Semplore: A scalable IR approach to search the Web of Data

  • Authors:

  • Haofen Wang;Qiaoling Liu;Thomas Penin;Linyun Fu;Lei Zhang;Thanh Tran;Yong Yu;Yue Pan

  • Affiliations:

  • Shanghai Jiao Tong University, Shanghai 200240, China;Shanghai Jiao Tong University, Shanghai 200240, China;Shanghai Jiao Tong University, Shanghai 200240, China;Shanghai Jiao Tong University, Shanghai 200240, China;IBM China Research Lab, Beijing 100094, China;Institute AIFB, Universität Karlsruhe, D-76128 Karlsruhe, Germany;Shanghai Jiao Tong University, Shanghai 200240, China;IBM China Research Lab, Beijing 100094, China

  • Venue:
  • Web Semantics: Science, Services and Agents on the World Wide Web
  • Year:
  • 2009

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Abstract

The Web of Data keeps growing rapidly. However, the full exploitation of this large amount of structured data faces numerous challenges like usability, scalability, imprecise information needs and data change. We present Semplore, an IR-based system that aims at addressing these issues. Semplore supports intuitive faceted search and complex queries both on text and structured data. It combines imprecise keyword search and precise structured query in a unified ranking scheme. Scalable query processing is supported by leveraging inverted indexes traditionally used in IR systems. This is combined with a novel block-based index structure to support efficient index update when data changes. The experimental results show that Semplore is an efficient and effective system for searching the Web of Data and can be used as a basic infrastructure for Web-scale Semantic Web search engines.