arxiv_ai 95% Match Research Paper NLP Researchers,Data Scientists,Information Retrieval Specialists 2 weeks ago

Contextual Augmentation for Entity Linking using Large Language Models

large-language-models › model-architecture

📄 Abstract

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.

Authors (4)

Daniel Vollmers

Hamada M. Zahera

Diego Moussallem

Axel-Cyrille Ngonga Ngomo

Submitted

October 17, 2025

arXiv Category

cs.CL

arXiv PDF

Key Contributions

This paper proposes a unified framework for entity linking that jointly integrates entity recognition and disambiguation, moving beyond traditional two-step methods. It leverages large language models to enrich context, significantly improving performance, especially on out-of-domain datasets, by addressing the limitations of separate, computationally intensive models.

Business Value

Improved accuracy in extracting structured information from unstructured text can enhance knowledge management systems, power more intelligent search engines, and enable better data integration for businesses.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

Moderate. Requires access to LLMs and potentially significant computational resources for fine-tuning and inference, but the unified framework simplifies deployment compared to multi-stage systems.

Limitations Addressed

Computational intensity and reduced effectiveness of traditional two-step entity linking methods; poor performance on out-of-domain datasets.

Technical Tags

Entity LinkingLarge Language ModelsFine-tuningKnowledge GraphsNatural Language ProcessingDisambiguationEntity RecognitionContextual AugmentationUnified FrameworkOut-of-Domain

Research Topics

Information ExtractionKnowledge RepresentationNatural Language UnderstandingMachine LearningArtificial Intelligence

Methods & Architectures

Fine-tuning LLMsJoint Entity Recognition and DisambiguationContextual Enrichment Large Language Models (LLMs)

Applications & Tasks

Information Retrieval Knowledge Base Construction Text Analysis Entity RecognitionEntity DisambiguationInformation Extraction Entity LinkingKnowledge Graph Population

Datasets & Benchmarks

Datasets

benchmark datasets

Related Fields

Computational LinguisticsInformation RetrievalKnowledge RepresentationMachine Learning

Keywords

Entity LinkingLarge Language ModelsKnowledge GraphsNLPDisambiguationEntity RecognitionContextual AugmentationUnified FrameworkOut-of-Domain PerformanceInformation ExtractionText MiningMachine Learning

Academic Context

#Information Extraction#Knowledge Representation#Natural Language Understanding#Machine Learning#Artificial Intelligence

Commercial Potential

Potential Products

Automated knowledge graph population toolsEnhanced search and recommendation systemsIntelligent document analysis platforms

Target Industries

Information ServicesPublishingMediaResearch

Use Case Examples

Linking mentions of people, organizations, and locations in news articles to a knowledge base.Populating a company's internal knowledge graph from customer support logs.

Competitive Edge

Offers a more integrated and potentially more accurate approach than traditional two-step entity linking systems, particularly for challenging out-of-domain scenarios.

Market Opportunity

Large market for NLP-based information extraction and knowledge management solutions.

Revenue Models

SaaSAPI accessconsulting services.

Resource Requirements

Compute Needs

Significant, especially for fine-tuning LLMs.

Data Requirements

Labeled data for entity recognition and disambiguation, potentially large corpora for LLM pre-training/fine-tuning.

Deployment Constraints

Dependency on LLM availability and computational resources.

Scalability

Scalability depends on the underlying LLM and inference infrastructure. The unified approach might be more scalable than separate models.

Production Readiness

Maturity Level

Research

Time to Market

1-3 years for specialized applications.

Patent Potential

Low to Moderate, depending on specific algorithmic innovations.

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