Cooperative Query Answering

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Cooperative Query Answering

• Erick Martinez
• Nov. 19, 2002

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MOTIVATION

• Responses to queries posed by a user of a
  database do not always contain the information
  required
• DB and information systems are often hard to use
  because they do not explicitly attempt to
  cooperate with their users. They answer
  literally the queries posed to them
• A user might need more information than
  requested, or might actually need different
  information
• An answer with extra or alternative information
  may be more useful and less misleading to a user

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Cooperative Answer (CA)

• A CA should be a correct, non-misleading, and
  useful answer to a query.

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Grice's maxims
Q0 Which students are enrolled?A0 joana,
jacob, shakil, A0 ?X. student(X)

• Maxim of Quality a system should never give an
  answer which might mislead the user

• Maxim of Quantity an answer should not be more
  informative, or more detailed, than necessary
• Maxim of Relation an answer should be always
  relevant to the user who asked the question
• Maxim of Manner an answer should not be
  ambiguous, leaving the user with choices to make
  about its meaning

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Database Stonewalling

• Q1 "Who passed COSC6115 in the winter semester
  of 2001?A1 No one

• Q2 "Who failed COSC6115 in the winter semester
  of 2001?A2 No one
• Q3 "Who taught COSC6115 in the winter semester
  of 2001?A3 No one"
• DB stonewall - will answer a yes/no question
  with a yes or no regardless of whether the answer
  is misleading.

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QUERY / ANSWER SYSTEMS

• Natural language interfaces
• Databases (relational)
• Logic programming and deductive databases()

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Deductive Databases (DDB)

• Distinction between data and
• knowledge
• Data represented in the EDB, and knowledge in the
  IDB and IC.
• Knowledge is the semantics of the DB, that which
  must be true of the DBs state, and the logical
  conclusions that must follow from given data

• A deductive database consists of
• three parts
• Facts the set of all facts constitute the
  extensional database (EDB)
• Rules the set of rules constitute the
  intensional database (IDB)
• Integrity constraints (IC) the set of logical
  formula that must be true of the database
• e.g. IC0 ? enrolled_in(X, Y),
• not student(X).

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TECHNIQUES

• Evaluation of presuppositions in a query()
• Detection and correction of misconceptions in a
  query()
• Relaxation and generalization of queries and
  responses()
• Consideration of specific information about a
  user's state of mind
• Formulation of intensional answers

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Presuppositions
TECHNIQUES

• Usually, asking a query not only presupposes the
  existence of all components of the query, but
  also presupposes an answer to the query itself.
• i.e. "Which employees own red cars?
• Q4 ? emp(X), owns(X,Y), car(Y), red(Y).

• Two atoms in a query are joined if they share a
  variable.
• A query is connected if every two atoms in the
  query are connected.
• 2n - 2 sub-queries for a conjunctive query with n
  atoms (exp. cost)
• ? Algorithm Report the smallest sub-queries
  that fail, considering only connected sub-queries

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Lattice of sub-queries
Presuppositions
TECHNIQUES

• If a sub-query has no answers, the query cannot
  have any answers either (scalar implicature)
• Finding presuppositions (failed sub-queries) is
  independent of domain specific knowledge.

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Misconceptions
TECHNIQUES

• Integrity constraints
• IC1 ? professor(X), student(X).IC2 ?
  enrolled_in(X, Y), not student(X).
• Query
• "Which professor is enrolled in COSC6115?
• Q5 ? professor(X), enrolled_in(X, COSC6115).

• Answer
• No one is both a professor and a student.
  Anyone who is enrolled in a class is a student.
  So no one is a professor and enrolled in class.

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Relaxation
TECHNIQUES

• Taxonomy clause
• C6 travel(From, To) ?
• serves_area(A, From),
• serves_area(B, To),
• flight(A,B) .
• Reciprocal clause
• C6T relax(flight(A,B) ) ?
• serves_area(A, From), serves_area(B, To),
  travel(From, To) .

• Relaxation step let ? be a
• substitution after unifying atom in
• goal with key () in the taxonomy
• clause
• Apply ? across the taxonomy clause.
• Replace the query atom with the head atom of the
  taxonomy clause.
• Add the non-key literals from the body of the
  taxonomy clause to the new query as constraints
  on the variables.

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Relaxation
C6 travel(From, To) ? serves_area(A,
From), serves_area(B, To), flight(A,B) . C6T
relax(flight(A,B) ) ? serves_area(A,
From), serves_area(B, To), travel(From, To) .
TECHNIQUES

• Original query
• Q6 ? flight(Dulles, Orly).
• Q6r ? relax (flight(Dulles, Orly)).
• Relaxing via reciprocal clause C6T
• Q6r ? serves_area(Dulles, From),
  serves_area(Orly, To), travel(From,
  To) .
• Resolving with taxonomy clause C6
• Q6r ? serves_area(Dulles, From),
  serves_area(Orly, To),
• serves_area(A, From), serves_area(B,
  To), flight(A, B) .

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Relaxation
TECHNIQUES

• Q6r ? serves_area(Dulles, From),
  serves_area(Orly, To),
• serves_area(A, From), serves_area(B,
  To), flight(A, B).
• When A Dulles and B Orly, solving
• flight(Dulles, Orly) again and will get the
  same answers
• When A ? Dulles and B ? Orly, will get new
  answers
• From Washington, D.C. and
• serves_area(A, Washington, D.C.) will be
  satisfied by A National, A
  BWI

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Generalization
C6T relax(flight(A,B) ) ? serves_area(A,
From), serves_area(B, To), travel(From, To) .
TECHNIQUES

• Relaxation is strictly a syntactic notion, a
  rewrite mechanism. Generalization is a semantic
  counterpart to relaxation.
• Literal answers to the relaxed query should
  include answers to the original query, plus some
  new neighbourhood answers with respect to the
  original query.

• After applying relaxation a new query is a
  generalization only if all the non-key atoms are
  satisfied whenever the key atom is satisfied.
  (conservative reciprocal clause)
• When all reciprocal clauses are conservative,
  resolution over a relaxed query will produce all
  the answers of the original query.

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USER GOALS AND MODELS

• Types of knowledge about a user relevant to CA
• Interests and preferences
• Needs user constraints (UC)
• Goals and intent

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MY KEY POINTS

• CA is mostly intended for DDB as a platform.
• For RDB, a deductive database interface should be
  implemented on top of any relational system.
• The system should support natural language input
  to some extend for some domains (the natural
  language translator generates a logical query)
• The system should produce natural language
  responses
• CA techniques, in particular relaxation, can
  useful for applications like Internet queries
• It is not evident that first order logic can
  serve as an adequate ontology for CA

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The End

• Thats thats thats all folks

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A CA SYSTEM (at U of Maryland)

• Uniform system
• Defined and implemented through logic
• Uniform representation and support for all
  cooperative methods
• Portable
• General approach for RDB, DDB and logic programs
• Domain-independent
• Natural language interface
• Accept natural language queries
• Provide cohesive and coherent responses in
  natural language

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Deductive Database Structure

• EDB prerequisite(MATH-300, MATH-350).
  prerequisite(MATH-350, MATH-400).
• teaches(smith, MATH-400).
• IDB teaches(X, Y) ? teaches(X, Z) ,
• prerequisite(Y, Z).
• IC ? enrolled_in(X, Y), not student(X).