- Franz Baader, Martin Buchheit, Manfred A. Jeusfeld, Werner Nutt:
Knowledge Representation Meets Databases, Proceedings of the 3rd Workshop KRDB'96, Budapest, Hungary, August 13, 1996. CEUR Workshop Proceedings 4, CEUR-WS.org 1996
KRDB-96 was held in conjunction with the European Conference on Artificial Intelligence (ECAI-96).
Both databases and knowledge bases are used to represent the relevant parts of an application domain, and to allow convenient access to the stored information. Nevertheless, until recently there has been little cross-fertilization between the two areas. Research in KR mostly concentrated on expressive formalism with sophisticated reasoning services, usually under the assumption that the size of the KB is relatively small. In contrast, DB research was concerned with efficiently storing and retrieving large amounts of data, but the languages for describing schema information were rather simple, and reasoning about the schema played only a minor role.
In recent years, this distinction between the requirements and problems in KR and DB is vanishing rapidly. On the one hand, a modern KR-system must be able to handle large data sets if it is to be employed in realistic applications. This means that techniques developed in the DB area can and should be employed. On the other hand, the information stored in DBs becomes more complex, and thus requires more intelligent retrieval and reasoning techniques. For example, it turned out that important information about the connection between different data items could not be expressed in traditional data models. This led to the introduction of semantic, deductive, and object oriented data models. Recently, it has been shown that many of these data models can be expressed in suitable KR formalisms, which allows one to apply reasoning techniques from AI to database problems.
Unlike its predecessor workshops KRDB'94 and KRDB'95, which concentrated on the connection between object-oriented formalisms in KR and DB, KRDB'96 is intended to have a broader scope. We want to bring together researchers and developers from all areas of KR and DB where an interaction seems to be promising. In addition, users from industry can obtain a good impression of the research done on the border line between the two areas, and they can contribute their knowledge of what type of research is relevant in their applications.
- Troels Andreasen, Henning Christiansen:
Flexible Query-Answering Systems Modelled in Metalogic Programming.
- Mira Balaban, Adi Eyal:
DFL - A Hybrid Integration of Descriptions and Rules, using F-Logic as Underlying Semantics.
- Paolo Bresciani:
Some Research Trends in KR & DB.
- Stéphane Demphlous:
Adapting Database Object Models to Knowledge Representation Needs.
- Alfredo Goñi, Jesús Bermúdez, José Miguel Blanco, Arantza Illarramendi:
Using Reasoning of Description Logics for Query Processing in Multidatabase Systems.
- Ian Horrocks, Alan L. Rector, Carole A. Goble:
A Description Logic Based Schema for the Classification of Medical Data.
- Alka Irani, P. Sadanandan:
- Hannu Kangassalo:
Conceptual Description for Information Modelling Based on Intensional Containment Relation.
- T. Kessel, Michael Schlick, H.-M. Speiser, Uwe Brinkschulte, Holger Vogelsang:
C3L+++: Implementing a Description Logics System on Top of an Object-Oriented Database System.
- Stéphane Le Peutrec, Sophie Robin:
Abstraction on Object Based Knowledge Representation.
- Alon Y. Levy, Marie-Christine Rousset:
Using Description Logics to Model and Reason About Views.
- Eduardo Mena, Vipul Kashyap, Arantza Illarramendi, Amit P. Sheth:
Managing Multiple Information Sources through Ontologies: Relationship between Vocabulary Heterogeneity and Loss of Information.
- Amedeo Napoli, Arnaud Simon:
KR Meets DB for Data Mining.
- David Rudloff:
Terminological Reasoning and Conceptual Modeling for Datawarehouse.
- Piet-Hein Speel:
Managing Data and Knowledge in the Industrial Unilever Environment.
- Maurice Szmurlo, Bruno Crémilleux, Mauro Gaio, Jacques Madelaine:
Toward an Efficient Cross-Fertilization of Multiple Information Sources.
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