Richard Waldinger from SRI International presented this for the Cognitive Systems Institute Speaker Series on April 7, 2016. To hear a replay go to http://cognitive-science.info/community/weekly-update/

1. Natural Language Access to Data:
Where Reasoning Makes Sense
Richard Waldinger
Artificial Intelligence Center
SRI International
Cognitive Science Institute
Speaker Series
7 April 2016
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2. 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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3. natural language access to data
the problem
accessing knowledge
from structured data sources.
via questions in natural
language.
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4. 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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5. 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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6. waldinger natural language access to datawaldinger natural language access to data
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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7. waldinger natural language access to datawaldinger natural language access to data
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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8. waldinger natural language access to datawaldinger natural language access to data
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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9. waldinger natural language access to datawaldinger natural language access to data
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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10. waldinger question answering/ deductionwaldinger question answering/ deduction
theorem prover (SNARK)
resolution (general reasoning).
paramodulation, rewriting (equality).
sorted unification.
answer extraction.
procedural attachment.
spatial and temporal reasoning.
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11. waldinger natural language access to datawaldinger natural language access to data
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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12. waldinger question answering/ deductionwaldinger question answering/ deduction
sort structure
entity
agent
company
time interval
debt
number
money
size
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13. waldinger question answering/ deductionwaldinger question answering/ deduction
sorts of relations
company-has-debt(<company>, <debt>)
company-has-size(<company>, <size>)
within(<time interval>, <time interval>)
swiss(<agent>)
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14. waldinger natural language access to datawaldinger natural language access to data
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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15. waldinger question answering/ deductionwaldinger question answering/ deduction
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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16. waldinger question answering/ deductionwaldinger question answering/ deduction
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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17. waldinger question answering/ deductionwaldinger question answering/ deduction
axiom: definition of high debt
high(debt-record(?company, ?money,…))
⇔
?money > dollars(1000000)
i.e., a debt is high if its money amount is
greater than 1 million dollars.
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18. waldinger question answering/ deductionwaldinger question answering/ deduction
axiom: company has debt
company-has-debt(?company, ?debt) ⇔
(exists (?location, ?size, ?dso, ….)
company-record(?company,
?debt,
?location,
?size,
?dso, ….) &
positive(?debt)
procedural attachment
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19. waldinger nl access to datawaldinger nl access to data
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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20. waldinger question answering/ deductionwaldinger question answering/ deduction
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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21. waldinger question answering/ deductionwaldinger question answering/ deduction
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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22. waldinger question answering/ deductionwaldinger question answering/ deduction
crowd-sourced axiomatic theories
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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23. waldinger question answering/ deductionwaldinger question answering/ deduction
other future work
other domains.
spoken input.
efficiency.
changing data bases.
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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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