论文标题
跨印I
TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces
论文作者
论文摘要
知识图（KG）由实体和关系组成，提供了知识的结构化表示。为了轻松访问关系数据的统计方法，已经引入了多种将kg嵌入f（kg）$ \ $ r^d中的方法。我们提出了TransInt，这是一种新颖且可解释的KG嵌入方法，同构保留了嵌入空间中关系之间的含义排序。鉴于含义规则，跨印IS映射集合集合（由关系绑定）与连续的向量集，这些向量均与关系的含义同构为含义。借助新的参数共享方案，Transint可以自动培训缺失但隐含的事实而没有规则接地。在基准数据集上，我们的表现优于最佳现有的最新规则集成嵌入方法，其链接预测和三重分类的利润很大。 Transint嵌入的连续集合之间的角度为挖掘语义相关性和关系中的含义规则提供了一种可解释的方式。
Knowledge Graphs (KG), composed of entities and relations, provide a structured representation of knowledge. For easy access to statistical approaches on relational data, multiple methods to embed a KG into f(KG) $\in$ R^d have been introduced. We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space. Given implication rules, TransINT maps set of entities (tied by a relation) to continuous sets of vectors that are inclusion-ordered isomorphically to relation implications. With a novel parameter sharing scheme, TransINT enables automatic training on missing but implied facts without rule grounding. On a benchmark dataset, we outperform the best existing state-of-the-art rule integration embedding methods with significant margins in link Prediction and triple Classification. The angles between the continuous sets embedded by TransINT provide an interpretable way to mine semantic relatedness and implication rules among relations.
