A Novel Metric for Information Retrieval in Semantic Networks
Identifieur interne : 001A29 ( Istex/Curation ); précédent : 001A28; suivant : 001A30A 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.
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.
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DOI: 10.1007/978-3-642-25953-1_6
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Florian Steinke<affiliation><mods:affiliation>E-mail: Florian.Steinke@siemens.com</mods:affiliation><wicri:noCountry code="no comma">E-mail: Florian.Steinke@siemens.com</wicri:noCountry></affiliation><affiliation><mods:affiliation>E-mail: Volker.Tresp@siemens.com</mods:affiliation><wicri:noCountry code="no comma">E-mail: Volker.Tresp@siemens.com</wicri:noCountry></affiliation>Le document en format XML
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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></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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