Knowledge graph completion is an important task that aims to predict the missing relational link between entities. Knowledge graph embedding methods perform this task by representing entities and relations as embedding vectors and modeling their interactions to compute the matching score of each triple. Previous work has usually treated each embedding as a whole and has modeled the interactions between these whole embeddings, potentially making the model excessively expensive or requiring specially designed interaction mechanisms. In this work, we propose the multi-partition embedding interaction (MEI) model with block term format to systematically address this problem. MEI divides each embedding into a multi-partition vector to efficiently restrict the interactions. Each local interaction is modeled with the Tucker tensor format and the full interaction is modeled with the block term tensor format, enabling MEI to control the trade-off between expressiveness and computational cost, learn the interaction mechanisms from data automatically, and achieve state-of-the-art performance on the link prediction task. In addition, we theoretically study the parameter efficiency problem and derive a simple empirically verified criterion for optimal parameter trade-off. We also apply the framework of MEI to provide a new generalized explanation for several specially designed interaction mechanisms in previous models. The source code is released at https://github.com/tranhungnghiep/MEI-KGE.

翻译：知识图的完成是一项重要任务,目的是预测各实体之间缺少的关系联系。 知识图嵌入方法通过将实体和关系作为嵌入矢量来代表实体和关系来完成这项任务,并模拟它们的互动,以计算每三倍的匹配分数。 以往的工作通常将每个嵌入全部,并模拟了整个嵌入过程之间的相互作用,有可能使模型过于昂贵,或需要专门设计的互动机制。 在这项工作中,我们提议多部分嵌入互动(MEI)模式,采用轮期格式来系统解决这一问题。 MEI将每个嵌入多部分矢量以有效限制互动。 每种本地互动都以塔克高压格式为模型,而全面互动则以区段术语 " 点 " 格式为模型,使MEI能够控制表达性和计算成本之间的取舍,自动从数据中学习互动机制,并在链接预测任务中实现最先进的性能性能。 此外,我们理论上研究参数效率问题,并得出一个经经验验证的最佳参数交易标准。 我们还应用了MEAI框架,而全面互动则以区段格式模式为模型。