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K (Textersetzung - „Forschungsgruppe=Wissensmanagement“ durch „Forschungsgruppe=Web Science und Wissensmanagement“)
 
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{{Publikation Erster Autor
 
{{Publikation Erster Autor
|ErsterAutorNachname=Tran
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|ErsterAutorNachname=Thanh Tran
|ErsterAutorVorname=Thanh
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|ErsterAutorVorname=Duc
 
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{{Publikation Author
 
{{Publikation Author
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{{Article
 
{{Article
|Referiert=False
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|Referiert=True
 
|Title=Keyword Query Routing
 
|Title=Keyword Query Routing
|Year=2013
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|Year=2014
|Journal=IEEE Transactions on Knowledge and Data Engineering
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|Month=Februar
|Publisher=IEEE computer Society Digital Library
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|Journal=IEEE Transactions on Knowledge and Data Engineering (TKDE)
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|Volume=26
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|Number=2
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|Pages=363-375
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|Publisher=IEEE Computer Society
 
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{{Publikation Details
 
{{Publikation Details
 
|Abstract=Keyword search is an intuitive paradigm for searching linked data sources on the Web. We propose to route keywords only to relevant sources to reduce the high cost of processing keyword search queries over all sources. We propose a novel method for computing top-k routing plans based on their potentials to contain results for a given keyword query. We employ a keyword-element relationship summary that compactly represents relationships between keywords and the data elements mentioning them. A multi-level scoring mechanism is proposed for computing the relevance of routing plans based on scores at the level of keywords, data elements, element sets and subgraphs which connect these elements. Experiments carried out using 150 publicly available sources on the Web showed that valid plans (precision@1 of 0.92) that are highly relevant (mean reciprocal rank of 0.89) can be computed in 1 second on average on a single PC. Further, we show routing greatly helps to improve the performance of keyword search, without compromising its result quality.
 
|Abstract=Keyword search is an intuitive paradigm for searching linked data sources on the Web. We propose to route keywords only to relevant sources to reduce the high cost of processing keyword search queries over all sources. We propose a novel method for computing top-k routing plans based on their potentials to contain results for a given keyword query. We employ a keyword-element relationship summary that compactly represents relationships between keywords and the data elements mentioning them. A multi-level scoring mechanism is proposed for computing the relevance of routing plans based on scores at the level of keywords, data elements, element sets and subgraphs which connect these elements. Experiments carried out using 150 publicly available sources on the Web showed that valid plans (precision@1 of 0.92) that are highly relevant (mean reciprocal rank of 0.89) can be computed in 1 second on average on a single PC. Further, we show routing greatly helps to improve the performance of keyword search, without compromising its result quality.
|ISSN=1041-4347
 
 
|DOI Name=http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.13
 
|DOI Name=http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.13
|Forschungsgruppe=Wissensmanagement
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|Forschungsgruppe=Web Science und Wissensmanagement
 
}}
 
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Aktuelle Version vom 15. Oktober 2015, 11:49 Uhr


Keyword Query Routing


Keyword Query Routing



Veröffentlicht: 2014 Februar

Journal: IEEE Transactions on Knowledge and Data Engineering (TKDE)
Nummer: 2
Seiten: 363-375
Verlag: IEEE Computer Society
Volume: 26


Referierte Veröffentlichung

BibTeX




Kurzfassung
Keyword search is an intuitive paradigm for searching linked data sources on the Web. We propose to route keywords only to relevant sources to reduce the high cost of processing keyword search queries over all sources. We propose a novel method for computing top-k routing plans based on their potentials to contain results for a given keyword query. We employ a keyword-element relationship summary that compactly represents relationships between keywords and the data elements mentioning them. A multi-level scoring mechanism is proposed for computing the relevance of routing plans based on scores at the level of keywords, data elements, element sets and subgraphs which connect these elements. Experiments carried out using 150 publicly available sources on the Web showed that valid plans (precision∂1 of 0.92) that are highly relevant (mean reciprocal rank of 0.89) can be computed in 1 second on average on a single PC. Further, we show routing greatly helps to improve the performance of keyword search, without compromising its result quality.

DOI Link: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.13



Forschungsgruppe

Web Science und Wissensmanagement


Forschungsgebiet