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

Abstract

A 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‐labeled, arc‐labeled, 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 parameterized, 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.