时序知识图谱的增量构建

Incremental Construction of Time-Series Knowledge Graph

带有时序特征的知识图谱(KG)称为时序知识图谱,用来描述知识库中增量式的概念及其相互关系。知识随着时间推移而变化,将新增知识实时、准确地添加到时序知识图谱中,可以实时反映知识的演化更新。对此,给出时序知识图谱的定义,并基于TransH提出一种时序知识图谱的增量构建方法。为了将新增且相关的三元组准确地添加到当前知识图谱中,提出了三元组与当前知识图谱之间吻合度的计算模型,以及基于贪心思想的待添加到知识图谱中的最优三元组子集提取算法,进而将最优的三元组集合添加到当前知识图谱中,完成时序知识图谱的增量更新。实验结果表明,提出的增量构建方法能够快速地提取出最优三元组并有效地添加到知识图谱中,验证了方法的高效性和有效性。

Knowledge graph(KG)with time-series feature is referred to as time-series KG,which depicts the incremental concepts and corresponding relations in knowledge base.In view of knowledge being dramatically changing,by adding new knowledge to time-series KG,the evolution and update of knowledge can be reflected in time.Thus,this paper gives the definition of time-series KG and proposes the method for its incremental construction model based on TransH.In order to add new and relevant triple set to time-series KG,this paper proposes a model for calculating the coincidence between the triple and the current KG,and the technique for extracting the optimal triples by the idea of greedy algorithm.Then,the optimal set of triples is added to the time-series KG and the incremental update is fulfilled.Experimental results show that optimal triples can be extracted efficiently and added into the time-series KG by the proposed method.The effectiveness and efficiency of the method are verified.