基于二值相似度计算的异构本体融合方法

An Ontology Fusion Method Based on Binary Similarity Calculation

异构本体的存在带来了知识检索的冗余,基于异构本体的知识融合是十分必要的。大量的语义相似度计算容量与复杂的计算过程使得知识融合变得困难,本文提出二值相似度计算的异构本体融合方法,将语义相似度的计算提前至原始本体的构建过程,融合时只进行概念和关系的二值匹配,从而简化融合过程再次计算语义相似度的过程。文章从实体图书元数据、小样本本体和大样本本体三个角度组织了三个实验,利用武汉大学图书馆书目数据的实验一显示本文方法可以完成本体融合的过程,实验二和实验三显示本文方法可以提高本体融合的准确性,并显著提高运行反馈时间,综合反映本体融合效果良好,但需要在召回率上进行改进。本文方法有望在扩展专家本体、减少本体构建开销等方面体现应用价值。

Heterogeneous ontology causes redundancy in knowledge retrieval. Therefore, knowledge fusion based on het erogeneous ontology is necessary. However, because of the massive capacity and complicated processes required for se mantic similarity computing, knowledge fusion has become less simple. In this paper, we propose an ontology fusion meth od based on binary metrics of semantic similarity calculation. In the fusion process, there will be only binary matching, thus aiming to further simplify the calculation of fusion from semantic similarity. Thus, the present research represents a shift from methods locating computing progress at the beginning of original ontology construction. We adopted three experi ments to test the usability of our approach, from the perspectives of (1) actual library resources,(2) a small dataset, and (3) a large dataset. In experiment one, bibliographic data from Wuhan University Library were used to test our proposal s feasi bility and capabilities. Results showed that our approach can completely merge two ontologies into a single theme. The sec ond and third experiments both verified that our approach has the ability to accurately detect merging couples and decrease time cost. The tests demonstrated a good overall fusion result;nevertheless, recall requires future improvement. This meth od is expected to extend the implementation of expert ontology and aid in cost reduction of ontology construction.