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Master Thesis

Scalable Query-based Faceted Search on top of SPARQL Endpoints for Guided and Expressive Semantic Search

Abstract : Because the Web of Documents is composed of structured pages that are not meaningful to machines, search in the Web of Documents is generally processed by keywords. However, because the Web of Data provides structured information, search in the Web of Data can be more precise. SPARQL is the standard query language for querying this structured information. SPARQL is expressive and its syntax is similar to SQL. However, casual user can not write SPARQL queries. There is a search system for the Web of Data, Sewelis, offering to explore data progressively and more user-friendly than SPARQL. Sewelis guides the search with a query built incrementally because users only have to select query elements in order to complete the query. However, Sewelis does not scale to large datasets such as DBpedia, which is composed of about 2 billion triples. In this report, we introduce Scalewelis. Scalewelis is a search system for the Web of Data like Sewelis that is scalable. Moreover, Scalewelis is independent to data because it connects to SPARQL endpoints. We took part in a challenge on DBpedia with Scalewelis. We were able to answer to 70 questions out of 99 with acceptable response times.
Keywords : Semantic search SPARQL
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Master Thesis
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https://dumas.ccsd.cnrs.fr/dumas-00854852
Contributor : Co-Responsables Du Mri V. Gouranton Et S. Blazy <>
Submitted on : Wednesday, August 28, 2013 - 11:36:50 AM
Last modification on : Tuesday, June 15, 2021 - 4:16:21 PM
Long-term archiving on: : Monday, December 2, 2013 - 8:49:41 AM

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  • HAL Id : dumas-00854852, version 1

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Joris Guyonvarch. Scalable Query-based Faceted Search on top of SPARQL Endpoints for Guided and Expressive Semantic Search. Information Retrieval [cs.IR]. 2013. ⟨dumas-00854852⟩

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