UDC 004.896, DOI:10.2298/CSIS100209015D

A Layered Rule-Based Architecture for Approximate Knowledge Fusion

Barbara Dunin-Keplicz1, 2, Linh Anh Nguyen1 and Andrzej Szalas1, 3

  1. Institute of Informatics, University of Warsaw
    Banacha 2, 02-097 Warsaw, Poland
  2. Institute of Computer Science, Polish Academy of Sciences
    Ordona 21, 01-237 Warsaw, Poland
  3. Dept. of Computer and Information Science, Linkoping University
    SE-581 83 Linkoping, Sweden


In this paper we present a framework for fusing approximate knowledge obtained from various distributed, heterogenous knowledge sources. This issue is substantial in modeling multi-agent systems, where a group of loosely coupled heterogeneous agents cooperate in achieving a common goal. In paper [5] we have focused on defining general mechanism for knowledge fusion. Next, the techniques ensuring tractability of fusing knowledge expressed as a Horn subset of propositional dynamic logic were developed in [13,16]. Propositional logics may seem too weak to be useful in real-world applications. On the other hand, propositional languages may be viewed as sublanguages of first-order logics which serve as a natural tool to define concepts in the spirit of description logics [2]. These notions may be further used to define various ontologies, like e.g. those applicable in the Semantic Web. Taking this step, we propose a framework, in which our Horn subset of dynamic logic is combined with deductive database technology. This synthesis is formally implemented in the framework of HSPDL architecture. The resulting knowledge fusion rules are naturally applicable to real-world data.

Key words

knowledge fusion, multi-agent systems, approximate reasoning, rule-based systems

Digital Object Identifier (DOI)


Publication information

Volume 7, Issue 3 (Jun 2010)
Year of Publication: 2010
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Dunin-Keplicz, B., Nguyen, L. A., Szalas, A.: A Layered Rule-Based Architecture for Approximate Knowledge Fusion. Computer Science and Information Systems, Vol. 7, No. 3, 617-642. (2010)