Probabilistic Logical Information Retrieval for Content, Hypertext, and Database Querying

Classical retrieval models support content-oriented searching for documents using a set of words as data model. However, in hypertext and database applications we want to consider the link structure and attribute values of documents in addition to the pure content. In this paper, we present a framework based on probabilistic logical retrieval for describing the retrieval function for a query which refers to the content of documents, to the hypertext structure of documents, and to the database attribute values of documents. The challenge is to find a retrieval function which yields welldefined retrieval weights for ranking the documents with respect to a combination of the query criteria. We demonstrate the implementation and evaluation of our approach using HySpirit, a prototypical system of a probabilistic deductive database.