Ontology matching is an effective technique to solve the ontology heterogeneous problem in Semantic Web. Since different ontology matchers do not necessarily find the same correct correspondences, usually several competing matchers are applied to the same pair of entities in order to increase evidence towards a potential match or mismatch. How to select, combine and tune various ontology matchers to obtain the high quality ontology alignment is one of the main challenges in ontology matching domain. Recently, Evolutionary Algorithms (EA) has become the most suitable methodology to face this challenge, however, the huge memory consumption, slow convergence and premature convergence limit its application and reduce the solution’s quality. To this end, in this paper, we propose a Hybrid Population-based Incremental Learning algorithm (HPBIL) to automatically select, combine and tune different ontology matchers, which can overcome three drawbacks of EA based ontology matching techniques and improve the ontology alignment’s quality. In one hand, HPBIL makes use of a probabilistic representation of the population to perform the optimization process, which can significantly reduce EA’s the memory consumption and the possibility of the premature convergence. In the other, we introduce the local search strategy into PBIL’s evolving process to trade off its exploration and exploitation, and this marriage between global search and local search is helpful to reduce the runtime. In the experiment, we utilize different scale testing cases provided by the Ontology Alignment Evaluation Initiative (OAEI 2016) to test HPBIL’s performance, and the experimental results show that HPBIL’s results significantly outperform other EA based ontology matching techniques and top-performers of the OAEI competitions.

中文翻译：

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通过基于种群的混合增量学习算法优化本体对齐

本体匹配是解决语义网中本体异构问题的有效技术。由于不同的本体匹配器不一定找到相同的正确对应关系，因此通常将多个竞争匹配器应用于同一对实体，以增加针对潜在匹配或不匹配的证据。如何选择，组合和调优各种本体匹配器以获得高质量的本体比对是本体匹配领域的主要挑战之一。最近，进化算法（EA）成为应对这一挑战的最合适方法，但是，巨大的内存消耗，缓慢的收敛和过早的收敛限制了其应用并降低了解决方案的质量。为此，在本文中，我们提出一种基于混合种群的增量学习算法（HPBIL），以自动选择，组合和调整不同的本体匹配器，它可以克服基于EA的本体匹配技术的三个缺点，并提高本体对齐的质量。一方面，HPBIL利用总体的概率表示来执行优化过程，这可以显着减少EA的内存消耗和过早收敛的可能性。另一方面，我们将本地搜索策略引入PBIL的不断发展的过程中，以权衡其探索和开发，而全局搜索与本地搜索之间的这种结合有助于减少运行时间。在实验中