一种结合层次化类别信息的知识图谱表示学习方法

Knowledge Graph Embedding Combining with Hierarchical Type Information

知识图谱表示学习方法旨在将知识图谱中的实体和关系嵌入到低维连续的向量空间.由于知识图谱本身具有数据稀疏性的问题导致学习出的向量表示性能欠缺.实体的类别信息包含了丰富的语义,引入它能够更好地指导向量表示的学习.已有结合类别信息的表示学习方法要么不支持类别信息的层次化结构或者关系的类别约束,要么对层次化结构的建模过于复杂.提出一种结合层次化类别信息的表示学习方法.我们将类别嵌入到不同的向量空间,使用偏序关系建模类别的层次化结构.同时,将实体向量表示映射到类别向量空间中,要求实体与其所属类别满足偏序关系,且三元组的实体与其关系的类别约束也满足偏序关系.最后,在多个数据集上执行链接预测、三元组分类和实体分类任务的实验结果表明我们的方法相比其他基线方法学习出的向量表示性能更好.

Knowledge graph embedding aims to embed entities and relations into a low-dimensional continuous vector space. Due to the data sparsity of knowledge graphs, the performance of knowledge graph embedding is poor in vector representation. Since the type information of entities encompasses rich semantic information, it is introduced to improve the performance. However, the existing methods either do not support the hierarchical structure of type information or the type constraint of relations or complicate the model of the hierarchical structure. This study proposes a novel knowledge graph embedding method combining with hierarchical type information.Specifically, types are embedded into different vector spaces and the hierarchical structure of types is modeled by the partial order relation.Moreover, the vector representations of entities are mapped into the type vector space so that entities and their types can be required to satisfy the partial order relation. The entities and their type constraint of relations in triples are also made to satisfy the partial order relation. Finally, experimental results of link prediction, triple classification and entity typing task on four datasets show that the proposed method outperforms the state-of-the-art baseline methods in vector representation performance.