薛醒思（Xingsi Xue），received the Ph.D. degree in Computer Application Technology from Xidian University， in 2014. He is a professor at College of Information Science and Engineering， Intelligent Information Processing Research Center， Fujian Provincial Key Laboratory of Big Data Mining and Applications， Fujian Key Lab for Automotive Electronics and Electric Drive， Fujian University of Technology. He is a member of IEEE and ACM， and won 2017 ACM Xi'an Rising Star Award and IIH-MSP 2016 excellent paper award，陳俊風(fēng)（Junfeng Chen），received the Ph.D. degree from the College of Control Science and Engineering， Zhejiang University in 2011. Currently， she is an associate professor in the College of loT Engineering， Hohai University. She is a member of IEEE and ACM.潘正祥（Jeng-Shyang Pan）received the Ph D. degree in Electrical Engineering from the University of Edinburgh in 1996. He is a professor at the College of Information Science and Engineering， Intelligent Information Processing Research Center， Fujian Provincial Key Laboratory of Big Data Mining and Applications， Fujian Key Lab for Automotive Electronics and Electric Drive， Fujian University of Technology. He was offered the Thousand Talents Plan in China in 2010.
圖書(shū)目錄
Chapter 1 Evolutionary Algorithm based Ontology Schema-level Matching Technique 1．1 Preliminaries 1．1．1 Ontology， Ontology Matching， Ontology Alignment 1．1．2 Similarity Measure 1．2 Optimizing Ontology Alignments through Memetic Algorithm Using both MatchFmeasure and Unanimous Improvement Ratio 1．2．1 MatchFmeasure and Unanimous Improvement Ratio 1．2．2 MA Using MatchFmeasure and UIR 1．2．3 Experimental Results and Analysis 1．2．4 Conclusion and Future Work 1．3 Using Problem-speciˉc MOEA/D for Optimizing Ontology Alignments 1．3．1 Multi-Objective Ontology Matching Problem 1．3．2 MOEA/D for Optimizing Ontology Alignments 1．3．3 Experimental Results and Analysis 1．3．4 Conclusion and Future Work Chapter 2 Evolutionary Algorithm based Ontology Instance-level Matching Technique 2．1 Using Memetic Algorithm for Instance Coreference Resolution 2．1．1 Similarity Measure for Instance Coreference Resolution 2．1．2 Memetic Algorithm for Instance Coreference Resolution 2．1．3 Experimental Results and Analysis 2．1．4 Conclusion and Future Work 2．2 Many-Objective Instance Matching in Linked Open Data 2．2．1 Many-Objective Instance Matching 2．2．2 NSGA-III based Many-Objective Instance Matching 2．2．3 Experimental Studies and Analysis 2．2．4 Conclusion and Future Work Chapter 3 Improving the Performance of Evolutionary Algorithm based Ontology Matching Technique 3．1 An Alignment-Oriented Segmenting Approach for Optimizing Large Scale Ontology Alignments 3．1．1 The Framework of Segment-based Large Scale Ontology Matching Approach 3．1．2 Source Ontology Partition 3．1．3 Target Ontology Segment Determination 3．1．4 Ontology Segment Matching through the Hybrid Evolutionary Algorithm 3．1．5 Experimental Results and Analysis 3．1．6 Conclusion 3．2 E±cient Ontology Matching Using Meta-Model assisted NSGA-II 3．2．1 Error Ratio based Dynamic Alignment Candidates Selection Strategy 3．2．2 NSGA-II for Optimizing Ontology Alignment 3．2．3 Gaussian Random Field Model 3．2．4 Experimental Results and Analysis 3．2．5 Conclusion and Future Work 3．3 Using Compact Memetic Algorithm for Optimizing Ontology Alignment 3．3．1 Hybrid Population-based Incremental Learning Algorithm 3．3．2 Experimental Studies and Analysis 3．3．3 Conclusion and Future Work Reference
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