Semantic peer to peer (P2P) systems are fully decentralized overlay networks of people or machines (called peers) sharing and searching varied resources (documents, videos, photos, data, services) based on their semantic annotations using ontologies. They provide a support for the emergence of open and decentralized electronic social networks, in which no central or external authority can control the reliability of the peers participating to the network. This lack of control may however cause some of the results provided by some peers to be unsatisfactory, because of inadequate or obsolete annotations.In this paper, we propose a probabilistic model to handle trust in a P2P setting. It supports a local computation and a simple form of propagation of the trust of peers into classes of other peers. We claim that it is well appropriate to the dynamics of P2P networks and to the freedom of each peer within the network to have different viewpoints towards the peers with which it interacts.

We present the WebContent platform for managing distributed repositories of XML and semantic Web data. The platform allows integrating various data processing building blocks (crawling, translation, semantic annotation, full-text search, structured XML querying, and semantic querying), presented as Web services, into a large-scale efficient platform. Calls to various services are combined inside ActiveXML [8] documents, which are XML documents including service calls. An ActiveXML optimizer is used to: ( i ) efficiently distribute computations among sites; ( ii ) perform XQuery-specific optimizations by leveraging an algebraic XQuery optimizer; and ( iii ) given an XML query, chose among several distributed indices the most appropriate in order to answer the query.

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.

The paper presents a system based on new operators for handling sets of propositional clauses represented by means of ZBDDs (zero-suppressed binary decision diagrams). The high compression power of such data structures allows efficient encodings of structured instances. A specialized operator for the distribution of sets of clauses is introduced and used for performing multi-resolution on clause sets. Cut eliminations between sets of clauses of exponential size may then be performed using polynomial size data structures. The ZREs system, a new implementation of the Davis-Putnam procedure (M. Davis and H. Putnam, 1960), solves two hard problems for resolution, that are currently out of the scope of the best SAT provers.