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|Download=LinkSUM.pdf,  
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|Abstract=The amount of structured data published on the Web is constantly growing. A significant part of this data is published in accordance to the Linked Data principles. The explicit graph structure enables machines and humans to retrieve descriptions of entities and discover information about relations to other entities. In many cases, descriptions of single entities include thousands of statements and for human users it becomes difficult to comprehend the data unless a selection of the most relevant facts is provided.
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In this paper we introduce LinkSUM, a lightweight link-based approach for the relevance-oriented summarization of knowledge graph entities. LinkSUM optimizes the combination of the PageRank algorithm with an adaption of the Backlink method together with new approaches for predicate selection. Both, quantitative and qualitative evaluations have been conducted to study the performance of the method in comparison to an existing entity summarization approach. The results show a significant improvement over the state of the art and lead us to conclude that prioritizing the selection of related resources leads to better summaries.
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|Download=LinkSUM.pdf,
 
|Projekt=SumOn, XLiMe
 
|Projekt=SumOn, XLiMe
 
|Forschungsgruppe=Web Science und Wissensmanagement
 
|Forschungsgruppe=Web Science und Wissensmanagement

Version vom 23. März 2016, 11:36 Uhr


LinkSUM: Using Link Analysis to Summarize Entity Data


LinkSUM: Using Link Analysis to Summarize Entity Data



Published: 2016 Juni

Buchtitel: ICWE 2016
Verlag: Springer

Referierte VeröffentlichungNote: to appear

BibTeX




Kurzfassung
The amount of structured data published on the Web is constantly growing. A significant part of this data is published in accordance to the Linked Data principles. The explicit graph structure enables machines and humans to retrieve descriptions of entities and discover information about relations to other entities. In many cases, descriptions of single entities include thousands of statements and for human users it becomes difficult to comprehend the data unless a selection of the most relevant facts is provided.

In this paper we introduce LinkSUM, a lightweight link-based approach for the relevance-oriented summarization of knowledge graph entities. LinkSUM optimizes the combination of the PageRank algorithm with an adaption of the Backlink method together with new approaches for predicate selection. Both, quantitative and qualitative evaluations have been conducted to study the performance of the method in comparison to an existing entity summarization approach. The results show a significant improvement over the state of the art and lead us to conclude that prioritizing the selection of related resources leads to better summaries.

Download: Media:LinkSUM.pdf

Projekt

SumOnXLiMe


Verknüpfte Tools

LinkSUM


Verknüpfte Datasets

DBpedia PageRank


Forschungsgruppe

Web Science und Wissensmanagement


Forschungsgebiet

Vernetzte Daten, Semantische Suche, Maschinelles Lernen, Entitätszusammenfassung