论文标题
伽玛：伽马嵌入知识图上逻辑查询的嵌入
GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs
论文作者
论文摘要
嵌入多跳逻辑推理的知识图（kgs）是一个具有挑战性的问题，这是由于许多公斤的庞大而复杂的结构。最近，许多有前途的作品投射实体和查询成几何空间，以有效地找到答案。但是，建模否定和工会运营商仍然具有挑战性。否定操作员没有严格的边界，这会产生重叠的嵌入，并导致获得模棱两可的答案。另一个限制是工会运营商是不关闭的，这破坏了处理一系列工会运营商的模型。为了解决这些问题，我们提出了一个新颖的概率嵌入模型，即伽玛嵌入（Gammae），用于编码实体和查询，以回答KGS上不同类型的FOL查询。我们利用伽马分布的线性特性和强大的边界支持来捕获实体和查询的更多特征，从而大大降低了模型的不确定性。此外，伽马木实现了伽马混合物方法来设计封闭的联合操作员。在三个大型逻辑查询数据集上验证了γ的性能。实验结果表明，在公共基准上，伽玛显着优于最先进的模型。
Embedding knowledge graphs (KGs) for multi-hop logical reasoning is a challenging problem due to massive and complicated structures in many KGs. Recently, many promising works projected entities and queries into a geometric space to efficiently find answers. However, it remains challenging to model the negation and union operator. The negation operator has no strict boundaries, which generates overlapped embeddings and leads to obtaining ambiguous answers. An additional limitation is that the union operator is non-closure, which undermines the model to handle a series of union operators. To address these problems, we propose a novel probabilistic embedding model, namely Gamma Embeddings (GammaE), for encoding entities and queries to answer different types of FOL queries on KGs. We utilize the linear property and strong boundary support of the Gamma distribution to capture more features of entities and queries, which dramatically reduces model uncertainty. Furthermore, GammaE implements the Gamma mixture method to design the closed union operator. The performance of GammaE is validated on three large logical query datasets. Experimental results show that GammaE significantly outperforms state-of-the-art models on public benchmarks.
