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
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