Cooperative Query Answering

1. Cooperative Query Answering Based on a talk by Erick Martinez

2. 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

3. Cooperative Answer (CA) • A CA should be a correct, non-misleading, and useful answer to a query.

4. Q0:“Which students are enrolled?A0:“joana, jacob, shakil, …“ A0:“X. student(X)“ Grice's maxims • 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

5. 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.

6. QUERY / ANSWER SYSTEMS • Natural language interfaces • Databases (relational) • Logic programming and deductive databases(*)

7. 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

8. 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 Presuppositions: TECHNIQUES

9. Presuppositions: Lattice of sub-queries: 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.

10. 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.“

11. 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) . … Relaxation: 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) .

12. C6T:relax(flight(A,B) )  • serves_area(A, From), serves_area(B, To), travel(From, To) . Generalization: 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.

13. USER GOALS AND MODELS Types of knowledge about a user relevant to CA • Interests and preferences • Needs – user constraints (UC) • Goals and intent

14. 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

15. The End

16. 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

17. 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). …