Consensus ontologies in socially interacting multiagent systems

Abstract

This paper presents approaches for building, managing, and evaluating consensus ontologies from the individual ontologies of a network of socially interacting agents. Each agent has its own conceptualization of the world within the multiagent system framework. The interactions between agents are modeled by sending queries and receiving responses and later assessing each other's performance based on the results. This model enables us to measure the quality of the societal beliefs in the resources which we represent as the expertise in each domain. The dynamic nature of our system allows us to model the emergence of consensus that mimics the evolution of language. We present an algorithm for generating the consensus ontologies which makes use of the authoritative agent's conceptualization in a given domain. As the expertise of agents changes after a number of interactions, the consensus ontology that we build based on the agents' individual views evolves. The resulting approach is concordant with the principles of emergent semantics. We provide formal definitions for the problem of finding a consensus ontology in a step by step manner. We evaluate the consensus ontologies by using different heuristic measures of similarity based on the component ontologies. Conceptual processing methods for generating, manipulating, and evaluating consensus ontologies are given and experimental results are presented. The presented approach looks promising and opens new directions for further research.