Exploiting Background Information in Knowledge Discovery from Text

Ronen Feldman*, Haym Hirsh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


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, when background knowledge is available about the keywords labeling the documents FACT is able to use this information 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.

Original languageAmerican English
Pages (from-to)83-97
Number of pages15
JournalJournal of Intelligent Information Systems
Issue number1
StatePublished - 1997
Externally publishedYes

Bibliographical note

Funding Information:
This research was supported by NSF grant IRI-9509819 and by a grant from the Israeli Ministry of Sciences.


  • Association mining
  • Background knowledge
  • Constraint processing
  • Query languages
  • Textual databases


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