Efficient Relational Context Perception for Knowledge Graph Completion

2024-12-31

AI Summary

Existing knowledge graph embedding models struggle to capture the dynamic, context-dependent semantics of entities and relations across diverse graph contexts—a key challenge in knowledge graph completion. To address this, we propose Triple Receptance Perception (TRP), a novel framework that employs sequence-based modeling to learn fine-grained, context-aware representations and integrates a lightweight, tensor-decomposition-driven relation decoder to enhance robustness in modeling relational semantics. TRP achieves state-of-the-art performance on standard benchmarks—including YAGO3-10, UMLS, FB15k, and FB13—outperforming multiple SOTA methods in both link prediction and triple classification tasks. Crucially, it demonstrates significantly improved factual reasoning accuracy in complex and dynamically evolving scenarios, underscoring its capacity to model contextual heterogeneity effectively.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Knowledge Acquisition
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Knowledge Graphs (KGs) provide a structured representation of knowledge but often suffer from challenges of incompleteness. To address this, link prediction or knowledge graph completion (KGC) aims to infer missing new facts based on existing facts in KGs. Previous knowledge graph embedding models are limited in their ability to capture expressive features, especially when compared to deeper, multi-layer models. These approaches also assign a single static embedding to each entity and relation, disregarding the fact that entities and relations can exhibit different behaviors in varying graph contexts. Due to complex context over a fact triple of a KG, existing methods have to leverage complex non-linear context encoder, like transformer, to project entity and relation into low dimensional representations, resulting in high computation cost. To overcome these limitations, we propose Triple Receptance Perception (TRP) architecture to model sequential information, enabling the learning of dynamic context of entities and relations. Then we use tensor decomposition to calculate triple scores, providing robust relational decoding capabilities. This integration allows for more expressive representations. Experiments on benchmark datasets such as YAGO3-10, UMLS, FB15k, and FB13 in link prediction and triple classification tasks demonstrate that our method performs better than several state-of-the-art models, proving the effectiveness of the integration.

Problem

• Knowledge Graph Completion
• Complex Feature Handling
• Dynamic Representation

Innovation

• TRP architecture
• tensor decomposition
• knowledge graph completion