Knowledge Integration for Structured

Knowledge integration is the integration of distributed, heterogeneous databases, such as those available on the World Wide Web. In this paper we will consider a new type of knowledge integration problem, namely, the problem of combining information from relations that lack common object identiiers. A general technique for this problem is proposed, based on well-studied similarity measures for text, and the observation that Web-based databases often present their information to the end user through a veneer of text. We describe an extension of Datalog called WHIRL which allows passages of ordinary text to be used as keys. WHIRL supports documents as a built-in type, similarity reasoning with a built-in predicate, and answers every query with a list of answer substutitions that are ranked according to an overall similarity score. Experiments with a prototype implementation show that WHIRL is eecient for databases with tens of thousands of tuples, and produces highly accurate results for many queries.