LinkSUM: Using Link Analysis to Summarize Entity Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9671)


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.


Entity summarization Linked data Knowledge graph Information filtering 


The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 611346 and by the German Federal Ministry of Education and Research (BMBF) within the Software Campus project “SumOn” (grant no. 01IS12051).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.AIFB, Karlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.University for Health Sciences, Medical Informatics and TechnologyHall in TirolAustria

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