Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction

Abstract

Knowledge graphs represent known facts using triplets. While existing knowledge graph embedding methods only consider the connections between entities, we propose considering the relationships between triplets. For example, let us consider two triplets $T_1$ and $T_2$ where $T_1$ is (Academy_Awards, Nominates, Avatar) and $T_2$ is (Avatar, Wins, Academy_Awards). Given these two base-level triplets, we see that $T_1$ is a prerequisite for $T_2$. In this paper, we define a higher-level triplet to represent a relationship between triplets, e.g., $⟨T_1$, PrerequisiteFor, $T_2⟩$ where PrerequisiteFor is a higher-level relation. We define a bi-level knowledge graph that consists of the base-level and the higher-level triplets. We also propose a data augmentation strategy based on the random walks on the bi-level knowledge graph to augment plausible triplets. Our model called BiVE learns embeddings by taking into account the structures of the base-level and the higher-level triplets, with additional consideration of the augmented triplets. We propose two new tasks: triplet prediction and conditional link prediction. Given a triplet $T_1$ and a higher-level relation, the triplet prediction predicts a triplet that is likely to be connected to $T_1$ by the higher-level relation, e.g., $⟨T_1$, PrerequisiteFor, ?$⟩$. The conditional link prediction predicts a missing entity in a triplet conditioned on another triplet, e.g., $⟨T_1$, PrerequisiteFor, (Avatar, Wins, ?)$⟩$. Experimental results show that BiVE significantly outperforms all other methods in the two new tasks and the typical base-level link prediction in real-world bi-level knowledge graphs.

14 pages, 3 figures, 15 tables. 37th AAAI Conference on Artificial Intelligence (AAAI 2023)

References in corpus (5)

• DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
• Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers
• InGram: Inductive Knowledge Graph Embedding via Relation Graphs
• Rule-Guided Compositional Representation Learning on Knowledge Graphs
• ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers

Cited by in corpus (2)

• Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers
• A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges