 Information retrieval has long been a central theme of information science, covering retrieval of both structured data such as are found in relational databases as well as unstructured text documents (e.g., Salton 1989). Retrieval criteria for the two types of data are not unrelated, since both structured and unstructured data often require content-directed retrieval. For example, while users of an employee database may wish at times to retrieve employee records by the unique name or ID of employees, at other times they may wish to retrieve all employees in a certain employment category, perhaps with further restrictions such as falling into a certain salary bracket. This is accomplished with the use of “inverted files” that essentially index entities under their attributes and values rather than their identifiers. In the same way, text documents might be retrieved via some unique label, or they might instead be retrieved in accord with their relevance to a certain query or topic header. The simplest notion of relevance is that the documents should contain the terms (words or short phrases) of the query.  However, terms that are distinctive for a document should be given more weight. Therefore a standard measure of relevance, given a particular query term, is the tf–idf  (term frequency–inverse document frequency) for the term, which increases (e.g., logarithmically) with the frequency of occurrences of the term in the document but is discounted to the extent that it occurs frequently in the set of documents as a whole. Summing the tf-idf's of the query terms yields a simple measure of document relevance.