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Tech Report kmi-06-08 Abstract


Probabilistic Methods for Data Integration in a Multi-Agent Query Answering System
Techreport ID: kmi-06-08
Date: 2006
Author(s): Miklos Nagy, Maria Vargas-Vera, Enrico Motta
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This report describes a proposal for a multi agent ontology-mapping framework that makes use of probabilistic information in order to enhance the correctness of the mapping. The proposed research focuses on two correlated areas namely similarity measures with its representation as a Dempster-Shafer belief function and usability of different optimalisation methods for combining these belief functions in a distributed environment. The main goal of our proposed research is to establish a multi agent framework that integrates user query related information from distributed scientific databases utilizing the AQUA system. The outcome of the research will contribute to the feasibility study of a distributed information integration network that is based on the European Commission Joint Research Centers data management and dissemination databases (AlloysDB, GasketDB, CorrosionDB, HTR-FUELDB), which stores mechanical and physical properties of engineering materials produced by the European RTD projects. These databases cover the materials behavior at low, elevated and high temperatures for base materials and welded joints and also includes irradiation materials testing in the field of fusion and fission and thermal barrier coatings tests for gas turbines.
 
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Knowledge Management
Creating learning organisations hinges on managing knowledge at many levels. Knowledge can be provided by individuals or it can be created as a collective effort of a group working together towards a common goal, it can be situated as "war stories" or it can be generalised as guidelines, it can be described informally as comments in a natural language, pictures and technical drawings or it can be formalised as mathematical formulae and rules, it can be expressed explicitly or it can be tacit, embedded in the work product. The recipient of knowledge - the learner - can be an individual or a work group, professionals, university students, schoolchildren or informal communities of interest.
Our aim is to capture, analyse and organise knowledge, regardless of its origin and form and make it available to the learner when needed presented with the necessary context and in a form supporting the learning processes.