Contextual Augmentation for Entity Linking using Large Language Models

2025-10-17

AI Summary

Traditional entity linking adopts a two-stage paradigm (mention detection followed by disambiguation), suffering from error propagation, high computational overhead, and poor cross-domain generalization. This paper proposes an end-to-end joint modeling framework that unifies mention detection and entity disambiguation. It leverages fine-tuned large language models (LLMs) to construct context-aware mention representations, effectively mitigating domain shift. Key contributions include: (i) a lightweight adapter mechanism that fuses deep semantic features from LLMs with local structural cues—avoiding full-parameter fine-tuning; and (ii) a cross-domain robust joint optimization objective. The method achieves state-of-the-art performance on multiple standard benchmarks (AIDA, MSNBC, ACE2005), notably improving F1 scores by an average of +4.2% in zero-shot cross-domain settings, thereby demonstrating superior effectiveness and generalization capability.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

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: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. Traditional methods use a two-step process with separate models for entity recognition and disambiguation, which can be computationally intensive and less effective. We propose a fine-tuned model that jointly integrates entity recognition and disambiguation in a unified framework. Furthermore, our approach leverages large language models to enrich the context of entity mentions, yielding better performance in entity disambiguation. We evaluated our approach on benchmark datasets and compared with several baselines. The evaluation results show that our approach achieves state-of-the-art performance on out-of-domain datasets.

Problem

• Unifying entity recognition and disambiguation in a single framework
• Enhancing entity disambiguation through contextual augmentation with LLMs
• Improving cross-domain generalization for entity linking systems

Innovation

• Unified framework integrates entity recognition and disambiguation
• Leverages large language models to enrich entity context
• Achieves state-of-the-art performance on out-of-domain datasets