Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs

Abstract

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.

arXiv commentAccepted at Thirty-fifth Conference on Neural Information Processing Systems 2021 (NeurIPS '21)

References in corpus (6)

• Adversarial Attacks on Neural Networks for Graph Data
• Adversarial Attack on Graph Structured Data
• Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
• AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
• Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
• Faithful Embeddings for Knowledge Base Queries

Cited by in corpus (2)

• SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs
• NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs