natural language access to data via deduction
TRANSCRIPT
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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