Knowledge Discovery Based Query Answering in Hierarchical Information Systems

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Abstract

The paper concerns failing queries in incomplete Distributed Autonomous Information Systems (DAIS) based on attributes which are hierarchical and which semantics at different sites of DAIS may differ. Query q fails in an information system S, if the empty set of objects is returned as an answer. Alternatively, query q can be converted to a new query which is solvable in S. By a refinement of q, we mean a process of replacing q by a new relaxed query, as it was proposed in [2], [7], and [8], which is similar to q and which does not fail in S. If some attributes listed in q have values finer than the values used in S, then rules discovered either locally at S or at other sites of DAIS are used to assign new finer values of these attributes to objects in S. Queries may also fail in S when some of the attributes listed in q are outside the domain of S. To resolve this type of a problem, we extract definitions of such attributes at some of the remote sites for S in DAIS and next use them to approximate q in S. In order to do that successfully, we assume that all involved information systems have to agree on the ontology of some of their common attributes [14], [15], [16]. This paper shows that failing queries can be often handled successfully if knowledge discovery methods are used either to convert them to new queries or to find finer descriptions of objects in S.