OperaV1, Main, Exploration, bibRecord, 000883

A Novel Metric for Information Retrieval in Semantic Networks

Identifieur interne : 000883 ( Main/Exploration ); précédent : 000882; suivant : 000884

A Novel Metric for Information Retrieval in Semantic Networks

Auteurs : L. Moore [Allemagne, États-Unis] ; Florian Steinke [Allemagne] ; Volker Tresp [Allemagne]

Source :

Lecture Notes in Computer Science [ 0302-9743 ] ; 2012.

RBID : ISTEX:0951B373A3E50E06AB324D71EE453D37AD788D14

Abstract

Abstract: We propose a novel graph metric for semantic entity-relationship networks. The metric is used for solving two tasks. First, given a semantic entity-relationship graph, such as for example DBpedia, we find relevant neighbors for a given query node. This could be useful for retrieving information relating to a specific entity. Second, we search for paths between two given nodes to discover interesting links. As an example, this can be helpful to analyze the various relationships between Albert Einstein and Niels Bohr. Compared to using the default step metric our approach yields more specific and informative results, as we demonstrate using two semantic web datasets. The proposed metric is defined via paths that maximize the log-likelihood of a restricted round trip and can intuitively be interpreted in terms of random walks on graphs. Our distance metric is also related to the commute distance, which is highly plausible for the described tasks but prohibitively expensive to compute. Our metric can be calculated efficiently using standard graph algorithms, rendering the approach feasible for the very large graphs of the semantic web’s linked data.

Url:

https://api.istex.fr/document/0951B373A3E50E06AB324D71EE453D37AD788D14/fulltext/pdf

DOI: 10.1007/978-3-642-25953-1_6

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">Abstract: We propose a novel graph metric for semantic entity-relationship networks. The metric is used for solving two tasks. First, given a semantic entity-relationship graph, such as for example DBpedia, we find relevant neighbors for a given query node. This could be useful for retrieving information relating to a specific entity. Second, we search for paths between two given nodes to discover interesting links. As an example, this can be helpful to analyze the various relationships between Albert Einstein and Niels Bohr. Compared to using the default step metric our approach yields more specific and informative results, as we demonstrate using two semantic web datasets. The proposed metric is defined via paths that maximize the log-likelihood of a restricted round trip and can intuitively be interpreted in terms of random walks on graphs. Our distance metric is also related to the commute distance, which is highly plausible for the described tasks but prohibitively expensive to compute. Our metric can be calculated efficiently using standard graph algorithms, rendering the approach feasible for the very large graphs of the semantic web’s linked data.</div>
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A Novel Metric for Information Retrieval in Semantic Networks

A Novel Metric for Information Retrieval in Semantic Networks

Source :

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri