRI’s TextPro

3. An Ontology of Time: DAML-Time


DAML Search Engine

pred:

arg1:

arg2: Indonesia

?x

capital namespace

namespace

namespace

Searches entire(soon to be

exponentially growing)Semantic Web

Also conjunctive queries: population of capital of Indonesia

Problem: you have to know logic and RDF to use it.

Tecknowledge has developed ASCS:


DAML Search Engineas AQUAINT Web

Resource

pred:

arg1:

arg2: Indonesia

?x

capital namespace

namespace

namespace

Searches entire(soon to be

exponentially growing)Semantic Web

Solution: You only have to know English to use it; Makes the entire Semantic Web accessible to AQUAINT users.Also: Can use it for subqueries.

AQUAINT System

capital(?x,Indonesia)

procedural attachment in SNARK


Namespace Problem

Where to find the right predicates?

In QUARK: Subtheories linking predicates to namespaces Subtheories linking topics to namespaces

In DAML/ASCS: EQUIVALENT statements Standardized ontologies Use WordNet and SUMO to expand query Any namespace

Decreasingprecision

Decreasingprecision


Information ExtractionEngine as a Resource

Document retrieval for pre-processing

TextPro: Top of the line information extraction engine recognizes subject-verb-object, coref rels

Analyze NL query w GEMINI and SNARK

Bottom out in a pattern for TextPro to seek

Keyword search on very large corpus

TextPro runs over documents retrieved


Linking SNARK with TextPro

TextSearch(EntType(?x), Terms(p), Terms(c), WSeq)

& Analyze(WSeq, p(?x,c))

--> p(?x,c)

Call to TextPro

Type of questionedconstituent

Synonyms and hypernymsof word associated with p or c

Answer:Ordered sequence

of annotated strings of words

Match pieces of annotated answer strings with pieces of query

Subquery generated by SNARKduring analysis of query


Three Modes of Operationfor TextPro

1. Search for predefined patterns and relations (ACE-style) and translate relations into SNARK's logic

Where does the CEO of IBM live?

2. Search for subject-verb-object relations in processed text that matches predicate-argument structure of SNARK's logical expression "Samuel Palmisano is CEO of IBM."

3. Search for passage with highest density of relevant words and entity of right type for answer "Samuel Palmisano .... CEO .... IBM."

Use coreference links to get most informative answer

ACE Roleand AT

Relations


First Mode

TextSearch(Person, Terms(CEO), Terms(IBM), WSeq) & Analyze(WSeq, Role(?x,Management,IBM,CEO)) --> CEO(?x,IBM)

CEO(Samuel Palmisano,IBM)

Analyze

Entity1: {Samuel Palmisano, Palmisano, head, he}Entity2: {IBM, International Business Machines, they}Relation: Role(Entity1,Entity2, Management,CEO)

<relation TYPE=Role SUBTYPE=Management> <rel_entity_arg ID=“Entity1” ARGNUM=“1”/> <rel_entity_arg ID=“Entity2” ARGNUM=“2”/> <rel_attribute ATTR=“POSITION”>CEO</rel_attribute></relation>



1. Search for predefined patterns (MUC-style) and translate template into SNARK's logic Where does the CEO of IBM live?

2. Search for subject-verb-object relations in processed text that matches predicate-argument structure of SNARK's logical expression "Samuel Palmisano heads IBM."




Second Mode

TextSearch(Person, Terms(CEO), Terms(IBM), WSeq) & Analyze(WSeq, CEO(?x,IBM)) --> CEO(?x,IBM)

"<subj> Samuel Palmisano </subj> <verb> heads </verb> <obj> IBM </obj>"


Analyze



1. Search for predefined patterns (MUC-style) and translate template into SNARK's logic Where does the CEO of IBM live?

2. Search for subject-verb-object relations in processed text that matches predicate-argument structure of SNARK's logical expression "Samuel Palmisano is CEO of IBM."




Third Mode

TextSearch(Person, Terms(CEO), Terms(IBM), WSeq) & Analyze(WSeq, CEO(?x,IBM)) --> CEO(?x,IBM)

"<person> He </person> has recently been rumored to have been

appointed Lou Gerstner's successor as <CEOword> CEO </CEOword>of the major computer maker nicknamed <co> Big Blue </co>"


Analyze

"<person> Samuel Palmisano </person> ...."

coref


Challenges for IE

Cross-document identification of individuals Document 1: Osama bin Laden Document 2: bin Laden Document 3: Usama bin Laden

Do entities with the same or similar names represent the same individual?

Metonymy Text: Beijing approved the UN resolution on Iraq. Query involves “China”, not “Beijing”


Temporal Reasoning: Structure

Topology of Time: start, end, before, between

Measures of Duration: for an hour, ...

Clock and Calendar: 3:45pm, Wednesday, June 12

Temporal Aggregates: every other Wednesday

Deictic Time: last year, ...


Temporal Reasoning: Goals

Develop temporal ontology (DAML)

Reason about time in SNARK (AQUAINT, DAML)

Link with Temporal Annotation Language TimeML (AQUAINT)

Answer questions with temporal component (AQUAINT)

Nearly complete

In progress


Convergence

DAML Annotationof Temporal Information

on Web(DAML-Time)

Annotation of Temporal Information

in Text(TimeML)

Most information on Web is in text

The two annotation schemesshould be intertranslatable


TimeML Annotation Scheme(An Abstract View)

2001

6 mos

Sept 11

warning

clock & calendar intervals& instants

intervalsinclusion

beforedurations

instantaneousevents


TimeML Example

The top commander of a Cambodian resistance force said Thursdayhe has sent a team to recover the remains of a British mine removalexpert kidnapped and presumed killed by Khmer Rouge guerrillastwo years ago.

resist

command

sent recover

Thursday

said now

remove kidnap

2 years

presumed

killedremain


Vision for Time

Manual DAML temporal annotation of web resources

Manual temporal annotation of large NL corpus

Programs for automatic temporal annotation of NL text

Automatic DAML temporal annotation of web resources


Spatial and GeographicalReasoning: Structure

Topology of Space: Is Albania a part of Europe?

Dimensionality: How long/big is Chile?

Measures: How large is North Korea? Orientation and Shape: What direction is Monterey from SF?

Latitude and Longitude: Alexandrian Digital Library Gazetteer

Political Divisions: CIA World Fact Book, ...


Spatial and GeographicalReasoning: Goals

Develop spatial and geographical ontology (DAML)

Reason about space and geography in SNARK (AQUAINT, DAML)

Attach spatial and geographical resources (AQUAINT)

Answer questions with spatial component (AQUAINT)

Somecapability

now


Status and Future Directions

Basic architecture essentially complete

Good sampling of web and other resources have been incorporated

Focus on bulking up knowledge base relevant to domain (nonproliferation)

Focus on dialogue structure

Documents

From Question-Answering to Information-Seeking Dialogs Jerry R. Hobbs USC/ISI Marina del Rey, CA with Douglas Appelt, David Israel, Peter Jarvis, David