一种基于IC参数的知识图谱嵌入方法

Knowledge Graph Embedding Based on IC Parameters

TransC是一种高效的知识图谱嵌入方法,通过区分概念和实例来建立概念、实例及关系的嵌入。TransC将概念编码为球体,球体半径被随机初始化并在训练中迭代更新。由此导致模型出现两个问题:一是训练得到的部分球体半径与模型训练目标不符;二是忽略了概念本身提供的语义信息。针对上述两个问题,该文提出了TransIC模型,首先,基于IC参数给出新的概念球体半径求解方法,使求得的半径满足TransC目标,并且丰富了概念嵌入向量的语义信息。其次,该模型以TransC为基础,在概念编码阶段引入基于IC参数的概念球体半径。最后,在公开的数据集YAGO39K上完成链接预测和三元组分类两个任务,并将该文方法实验所得性能与TransC及其他模型的性能进行对比。结果表明,TransIC在多数指标上均取得显著提升。

TransC is an efficient method for embedding knowledge graphs.It establishes the embedding of concepts,instances,and relations by distinguishing concepts and instances.TransC encodes the concept as a sphere,and the radius of the sphere is randomly initialized and updated iteratively during training.This leads to two problems in the model.First,part of the sphere radius obtained from training does not match the model training target.Second,the semantic information provided by the concept itself is ignored.This paper proposes a model named TransIC to deal with the two issues above.TransIC adopts a novel concept sphere radius solution method based on IC parameters,so that the obtained radius meets the TransC goal,and enriches the semantic information of the concept embedding vector.Then it is based on TransC and introduces a concept sphere radius based on IC parameters during the concept coding phase.Finally,the two tasks of link prediction and triple classification are completed on the public data set YAGO39 K,and the experimental performance of the method in this paper is compared with the performance of TransC and other models.The results show that TransIC has achieved a significant improvement in most indicators.