Self-organizing peer-to-peer social networks

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 The Authors.Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules.National Natural Science of Foundation of Chin

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Finding the right answer: an information retrieval approach supporting knowledge sharing

Knowledge Management can be defined as the effective strategies to get the right piece of knowledge to the right person in the right time. Having the main purpose of providing users with information items of their interest, recommender systems seem to be quite valuable for organizational knowledge management environments. Here we present KARe (Knowledgeable Agent for Recommendations), a multiagent recommender system that supports users sharing knowledge in a peer-to-peer environment. Central to this work is the assumption that social interaction is essential for the creation and dissemination of new knowledge. Supporting social interaction, KARe allows users to share knowledge through questions and answers. This paper describes KARe�s agent-oriented architecture and presents its recommendation algorithm

Semantic Flooding: Semantic Search across Distributed Lightweight Ontologies

Lightweight ontologies are trees where links between nodes codify the fact that a node lower in the hierarchy describes a topic (and contains documents about this topic) which is more specific than the topic of the node one level above. In turn, multiple lightweight ontologies can be connected by semantic links which represent mappings among them and which can be computed, e.g., by ontology matching. In this paper we describe how these two types of links can be used to define a semantic overlay network which can cover any number of peers and which can be flooded to perform a semantic search on documents, i.e., to perform semantic flooding. We have evaluated our approach by simulating a network of 10,000 peers containing classifications which are fragments of the DMoz web directory. The results are promising and show that, in our approach, only a relatively small number of peers needs to be queried in order to achieve high accuracy