Browsing by Author "France, Robert Karl"

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    An artificial intelligence environment for information retrieval research
    France, Robert Karl (Virginia Polytechnic Institute and State University, 1986)
    The CODER (COmposite Document Expert/Extended/Effective Retrieval) project is a multi-year effort to investigate how best to apply artificial intelligence methods to increase the effectiveness of information retrieval systems. Particular attention is being given to analysis and representation of heterogeneous documents, such as electronic mail digests or messages, which vary widely in style, length, topic, and structure. In order to ensure system adaptability and to allow reconfiguration for controlled experimentation, the project has been designed as a moderated expert system. This thesis covers the design problems involved in providing a unified architecture and knowledge representation scheme for such a system, and the solutions chosen for CODER. An overall object-oriented environment is constructed using a set of message-passing primitives based on a modified Prolog call paradigm. Within this environment is embedded the skeleton of a flexible expert system, where task decomposition is performed in a knowledge-oriented fashion and where subtask managers are implemented as members of a community of experts. A three-level knowledge representation formalism of elementary data types, frames, and relations is provided, and can be used to construct knowledge structures such as terms, meaning structures, and document interpretations. The use of individually tailored specialist experts coupled with standardized blackboard modules for communication and control and external knowledge bases for maintenance of factual world knowledge allows for quick prototyping, incremental development, and flexibility under change. The system as a whole is structured as a set of communicating modules, defined functionally and implemented under UNIX™ using sockets and the TCP/IP protocol for communication. Inferential modules are being coded in MU-Prolog; non-inferential modules are being prototyped in MU-Prolog and will be re-implemented as needed in C++.
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    Effective, Efficient Retrieval in a Network of Digital Information Objects
    France, Robert Karl (Virginia Tech, 2001-11-26)
    Although different authors mean different thing by the term "digital libraries," one common thread is that they include or are built around collections of digital objects. Digital libraries also provide services to large communities, one of which is almost always search. Digital library collections, however, have several characteristic features that make search difficult. They are typically very large. They typically involve many different kinds of objects, including but not limited to books, e-published documents, images, and hypertexts, and often including items as esoteric as subtitled videos, simulations, and entire scientific databases. Even within a category, these objects may have widely different formats and internal structure. Furthermore, they are typically in complex relationships with each other and with such non-library objects as persons, institutions, and events. Relationships are a common feature of traditional libraries in the form of "See / See also" pointers, hierarchical relationships among categories, and relations between bibliographic and non-bibliographic objects such as having an author or being on a subject. Binary relations (typically in the form of directed links) are a common representational tool in computer science for structures from trees and graphs to semantic networks. And in recent years the World-Wide Web has made the construct of linked information objects commonplace for millions. Despite this, relationships have rarely been given "first-class" treatment in digital library collections or software. MARIAN is a digital library system designed and built to store, search over, and retrieve large numbers of diverse objects in a network of relationships. It is designed to run efficiently over large collections of digital library objects. It addresses the problem of object diversity through a system of classes unified by common abilities including searching and presentation. Divergent internal structure is exposed and interpreted using a simple and powerful graphical representation, and varied format through a unified system of presentation. Most importantly, MARIAN collections are designed to specifically include relations in the form of an extensible collection of different sorts of links. This thesis presents MARIAN and argues that it is both effective and efficient. MARIAN is effective in that it provides new and useful functionality to digital library end-users, and in that it makes constructing, modifying, and combining collections easy for library builders and maintainers. MARIAN is efficient since it works from an abstract presentation of search over networked collections to define on the one hand common operations required to implement a broad class of search engines, and on the other performance standards for those operations. Although some operations involve a high minimum cost under the most general assumptions, lower costs can be achieved when additional constraints are present. In particular, it is argued that the statistics of digital library collections can be exploited to obtain significant savings. MARIAN is designed to do exactly that, and in evidence from early versions appears to succeed. In conclusion, MARIAN presents a powerful and flexible platform for retrieval on large, diverse collections of networked information, significantly extending the representation and search capabilities of digital libraries.