Distributed Reasoning in a Peer-to-Peer Setting: Application to the Semantic Web

TL;DR

This paper introduces DeCA, a novel consequence-finding algorithm for peer-to-peer inference systems, and demonstrates its application and scalability within Semantic Web data management over large networks.

Contribution

It presents the first consequence-finding algorithm for peer-to-peer inference, guarantees its completeness under certain conditions, and applies it to Semantic Web data management with scalability analysis.

Findings

DeCA computes consequences gradually from local to distant peers.

The algorithm guarantees completeness under specific acquaintance graph conditions.

Scalability tested on networks of up to 1000 peers.

Abstract

In a peer-to-peer inference system, each peer can reason locally but can also solicit some of its acquaintances, which are peers sharing part of its vocabulary. In this paper, we consider peer-to-peer inference systems in which the local theory of each peer is a set of propositional clauses defined upon a local vocabulary. An important characteristic of peer-to-peer inference systems is that the global theory (the union of all peer theories) is not known (as opposed to partition-based reasoning systems). The main contribution of this paper is to provide the first consequence finding algorithm in a peer-to-peer setting: DeCA. It is anytime and computes consequences gradually from the solicited peer to peers that are more and more distant. We exhibit a sufficient condition on the acquaintance graph of the peer-to-peer inference system for guaranteeing the completeness of this algorithm. Another important contribution is to apply this general distributed reasoning setting to the setting of the Semantic Web through the Somewhere semantic peer-to-peer data management system. The last contribution of this paper is to provide an experimental analysis of the scalability of the peer-to-peer infrastructure that we propose, on large networks of 1000 peers.