UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 5 Issue 7
July-2018
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1807967


Registration ID:
186110

Page Number

454-457

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Title

A Review on Knowledge Base Semantic Integration using Crowdsourcing Through Ontological Model

Abstract

The semantic web has enabled the formulation of a growing number of knowledge bases (KBs), which are designed separately using different procedures. Integration of KBs has brought much attention as different KBs normally contain overlapping and interconnected information. Automatic techniques for KB integration have been developed but far from ideal. Therefore, in this paper, the problem of knowledge base semantic integration using crowd knowledge. There are both classes and instances in a KB, in our work, we propose a novel hybrid framework for KB semantic integration considering the semantic heterogeneity of KB class structures. We first perform semantic integration of the class structures via crowdsourcing, then apply the blocking-based instance matching approach according to the integrated class structure. For class structure (taxonomy) semantic integration, the crowd is leveraged to help to identify the semantic relationships between classes to handle the semantic heterogeneity problem. Under the conditions of both large-scale KBs and limited monetary budget for crowdsourcing, here formalize the class structure (taxonomy) semantic integration problem as a Local Tree Based Query Selection. Furthermore, the KBs are usually of large scales and have millions of instances, and direct pairwise-based instance matching is ineffective. Therefore, we adopt the blocking-based strategy for instance matching, taking advantage of the class structure integration result. The experiments on real large-scale KBs verify the effectiveness and efficiency of the proposed strategies.

Key Words

knowledge bases, crowdsourcing, semantic web, relevance.

Cite This Article

"A Review on Knowledge Base Semantic Integration using Crowdsourcing Through Ontological Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.454-457, July-2018, Available :http://www.jetir.org/papers/JETIR1807967.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"A Review on Knowledge Base Semantic Integration using Crowdsourcing Through Ontological Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp454-457, July-2018, Available at : http://www.jetir.org/papers/JETIR1807967.pdf

Publication Details

Published Paper ID: JETIR1807967
Registration ID: 186110
Published In: Volume 5 | Issue 7 | Year July-2018
DOI (Digital Object Identifier):
Page No: 454-457
Country: Gargoti, MAHARASHTRA, India .
Area: Engineering
ISSN Number: 2349-5162


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