Han Xiao

Learn More
Knowledge representation is a major topic in AI, and many studies attempt to represent entities and relations of knowledge base in a continuous vector space. Among these attempts , translation-based methods build entity and relation vectors by minimizing the translation loss from a head entity to a tail one. In spite of the success of these methods,(More)
Knowledge graph embedding aims at offering a numerical knowledge representation paradigm by transforming the entities and relations into continuous vector space. However, existing methods could not characterize the knowledge graph in a fine degree to make a precise link prediction. There are two reasons for this issue: being an ill-posed algebraic system(More)
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 proposes a novel gen-erative model (TransG) to address the issue of multiple relation semantics that a relation may have multiple meanings revealed by the entity pairs associated(More)
Knowledge representation is a critical topic in AI, and currently embedding as a key branch of knowledge representation takes the numerical form of entities and relations to joint the statistical models. However, most embedding methods merely concentrate on the triple fitting and ignore the explicit semantic expression, leading to an uninterpretable(More)
Knowledge graph embedding represents the entities and relations as numerical vectors, and then knowledge analysis could be promoted as a numerical method. So far, most methods merely concentrate on the fact triples that are composed by the symbolic entities and relations, while the textual information which is supposed to be most critical in NLP could(More)
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(More)
Knowledge graph embedding aims at offering a numerical representation paradigm for knowledge by transforming the entities and relations into continuous vector space. This paper studies the problem of unsatisfactory precise prediction, that existing methods could not express the knowledge in a fine degree to make a precise prediction. To alleviate this(More)
  • 1