Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs
Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs
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发表时间：
2021-10
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通讯作者：
Nurendra Choudhary;Nikhil S. Rao;S. Katariya;Karthik Subbian;Chandan K. Reddy
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作者：
Nurendra Choudhary;Nikhil S. Rao;S. Katariya;Karthik Subbian;Chandan K. Reddy
知识图上的逻辑推理是在大型不完整数据库上提供高效查询机制的基础技术。目前的方法采用空间几何形状，如框来学习查询表示，包括答案实体和模型的投影和交集的逻辑运算。然而，它们的几何形状是限制性的，并导致非光滑的严格边界，这进一步导致模糊的答案实体。此外，以前的作品提出了转换技巧来处理非封闭的结果，因此，不能链接在一个流的工会。在本文中，我们提出了一个概率实体表示模型（PERM）编码的实体作为一个多元高斯密度与平均值和协方差参数，以捕捉其语义位置和平滑的决策边界，分别。此外，我们还定义了可以使用端到端目标函数聚合的投影，交集和并集的闭合逻辑运算。在逻辑查询推理问题上，我们证明了所提出的PERM显着优于各种公共基准KG数据集上的标准评估指标的最先进的方法。我们还评估了PERM在COVID-19药物再利用案例研究中的能力，并表明我们提出的工作能够推荐比当前方法更好的F1药物。最后，我们展示了我们的PERM的查询回答过程中，通过低维可视化的高斯表示的工作。
Logical reasoning over Knowledge Graphs (KGs) is a fundamental technique that can provide efficient querying mechanism over large and incomplete databases. Current approaches employ spatial geometries such as boxes to learn query representations that encompass the answer entities and model the logical operations of projection and intersection. However, their geometry is restrictive and leads to non-smooth strict boundaries, which further results in ambiguous answer entities. Furthermore, previous works propose transformation tricks to handle unions which results in non-closure and, thus, cannot be chained in a stream. In this paper, we propose a Probabilistic Entity Representation Model (PERM) to encode entities as a Multivariate Gaussian density with mean and covariance parameters to capture its semantic position and smooth decision boundary, respectively. Additionally, we also define the closed logical operations of projection, intersection, and union that can be aggregated using an end-to-end objective function. On the logical query reasoning problem, we demonstrate that the proposed PERM significantly outperforms the state-of-the-art methods on various public benchmark KG datasets on standard evaluation metrics. We also evaluate PERM's competence on a COVID-19 drug-repurposing case study and show that our proposed work is able to recommend drugs with substantially better F1 than current methods. Finally, we demonstrate the working of our PERM's query answering process through a low-dimensional visualization of the Gaussian representations.