A Weighted-Tree Similarity Algorithm for Multi-Agent Systems in E-Business Environments

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ConferenceComputational Intelligence Journal, 2004
Subjectmulti-agent system; e-Business; e-Learning; similarity measure; buyer-seller matching; arc-labelled trees; arc-weighted trees; Object-Oriented RuleML; Relfun; commerce électronique; apprentissage électronique; mesure des similarités; appariement acheteur vendeur; arbres à arcs étiquetés; arbres à arcs pondérés; RuleML orienté objet; Relfun
AbstractA tree similarity algorithm for match-making of agents in e-Business environments is presented. Product/service descriptions of seller and buyer agents are represented as node-labelled, arc-labelled, arc-weighted trees. A similarity algorithm for such trees is developed as the basis for semantic match-making in a virtual marketplace. The trees are exchanged using an XML serialization in Object-Oriented RuleML. Correspondingly, we use the declarative language Relfun to implement the similarity algorithm as a parameterised, recursive functional program. Three main recursive functions perform a top-down traversal of trees and the bottom-up computation of similarity. Results from our experiments aiming to match buyers and sellers are found to be effective and promising for e-Business/e-Learning environments. The algorithm can be applied in all environments where weighted trees are used.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48060
NPARC number8913150
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Record identifier12d96b97-3533-41da-875a-35202f742420
Record created2009-04-22
Record modified2016-05-09
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