论文标题
寻求：知识图的细分嵌入
SEEK: Segmented Embedding of Knowledge Graphs
论文作者
论文摘要
近年来，知识图嵌入成为人工智能的一个非常热门的研究主题，并且在各种下游应用程序中扮演着越来越重要的角色，例如建议和问题答案。但是，现有的知识图嵌入方法无法在模型复杂性和模型表现力之间进行适当的权衡，这使它们远非令人满意。为了减轻此问题，我们提出了一个轻巧的建模框架，该框架可以实现高度竞争性的关系表达性而不会增加模型的复杂性。我们的框架着重于评分功能的设计，并突出了两个关键特征：1）促进足够的特征相互作用； 2）保留关系的对称性和反对称特性。值得注意的是，由于评分功能的一般设计设计，我们的框架可以将许多著名的现有方法纳入特殊情况。此外，对公共基准测试的广泛实验证明了我们框架的效率和有效性。源代码和数据可在\ url {https://github.com/wentao-xu/seek}找到。
In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering. However, existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them still far from satisfactory. To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations. It is noteworthy that owing to the general and elegant design of scoring functions, our framework can incorporate many famous existing methods as special cases. Moreover, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework. Source codes and data can be found at \url{https://github.com/Wentao-Xu/SEEK}.
