A Weighted-Tree Simplicity Algorithm for Similarity Matching of Partial Product Descriptions

Our weighted-tree similarity algorithm matches buyers and sellers in e-Business environments. We use arc-labeled, arc-weighted trees to represent the products (or services) sought/offered by buyers/sellers. Partial product descriptions can be represented via subtrees missing in either or both of the trees. In order to take into account the effect of a missing subtree on the similarity between two trees, our algorithm uses a (complexity or) simplicity measure. Besides tree size (breadth and depth), arc weights are taken into account by our tree simplicity algorithm. This paper formalizes our buyer/seller trees and analyzes the properties of the implemented tree simplicity measure. We discuss how this measure captures business intuitions, give computational results on the simplicity of balanced k-ary trees, and show that they conform to the theoretical analysis.Notre algorithme de similitude d'arborescences \ue0 pond\ue9ration correspond \ue0 celui des acheteurs et des vendeurs des environnements d'affaires \ue9lectroniques. Nous utilisons des arborescences \ue9tiquet\ue9es et pond\ue9r\ue9es par arc pour repr\ue9senter les produits (ou les services) recherch\ue9s/offerts par les acheteurs/vendeurs. Des descriptions partielles de produits peuvent \ueatre repr\ue9sent\ue9es \ue0 l'aide de sous-arborescences manquantes dans l'une des arborescences ou les deux. Afin de prendre en compte l'effet d'une arborescence manquante sur la similitude entre les deux arborescences, notre algorithme utilise une mesure de simplicit\ue9 (ou de complexit\ue9). Outre la taille de l'arborescence (\ue9tendue et profondeur), les poids d'arc sont pris en compte par notre algorithme de simplicit\ue9 d'arborescence. Ce document officialise les arborescences des acheteurs/vendeurs et analyse les propri\ue9t\ue9 de la mesure de simplicit\ue9 d'arborescence implant\ue9e. Nous discutons de la fa\ue7on dont cette mesure saisit les intuitions commerciales, fournit des r\ue9sultats informatiques concernant la simplicit\ue9 des arborescences \uab k-ary \ubb \ue9quilibr\ue9es et montre qu'ils sont conformes \ue0 l'analyse th\ue9orique.NRC publication: Ye

Rulebase Integration for eCollaboration

A variety of conflicts between rulebases are identified and guidelines for conflict resolutions are suggested. Based on the classification, a framework for rulebase integration is proposed containing two different integration approaches, namely interoperation and interchange. The problem of semantics-preserving rulebase transformation is discussed, and a solution is given. Function serialization and representation during transformation are also discussed and represented in terms of Functional RuleML.Cet article expose divers conflits entre les bases de r\ue8gles et sugg\ue8re des directives pour la r\ue9solution de ces conflits. Il propose un cadre de travail pour l'int\ue9gration de bases de r\ue8gles, bas\ue9 sur la classification et contenant deux diff\ue9rentes m\ue9thodologies d'int\ue9gration qui font appel respectivement \ue0 l'interop\ue9rabilit\ue9 et \ue0 l'\ue9change. Il traite du probl\ue8me de la transformation \ue0 base de r\ue8gles et pr\ue9servatrice de la s\ue9mantique et pr\ue9sente une solution. Il aborde \ue9galement la s\ue9rialisation et la repr\ue9sentation des fonctions durant la transformation et les repr\ue9sente en fonction du Functional RuleMl (RuleML fonctionnel).NRC publication: Ye

Compromise matching in P2P e-marketplaces : concept, algorithm and use case

A basic component of automated matchmaking is the automatic generation of a ranked list of profiles matching with the profiles of a given participant. Identifying and ranking of matching profiles among thousands of candidate profiles is a challenging task. In order to determine the degree of matching between two profiles, corresponding pairs of constraints are compared and aggregated to the overall similarity between the two profiles. This paper describes the structure and algorithm of a proposed match-making system with a focus on the central notion of compromise match. A compromise match is called for when either one or both constraints within a pair are soft and moreover their values do not match exactly. Two important aspects of compromise matching are discussed, namely compromise count factor, compromise count reduction factor; furthermore their effect on ranking is described. A use case with a sample set of home rental profiles from an existing e-marketplace is employed for demonstration.Peer reviewed: YesNRC publication: Ye