基于区域子图的实体相关性度量

以实体关系图为研究背景,提出了基于区域子图的实体相关性度量方法.该方法从实体的邻居节点出发,通过定义实体的区域子图,对实体的语义上下文环境进行统一描述.为了快速有效地实现不同区域子图间的相似性计算,将区域子图转化为近似语义树结构,并利用树核函数,以计算语义树中相同子结构数量的方法对实体的相关性进行计算.最后,根据实验结果,对该方法的性能进行评估,结果显示该方法具有较好的准确率和运行效率.

Entity Relevance Based on the Area Subgraph

A new method for the measurement of entity relevance was proposed on the basis of entity relationship graph. By defining the area graph, the semantic of each entity was well presented. To speed up the calculation of the similarity between two area subgraphs, the area subgraphs were first converted into semantic trees. Then the kernel function was used to calculate the similarity by counting the number of shared subtrees. At last, the proposed methods were evaluated on the basis of the experimental results. The experimental results proved that the proposed method had a good performance in both accuracy and efficiency.