How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View (2109.11800v3)

Abstract: Knowledge Graph Embedding (KGE) aims to learn representations for entities and relations. Most KGE models have gained great success, especially on extrapolation scenarios. Specifically, given an unseen triple (h, r, t), a trained model can still correctly predict t from (h, r, ?), or h from (?, r, t), such extrapolation ability is impressive. However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate. Therefore in this work, we attempt to study the KGE extrapolation of two problems: 1. How does KGE extrapolate to unseen data? 2. How to design the KGE model with better extrapolation ability? For the problem 1, we first discuss the impact factors for extrapolation and from relation, entity and triple level respectively, propose three Semantic Evidences (SEs), which can be observed from train set and provide important semantic information for extrapolation. Then we verify the effectiveness of SEs through extensive experiments on several typical KGE methods. For the problem 2, to make better use of the three levels of SE, we propose a novel GNN-based KGE model, called Semantic Evidence aware Graph Neural Network (SE-GNN). In SE-GNN, each level of SE is modeled explicitly by the corresponding neighbor pattern, and merged sufficiently by the multi-layer aggregation, which contributes to obtaining more extrapolative knowledge representation. Finally, through extensive experiments on FB15k-237 and WN18RR datasets, we show that SE-GNN achieves state-of-the-art performance on Knowledge Graph Completion task and performs a better extrapolation ability. Our code is available at https://github.com/renli1024/SE-GNN.

Semantic Evidence for Extrapolation in Knowledge Graph Embedding

The paper "How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View" investigates the extrapolation capabilities of Knowledge Graph Embedding (KGE) models. Although KGE methods have demonstrated success in predicting unseen data, the rationale behind their extrapolation abilities remains insufficiently explored. This paper explores two critical questions: how KGE extrapolates to unseen data and how to design KGE models with enhanced extrapolation capabilities.

Key Contributions

1. Semantic Evidence Framework: The authors introduce the concept of Semantic Evidence (SE), comprising three levels of evidence that influence the extrapolative capabilities of KGE models:
   • Relation Level SE: This evidence considers the co-occurrence of relations and entities, evaluating how often a specific relation is paired with an entity in the training data.
   • Entity Level SE: This level assesses the direct or indirect connectivity between entities, implying semantic relatedness gained from path connections within the graph.
   • Triple Level SE: This involves the similarity between the entities present in the training data and the entities predicted in the test set.
2. Verification through Experiments: Through extensive experimentation on the FB15k-237 and WN18RR datasets, the paper validates the importance of SEs in KGE extrapolation. Results show a correlation between the strength of SEs and improved extrapolation performance across various KGE models.
3. SE-GNN Model Proposal: The paper proposes a novel Graph Neural Network-based model, Semantic Evidence aware Graph Neural Network (SE-GNN). This model explicitly incorporates the three levels of SEs into the KGE process. By structuring SEs as distinct neighbor patterns, SE-GNN outperforms existing models in extrapolation tasks on knowledge graph completion (KGC).

Experimental Outcomes

• The SE-GNN model consistently achieved state-of-the-art results in KGC tasks across the FB15k-237 and WN18RR datasets, demonstrating superior extrapolation abilities by effectively capitalizing on SE information.
• Comparative analysis with other models confirms that explicit SE modeling through SE-GNN leads to more robust extrapolative performance.

Implications and Future Directions

The paper provides a new perspective on enhancing KGE effectiveness by leveraging semantic structures within knowledge graphs. Practically, this insight can be transformative for domains relying on KGC tasks, such as recommendation systems and automated reasoning. Theoretically, it paves the way for future explorations into different types of semantic evidence and their integration into KGE models.

Future research could focus on:

• Exploring additional types of semantic evidence to further enhance extrapolation performance.
• Expanding SE-GNN to incorporate different GNN architectures or embedding techniques, optimizing its effectiveness across diverse domains and datasets.

Conclusively, this paper offers a compelling methodology for improving KGE models' capability to extrapolate to unseen data, establishing a valuable foundation for further advancements in AI applications reliant on knowledge graph embeddings.