Mining Associations in Text in the Presence of Background Knowledgein the Presence of Background Knowledge

This paper describes the FACT system for knowledge discovery from text. It discovers associations patterns of co-occurrence amongst keywords labeling the items in a collection of textual documents. In addition, FACT is able to use background knowledge about the keywords labeling the documents in its discovery process. FACT takes a query-centered view of knowledge discovery, in which a discovery request is viewed as a query over the implicit set of possible results supported by a collection of documents, and where background knowledge is used to specify constraints on the desired results of this query process. Execution of a knowledge-discovery query is structured so that these background-knowledge constraints can be exploited in the search for possible results. Finally, rather than requiring a user to specify an explicit query expression in the knowledge-discovery query language, FACT presents the user with a simple-to-use graphical interface to the query language, with the language providing a well-defined semantics for the discovery actions performed by a user through the interface.