This research examines the feasibility of dynamic element retrieval in a structured environment. Structured documents and queries are represented in extended vector form, based on a modification of the basic vector space model suggested by Fox [1983]. A method for the dynamic retrieval of XML elements, which requires only a single indexing of the documents at the level of the basic indexing node, is presented. This method, which we refer to as flexible retrieval , produces a rank ordered list of retrieved elements that is equivalent to the result produced by the same retrieval against an all-element index of the collection. Flexible retrieval obviates the need for storing either an all-element index or multiple indices of the collection.

A well constructed thesaurus has long been recognized as a valuable tool in the effective operation of an information retrieval system. This paper reports the results of experiments designed to determine the validity of an approach to the automatic construction of global thesauri (described originally by Crouch in [1] and [2] based on a clustering of the document collection. The authors validate the approach by showing that the use of thesauri generated by this method results in substantial improvements in retrieval effectiveness in four test collections. The term discrimination value theory, used in the thesaurus generation algorithm to determine a term's membership in a particular thesaurus class, is found not to be useful in distinguishing a “good” from an “indifferent” or “poor” thesaurus class). In conclusion, the authors suggest an alternate approach to automatic thesaurus construction which greatly simplifies the work of producing viable thesaurus classes. Experimental results show that the alternate approach described herein in some cases produces thesauri which are comparable in retrieval effectiveness to those produced by the first method at much lower cost.

In the extended vector space model, each document vector consists of a set of subvectors representing the multiple concepts or concept classes present in the document. Typical information concepts, in addition to the usual content terms or descriptors, include author names, bibliographic links, etc. The extended vector space model is known to improve retrieval effectiveness. However, a major impediment to the use of the extended model is the construction of an extended query. In this paper, we describe a method for automatically extending a query containing only content terms (a single concept class) to a representation containing multiple concept classes. No relevance feedback is involved. Experiments using the CACM collection resulted in an average precision 34% better than that obtained using the standard single-concept term vector model.

The importance of a thesaurus in the successful operation of an information retrieval system is well recognized. Yet techniques which support the automatic generation of thesauri remain largely undiscovered. This paper describes one approach to the automatic generation of global thesauri, based on the discrimination value model of Salton, Yang, and Yu and on an appropriate clustering algorithm. This method has been implemented and applied to two document collections. Preliminary results indicate that this method, which produces improvements in retrieval performance in excess of 10 and 15 percent in the test collections, is viable and worthy of continued investigation.