Searching Dynamic Communities with Personal Indexes

Löser, Alexander; Tempich, Christoph; Quilitz, Bastian; Balke, Wolf‐Tilo; Staab, Steffen; Nejdl, Wolfgang

doi:10.1007/11574620_36

Cited by 18 publications

(10 citation statements)

References 20 publications

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“…The most notable piece of work in the second category is that of Tempich et al [27] which was later refined and extended in [18]. Both of their approaches, namely INGA and REMINDIN', are based on shortcut creation and borrow ideas from social networks to rank peers based on the information about queries in the past according to their likelihood to be able to answer a certain query and to select the best ranked node for each query.…”

Section: Taxonomy-or Ontology-based Approachesmentioning

confidence: 99%

Using taxonomies for content-based routing with ants

2007

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Although the ant metaphor has been successfully applied to routing of data packets both in wireless and fixed networks, little is known yet about its appropriateness for search in peer-to-peer environments. This paper presents SemAnt, a distributed content-based routing algorithm based on the Ant Colony Optimization meta-heuristic and adapted for deployment in peer-to-peer networks. Under the assumption that content is annotated according to a taxonomy, it is possible to determine the hierarchical relationships between queries, and to exploit this information to improve the routing process. Our results show that using taxonomies enhances search performance in peer-to-peer networks. The degree of enhancement is highly dependent on the content distribution in the network.

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Section: Taxonomy-or Ontology-based Approachesmentioning

confidence: 99%

Using taxonomies for content-based routing with ants

2007

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“…Many proposals have been made in the literature, for example: globally available term statistics about the peers' contents [8,16,27,2], epidemic routing using gossiping strategies [18], routing indices with peer summaries from local neighborhoods [10,21], statistical synopses such as Bloom filters or hash sketches maintained in a directory based on distributed hash tables (DHT) [5,28,32], randomized expander graphs with low-diameter guarantees [25,26] and randomized rendezvous [31], clustering of thematically related peers [11,12,23], superpeer-based hierarchical networks [24,22], cost/benefit optimization based on coarse-grained global knowledge [29,30], and many more.…”

Section: Motivationmentioning

confidence: 99%

Efficient peer-to-peer semantic overlay networks based on statistical language models

Linari

Weikum

2006

Proceedings of the International Workshop on Information Retrieval in Peer-to-Peer Networks

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In this paper we address the query routing problem in peerto-peer (P2P) information retrieval. Our system builds up on the idea of a Semantic Overlay Network (SON), in which each peer becomes neighbor of a small number of peers, chosen among those that are most similar to it. Peers in the network are represented by a statistical Language Model derived from their local data collections but, instead of using the non-metric Kullback-Leibler divergence to compute the similarity between them, we use a symmetrized and "metricized" related measure, the square root of the JensenShannon divergence, which let us map the problem to a metric search problem. The search strategy exploits the triangular inequality to efficiently prune the search space and relies on a priority queue to visit the most promising peers first. To keep communications costs low and to perform an efficient comparison between Language Models, we devise a compression technique that builds on Bloom-filters and histograms and we provide error bounds for the approximation and a cost analysis for the algorithms used to build and maintain the SON.

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“…[13] and others rely on observing the past behavior of peers -queries sent and answered -to guess what kind of information peers contain, including some fallback strategies to overcome the bootstrapping problem. In [8], peers publish their expertise containing all topics they have information about without any aggregation, which will be a resource consumption problem for larger knowledge bases and networks.…”

Section: Related Workmentioning

confidence: 99%

Content Aggregation on Knowledge Bases Using Graph Clustering

Schmitz

Hotho

Jäschke

et al. 2006

Lecture Notes in Computer Science

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Abstract.Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded.This paper provides a graph clustering technique on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic P2PKM scenario.

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Searching Dynamic Communities with Personal Indexes

Cited by 18 publications

References 20 publications

Using taxonomies for content-based routing with ants

Using taxonomies for content-based routing with ants

Efficient peer-to-peer semantic overlay networks based on statistical language models

Content Aggregation on Knowledge Bases Using Graph Clustering

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