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

โœ Scribed by Haofen Wang; Qiaoling Liu; Thomas Penin; Linyun Fu; Lei Zhang; Thanh Tran; Yong Yu; Yue Pan

Publisher
Elsevier Science
Year
2009
Tongue
English
Weight
962 KB
Volume
7
Category
Article
ISSN
1570-8268

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โœฆ Synopsis

a b s t r a c t

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.

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