Knowledge Representation Learning Based on Multi-source Information Combination

Abstract

In knowledge graphs, there are rich contents hidden in the text description information of entity, the hierarchical type information of entity and the topological structure information of graph, and they can form an effective supplement to the triple information to get better performance. In order to make full use of these hetero-geneous information, the convolutional neural networks are firstly used to encode entity description. Then a projection matrix is constructed according to hierarchical type information to project entity vectors and entity description vectors into specific relation space to constrain their semantic information. After that, the graph attention mechanism is introduced to fuse the topological structure information of graph and calculate the influence of different adjacency points on entities. Meanwhile, the multi-hop relationship information between entities is calcu-lated to further solve the problem of data sparsity. Finally, a decoder is employed to capture the global information between different dimensions. Experimental results of link prediction show that the multi-source information com-bined knowledge representation learning (MCKRL) model can make good use of multi-source heterogeneous information beyond triples, so it obtains better results than other baseline models in link prediction.

Keywords

• |knowledge representation learning|entity description|hierarchical type|topological structure