[PDF] SigmaKB: Multiple Probabilistic Knowledge Base Fusion | Semantic Scholar Skip to search formSkip to main content> Semantic Scholar's Logo Search Sign InCreate Free Account You are currently offline. Some features of the site may not work correctly. DOI:10.14778/3007263.3007313 Corpus ID: 5798408SigmaKB: Multiple Probabilistic Knowledge Base Fusion @article{Rodrguez2016SigmaKBMP, title={SigmaKB: Multiple Probabilistic Knowledge Base Fusion}, author={M. E. Rodr{\'i}guez and S. Goldberg and D. Wang}, journal={Proc. VLDB Endow.}, year={2016}, volume={9}, pages={1577-1580} } M. E. Rodríguez, S. Goldberg, D. Wang Published 2016 Computer Science Proc. VLDB Endow. The interest in integrating web-scale knowledge bases (KBs) has intensified in the last several years. Research has focused on knowledge base completion between two KBs with complementary information, lacking any notion of uncertainty or method of handling conflicting information. We present SigmaKB, a knowledge base system that utilizes Consensus Maximization Fusion and user feedback to integrate and improve the query results of a total of 71 KBs. This paper presents the architecture and… Expand View via Publisher vldb.org Save to Library Create Alert Cite Launch Research Feed Share This Paper 5 CitationsBackground Citations 2 Methods Citations 1 View All Figures, Tables, and Topics from this paper figure 1 table 1 figure 2 figure 3 figure 4 View All 5 Figures & Tables Knowledge base Expectation–maximization algorithm 5 Citations Citation Type Citation Type All Types Cites Results Cites Methods Cites Background Has PDF Publication Type Author More Filters More Filters Filters Sort by Relevance Sort by Most Influenced Papers Sort by Citation Count Sort by Recency Query answering over uncertain RDF knowledge bases: explain and obviate unsuccessful query results I. Dellal, S. Jean, A. HadjAli, B. 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