Tech Reports
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
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.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
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We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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