Providing ranked cooperative query answers using the metricized knowledge abstraction hierarchy

Cooperative Query Answering by Abstract Interpretation

“…The Cooperative query evaluation with negation operation: Most of the existing abstraction-based techniques [4,5,6,13,15] suffer from the problem of soundness when query contains negation operation M IN U S, because abstraction of the query appearing on right side of M IN U S may remove more information from the result of the query on left side of M IN U S, yielding to a result that does not satisfy the soundness. In this section, we discuss the way to treat negation operation so as to preserve the soundness.…”

“…In addition, none of these schemes enlightens the key issues: soundness, relevancy and optimality in the context of cooperativeness of the query answers. Most of the existing techniques [4,5,6,13,15] suffer from the problem of soundness when query contains negation operation MINUS. In case of conceptual or approximate queries where approximate results are desirable, none of the schemes focuses on the way to compute aggregate functions when appearing in a query so as to preserve the soundness.…”

“…These approaches suffer from high overhead when the degree of relaxation for a query is large, as the query abstraction-refinement process produces a large set of concrete queries to be issued to the database. To remedy this, fuzzy set theory or semantic distance approach is combined with abstraction hierarchy [5,13,15] to control the abstraction-refinement process and to provide a measure of nearness between exact and approximate answers.…”

“…To remedy such shortcomings and to enhance the effectiveness of the information retrieval, the notion of cooperative query answering [5,6,8,15] has been explored. The cooperative query answering system provides users an intelligent database interface that allows them to issue approximate queries independent to the underlying database structure and its content, and provides additional useful information as well as the exact answers.…”

Clustering-based fragmentation and data replication for flexible query answering in distributed databases

“…Some approaches have used taxonomies or ontologies for flexible query answering but did not consider their application for distributed storage of data: CoBase [26] used a type abstraction hierarchy to generalize values; Shin et al [27] use some specific notion of metric distance in a knowledge abstraction hierarchy to identify semantically related answers; Halder and Cortesi [28] define a partial order between cooperative answers based on their abstract interpretation framework; Muslea [29] discusses the relaxation of queries in disjunctive normal form. Ontology-based query relaxation has also been studied for non-relational data (like XML data in [30]).…”