TransG：一种用于知识图谱嵌入的生成混合模型
TransG : A Generative Mixture Model for Knowledge Graph Embedding
摘要：
知识图谱嵌入是将符号实体和符号关系映射到连续向量空间中的一种方法，近年来已成为人工智能领域的一个新的研究热点。针对多元关系语义中一个关系可能具有多元语义的新问题，提出了一种新的基于高斯混合模型的嵌入算法TransG.新模型可以发现关系的潜在语义，并利用混合的关系分量向量来嵌入事实三元组。据我们所知，这是第一个生成模型的知识图谱嵌入，这是能够处理多个关系语义。大量的实验表明，该模型实现了对国家的最先进的基线大幅改善。
英文摘要：
Recently, knowledge graph embedding, which projects symbolic entities and relations into continuous vector space, has become a new, hot topic in artificial intelligence. This paper addresses a new issue of multiple relation semantics that a relation may have multiple meanings revealed by the entity pairs associated with the corresponding triples, and proposes a novel Gaussian mixture model for embedding, TransG. The new model can discover latent semantics for a relation and leverage a mixture of relation component vectors for embedding a fact triple. To the best of our knowledge, this is the first generative model for knowledge graph embedding, which is able to deal with multiple relation semantics. Extensive experiments show that the proposed model achieves substantial improvements against the state-of-the-art baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
