Using Dynamically Acquired Background Knowledge for Information Extraction and Intelligent Search

Abstract

This chapter presents a simple framework for extracting information found in publications or documents that are issued in large volumes and which cover similar concepts or issues within a given domain. The general aim of the work described is to present a model for automatically augmenting segments of these documents with metadata, using dynamically acquired background domain knowledge to help users easily locate information within these documents through a structured front end. To realize this goal, both document structure and dynamically acquired background knowledge are utilized. A real life example where these ideas have been applied is also presented.