一种综合事件本体相似度计算方法

事件本体相比于传统本体具有更加丰富的语义信息，在面向事件的大数据集成中更具优势，然而用传统的本体相似计算方法计算事件本体相似度存在很多不足，提出了一种综合的事件本体相似度计算方法。该方法以词语相似度、集合相似度、层次结构相似计算为基础，然后从事件类名称、事件类要素、事件类层次结构和非层次结构讨论事件本体的相似度，最终获得事件本体的综合相似度。实验表明该方法相比传统本体相似度计算方法准确率更高，语义信息更加丰富。

Comprehensive approach for event ontology similarity computation

Event ontology had more rich semantic information than traditional ontology, and had more advantages in event-oriented big data integration. There were many deficiencies in calculating the similarity of event ontology using traditional ontology similarity computation methods. So a comprehensive approach for calculating similarity of event ontology was proposed. This approach was based on words similarity computation, set similarity computation and hierarchical similarity computation, then, discussed event ontology similarity from the event class name, event class elements, event class hierarchy and event class non-hierarchy structure, finally got the comprehensive similarity of event ontology. Experimental results show that this approach is more accurate than the traditional ontology similarity computation approach and its semantic information is more abundant.