Commonality-Based Information Retrieval with a Terminological Knowledge Representation System

Thomas Mantay

ISBN 978-3-89722-632-6
223 Seiten, Erscheinungsjahr: 2001
Preis: 40.50 €

The World-Wide Web as a vast information repository consists of a largely unorganized collection of documents, and most information systems in the Web use standard database techniques for information retrieval. As argued in the knowledge discovery, data mining, and Artificial Intelligence literature, this may not always be appropriate in view of the growing number of private users accessing the internet. Researchers have reached a consensus that there is a need to structure information and provide intelligent information retrieval techniques, especially adapted to the needs of the new user class.

Terminological knowledge representation systems based on description logics as descendants of the system {\sc Kl-One} have proven to be an excellent means for structurally representing the knowledge of an application domain and reasoning about it. In this thesis we present a theoretical framework for a terminological knowledge representation system whose basic feature is a facility for commonality-based information retrieval. With this technique, information retrieval is performed on the basis of the commonalities of example information items specified by the user. The results in this thesis are thus a step towards a unification of the query-by-example information retrieval paradigm and the logic-based knowledge representation methodology.

Due to the formal syntax and well-defined semantics of description logics, it is possible to develop specialized and efficient reasoning services which are used to derive implicit knowledge from explicitly represented knowledge. The reasoning services can be used to support the design of the knowledge base as well as its application. In this thesis we present a number of reasoning services and integrate them into a terminological knowledge representation system based on expressive description logics. We will particularly focus on the development of specialized non-standard reasoning services to formalize commonality-based information retrieval.

Robustness issues play an important role in Artificial Intelligence research. In our application context robustness of a system is strongly influenced by the amount and quality of information items returned by the system. It is a crucial objective to keep the retrieval set processable, especially if strongly diversified or unexpectedly homogeneous collections of user-specified examples are present. Therefore, a great portion of this work will be concerned with the problem of avoiding inappropriately large or small retrieval sets. In addition, we will deal with the problem of incorporating so-called negative examples. The specification of negative examples enables the user to express examples of undesired information items.

The reasoning procedures developed in this thesis are not only of use for commonality-based information retrieval, but for many other description logic applications including innovative forms of example-based knowledge base design and learning methods. Aside from theoretical usefulness, the presented retrieval framework can be applied to build information systems for many purposes including e-commerce applications as a field of growing economic relevance. However, the implementation of the theoretical results into a full-featured application is not part of this thesis.

  • Wissensrepräsentation
  • Knowledge representation
  • Künstliche Intelligenz
  • Artificial Intelligence
  • Beschreibungslogik


40.50 €
Versandkostenfrei innerhalb Deutschlands

Wollen auch Sie Ihre Dissertation veröffentlichen?