融合多算法评估中英文知识图谱外延简洁性新方法

New Method for Integrating Multiple Algorithms to Assess Extension Conciseness of Chinese and English Knowledge Graphs

针对目前人们只是从理论上提出了针对知识图谱外延简洁性评估指标,并没有给出针对该指标规范的评估方法及流程问题,对知识图谱外延简洁性评估方法进行了研究,提出了支持中英文混合知识图谱外延简洁性评估的新方法。该方法定义了从总体层面进行分组以及分别对头实体、关系和尾实体进行评估的公式,同时为保障评估的准确性,定义了句子层面的评估公式。最后,将4种评估公式联合,得到了对知识图谱外延简洁性指标进行评估的算法。为验证所提出算法的准确性和性能,利用开放数据集OPEN KG(Knowledge Graph),对提出的算法和相关的算法进行了评估与比对,结果验证了本算法对中英文混合知识图谱简洁性评估方面的准确性、时间效率都具有一定的保障,综合性能高于相关的算法。

So far,the international community has only proposed an assessment metric for the extension conciseness of knowledge graph,but has not provided a standardized assessment method and process.To address this issue,the assessment method of the extension conciseness of knowledge graph is studied and a new method to assess the extension conciseness of the Chinese English mixed knowledge graph is proposed.The formulas for grouping at the overall level and assessing the head entities,relations,and tail entities are proposed and defined.To enhance the accuracy of the evaluation,the sentence level assessment formula is also defined.Finally,the four formulas are combined to create an algorithm for assessing the extension conciseness of the knowledge graph.To verify the accuracy and performance of the proposed algorithm,the open data set OPEN KG(Knowledge Graph)is used to assess and compare the proposed algorithm with related algorithms.The results confirm that the proposed algorithm provides a certain guarantee for the accuracy and time efficiency of the conciseness assessment of the Chinese English mixed knowledge graph,and the overall performance of the proposed algorithm is better than that of the related algorithm.