Natural Language Access to Datavia

Deduction

Richard Waldinger

Artificial Intelligence Center

SRI International

Cognitive Science Institute

Speaker Series

18 February 2016

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natural language access to data

joint work

Cleo Condoravdi, Stanford University

Kyle Richardson, Stuttgart University

Asuman Suenbuel, SAP

Vishal Sikka, SAP (now Infosys)

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natural language access to data

the problem

accessing knowledge

from structured data sources.

via questions in natural language.

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natural language access to data

why is this hard?

natural language uncontrolled.

we want answers, not websites.

answers deduced or computed.

multiple databases.

sequence of ongoing queries.

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natural language access to data

what makes it easier?

we restrict ourselves to a well-understood subject domain.

business enterprise

we use already known databases.

access to SAP’s HANA database.

“Quest”

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sample query sequence

Show a company with a long-term debt within the last two years.

The debt is more than 5 million Euros.

It must be Swiss.

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why does this require reasoning?

query may be logically complex.

to resolve ambiguities in the query.

differences in vocabularies.

bridge the inferential leap.

compose the answer.

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approach (nl+deduction)

semantic parsing ⇒ semantic representation

transform ⇒ logical form

proof ⇒ answers

proof conducted in an axiomatic theory

theory contains links to databases.

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implementation of Quest

natural language processing by SAPL (Cascade Parser)

reasoning by SRI’s SNARK.

data from SAP’s HANA, Currency Conversion, Nationality Tables, etc.

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theorem prover (SNARK)

resolution (general reasoning).

paramodulation, rewriting (equality).

sorted unification.

answer extraction.

procedural attachment.

spatial and temporal reasoning.

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axiomatic subject domain theory

defines concepts in queries.

expresses capabilities of the databases.

provides background knowledge to relate them.

sort (type) structure

axioms

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sort structure

entity agent

company

time interval debt

numbermoney

size

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sorts of relations

company-has-debt(<company>, <debt>)

company-has-size(<company>, <size>)

within(<time interval>, <time interval>)

swiss(<agent>)

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parsing

based on PARC natural language technology (XLE + Bridge)

new parser (SAPL) written for Quest.

parser knows sort structure and sorts of relations.

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semantic parsing

query: Show a company with a high debt within the last two years.

semantic representation (partial):

(quant exists company7 sort company)

(quant exists debt3 sort debt)

(scopes-over company7 debt3)

(in nscope debt3

(company-has-debt company7 debt3))

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logical form

(exists ((company7 sort company)

(debt3 sort debt)

(time-interval5 sort time-interval))

(and (company-has-debt company7 debt3)

(within debt3 time-interval5)

(time-measure time-interval5 2 year)

(last time-interval5))

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procedural attachment

company-has-debt(?company, ?debt) ⇔

(exists (?location, ?size, ?dso, ….)

company-record(?company,

?debt,

?location,

?size,

?dso, ….) &

positive(?debt)

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sample data

name money location date

SL Foods Inc. $105263551.70 CH 2007 Sept. 1

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name: SL Foods Inc.

amount of debt: $105,263,551.70.

date debt incurred: Sept 1, 2007.

nationality: CH (Switzerland)

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the answer(s)

the debt of sl food inc. is high,

the debt of sl food inc. is within the interval from 9/1/2006 to 9/1/2008,

the duration of the interval from 9/1/2006 to 9/1/2008 is 2 years,

the interval from 9/1/2006 to 9/1/2008 is last.

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reasoning resolves ambiguity.

Show me a client with a high debt.It was within the last 2 years.

(“It” must be the debt).

It should be Swiss.(“It” must be the client)

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future work

we currently translate english questions into logical form.

we could also translate declarative sentences into logical form.

develop axiomatic theory from text.

domain experts need not know logic.

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reference

Natural Language Access to Data:

It Takes Common Sense

AAAI Symposium

Logical Formalizations of Common Sense Reasoning

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