LODDO: using linked open data description overlap to measure semantic relatedness between named entities

  • Authors:

  • Wenlei Zhou;Haofen Wang;Jiansong Chao;Weinan Zhang;Yong Yu

  • Affiliations:

  • APEX Data & Knowledge Management Lab, Department of Computer Science Engineering, Shanghai Jiao Tong University, Shanghai, China;APEX Data & Knowledge Management Lab, Department of Computer Science Engineering, Shanghai Jiao Tong University, Shanghai, China;APEX Data & Knowledge Management Lab, Department of Computer Science Engineering, Shanghai Jiao Tong University, Shanghai, China;APEX Data & Knowledge Management Lab, Department of Computer Science Engineering, Shanghai Jiao Tong University, Shanghai, China;APEX Data & Knowledge Management Lab, Department of Computer Science Engineering, Shanghai Jiao Tong University, Shanghai, China

  • Venue:
  • JIST'11 Proceedings of the 2011 joint international conference on The Semantic Web
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

Measuring semantic relatedness plays an important role in information retrieval and Natural Language Processing. However, little attention has been paid to measuring semantic relatedness between named entities, which is also very significant. As the existing knowledge based approaches have the entity coverage issue and the statistical based approaches have unreliable result to low frequent entities, we propose a more comprehensive approach by leveraging Linked Open Data (LOD) to solve these problems. LOD consists of lots of data sources from different domains and provides rich a priori knowledge about the entities in the world. By exploiting the semantic associations in LOD, we propose a novel algorithm, called LODDO, to measure the semantic relatedness between named entities. The experimental results show the high performance and robustness of our approach.