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<p>Given the exponential increase of the information available online, we need effective and efficient methods for enabling the understanding and retrieval of key insights from complex data. Entity Summarization (ES) focuses on capturing the most relevant and representative information about an entity, facilitating its quick exploration and understanding. Previous entity summarization approaches struggled either to eliminate irrelevant information or to identify redundant content with an impact on the quality of the result. In this paper, we propose EntitySum, a novel approach that focuses on identify informative triples mainly based on entities’ topological and semantic importance as well as minimize redundancy. EntitySum, first filtering out noisy data, then selects the most relevant triples, considering enitites’ objects part in Knowledge Graph (KG) by using centrality measures combined with their properties’ frequency. Finally, to succeed the non-redundancy, we apply a minimize redundancy function that targeting the duplicate objects. Experiments conducted on the ESBM benchmark confirm that our method outperforms several baselines, demonstrating improved quality in entity summaries.</p>

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Efficient RDF entity summarization using centrality measures

  • 期刊论文

  • 发表日期：

    2026年7月4日

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  • Georgia Eirini Trouli,
  • Giannis Vassiliou,
  • Haridimos Kondylakis,
  • Nikolaos Papadakis

摘要

Given the exponential increase of the information available online, we need effective and efficient methods for enabling the understanding and retrieval of key insights from complex data. Entity Summarization (ES) focuses on capturing the most relevant and representative information about an entity, facilitating its quick exploration and understanding. Previous entity summarization approaches struggled either to eliminate irrelevant information or to identify redundant content with an impact on the quality of the result. In this paper, we propose EntitySum, a novel approach that focuses on identify informative triples mainly based on entities’ topological and semantic importance as well as minimize redundancy. EntitySum, first filtering out noisy data, then selects the most relevant triples, considering enitites’ objects part in Knowledge Graph (KG) by using centrality measures combined with their properties’ frequency. Finally, to succeed the non-redundancy, we apply a minimize redundancy function that targeting the duplicate objects. Experiments conducted on the ESBM benchmark confirm that our method outperforms several baselines, demonstrating improved quality in entity summaries.