Abstract:

In view of at the problem that the traditional knowledge map link prediction task ignores the possible semantic level between knowledge and the low link prediction results caused by the uncertainty of knowledge, this study proposes a Gaussian level-aware knowledge map link prediction model. In the model, the Gaussian embedding part introduces the Gaussian distribution information of entities and relationships, and the distance between the entity distribution and the relationship distribution is used to measure whether there is a link between entities. The word vector embedding part converts the word vectors of entities and relations into complex vectors. The complex vector of words is mapped to the semantic level of the modeling entities in the polar coordinate system, and the distance between the embedding vectors is used to measure whether there is a link between entities. According to the D-S evidence theory, the score function of the two parts is fused to achieve accurate knowledge map link prediction. The experimental results show that the model can effectively model the semantic level and uncertainty of entities in the knowledge graph, and is superior to other methods on the existing benchmark data sets.

Key words: artificial intelligence, knowledge map, knowledge representation, word vector, link prediction, Gaussian embedding, polar coordinate system, D-S evidence theory