In this demonstration, we present interoperable and personalized search services for the Networked Digital Library of Theses and Dissertations (NDLTD). Using standard protocols and software, including those specified by the Open Archives Initiative (OAI), distributed sites can share metadata easily. On top of these harvesting protocols, we implement a union collection of theses managed by the MARIAN digital library system. Our demonstration covers aspects of NDLTD, OAI, and MARIAN.

A digital library of computer science literature, Envision provides powerful information visualization by displaying search results as a matrix of icons, with layout semantics under user control.Envision's Graphic View interacts with an Item Summary Window giving users access to bibliographic information, and XMosaic provides access to complete bibliographic information, abstracts, and full content.While many visualization interfaces for information retrieval systems depict ranked query-document similarity, Envision graphically presents a variety of document characteristics and supports an extensive range of user tasks.Formative usability evaluation results show great user satisfaction with Envision's style of presentation and the document characteristics visualized.

Since 1986 we have investigated the problems and possibilities of applying modern information retrieval methods to large online public access library catalogs (OPACs). In the Retrieval Experiment—Virginia Tech OnLine Catalog (REVTOLC) study we carried out a large pilot test in 1987 and a larger, controlled investigation in 1990, with 216 users and roughly 500,000 MARC records. Results indicated that a forms-based interface coupled with vector and relevance feedback retrieval methods would be well received. Recent efforts developing the Multiple Access and Retrieval of Information with Annotations (MARIAN) system have involved used of a specially developed object-oriented DBMS, construction of a client running under NeXTSTEP, programming of a distributed server with a thread assigned to each user session to increase concurrency on a small network of NeXTs, refinement of algorithms to use objects and stopping rules for greater efficiency, usability testing and iterative interface refinement.

With the advent of the information society, many researchers are turning to artificial intelligence techniques to provide effective retrieval over large bodies of textual information. In the CODER system, the mission of which is to provide an environment for experiments in applying AI to information retrieval, a factual representation language (FRL) serves as a tool for knowledge engineering and experimentation. the FRL is a hybrid AI language supporting strong typing for attribute values, a frame system, and Prolog-like relational structures. Inheritance is enforced throughout, and the semantics of type subsumption and object matching are formally defined. A collection of type and object managers called the knowledge administration complex implements this common language for storing knowledge and communicating it within the system. Storage of large numbers of complete knowledge objects (statements in the language) is supported by a system of external knowledge bases. Of the three types of knowledge structures in the language, the frame facility has proven most useful in the retrieval domain. This article discusses the frame construct itself, the implementation of the relevant portions of the knowledge administration complex and external knowledge bases, and the use of frames in retrieval research. It closes with a discussion of the utility of the FRL.