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

薛醒思、陈俊风、潘正祥主编的《基于进化算法的本体匹配技术(英文版)》描述了领域间的概念以及概念间的关系，是解决语义网上数据异质问题的方案。但是由于人类的主观性，同一个实体在不同本体中可能拥有不同的名称和描述方式，使得本体间存在异质问题。给定两个描述一系列离散的实体（实体可能是概念、关系和实例）的本体,确定这些本体间的关系的过程称为本体匹配，本体匹配可以有效地解决本体异质问题。当本体中的实体规模庞大的时候，本体匹配问题是一个复杂的（非线性问题且有很多局部最优解）和费时的（大规模问题）问题，因此近似的求解方法通常被用于确定本体匹配结果。源自这一观点，进化算法成为了求解本体匹配问题的有效方法。本书首先为本体概念层和实例层构建了不同的单目标、多目标和众目标模型，然后针对性地给出了各种进化算法（如混合进化算法，NSGA-II和MOEA/D）来求解这些模型。最后，还描述了各种提高基于进化算法的本体匹配技术性能的方法，如本体划分算法、紧凑编码方案、并行匹配框架和元模型辅助策略等，这些方法可以显著地减少运行时、内存消耗和算法所需的评价次数。