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In a bibliographic information system the database consists of a set of document representatives. Part of each document representative is a list of index terms whose function is to identify the content of the document represented. These index terms can be derived using an automated process. It is argued that automatic methods do not result in the best set of index terms being assigned to a document representative. A mathematical model is proposed which describes an information system which includes a method, document learning, of improving the set of index terms assigned to a document representative.

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A method of using relevance judgements to modify document descriptions has previously been proposed. An experiment is described which investigates the effect that application of this method, based on one query, has on a second query. The results indicate that approximately half of the second queries have substantially different retrieved document sets when document modification is used. Of these, about half behave better, and half worse, than originally. If the queries are repeated a second time, the proportion showing improved performance increases to 90% and only 3% still give inferior retrieved document sets. The general conclusion is that document modification can lead to improved information system performance.

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