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In this paper we give a synoptic view of the growth of the text processing technology of information extraction (IE) whose function is to extract information about a pre‐specified set of entities, relations or events from natural language texts and to record this information in structured representations called templates. Here we describe the nature of the IE task, review the history of the area from its origins in AI work in the 1960s and 70s till the present, discuss the techniques being used to carry out the task, describe application areas where IE systems are or are about to be at work, and conclude with a discussion of the challenges facing the area. What emerges is a picture of an exciting new text processing technology with a host of new applications, both on its own and in conjunction with other technologies, such as information retrieval, machine translation and data mining.

Seeks to explore the gap that exists between knowledge management (KM) systems and the natural language materials that form almost all corporate data stores.

A conceptual discussion and approach are taken using recent scientific results in the fields of the semantic web and ontology‐based information extraction.

Provides a high‐level introduction to information extraction (IE) and descriptions of application scenarios for KM tools that exploit IE, a form of natural language analysis to link semantic web models with documents. The paper presents some examples of ontology‐based IE systems, one of which, KIM, is under development in the SEKT Project. KIM offers IE‐based facilities for metadata creation, storage and conceptual search. The system can be used by diverse applications for annotating and querying documents.

Focuses on technologies and facilities that will become an important part of next‐generation KM applications.