• 1Mayur A. Pilankar

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.

knowledge bases, crowdsourcing, semantic web, relevance.

"Knowledge Base Semantic Integration using Crowdsourcing Through Ontological Model and Hidden Markov Chain ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.30-35, February-2019, Available :http://www.jetir.org/papers/JETIR1902303.pdf

"Knowledge Base Semantic Integration using Crowdsourcing Through Ontological Model and Hidden Markov Chain ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp30-35, February-2019, Available at : http://www.jetir.org/papers/JETIR1902303.pdf