# Cross-Document Contextual Coreference Resolution in Knowledge Graphs

arXiv: https://arxiv.org/abs/2504.05767 · HTML: https://mlsanity.com/paper/2504.05767/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 19 of 19)

- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph](https://mlsanity.com/paper/1801.10314/) (2018)
- [Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering](https://mlsanity.com/paper/2305.15387/) (2023)
- [Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends](https://mlsanity.com/paper/2407.21489/) (2024)
- [Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding](https://mlsanity.com/paper/2410.01671/) (2024)
- [Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities](https://mlsanity.com/paper/2205.11438/) (2022)
- [SP-10K: A Large-scale Evaluation Set for Selectional Preference Acquisition](https://mlsanity.com/paper/1906.02123/) (2019)
- [Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning](https://mlsanity.com/paper/2305.12738/) (2023)
- [Identifying Conspiracy Theories News based on Event Relation Graph](https://mlsanity.com/paper/2310.18545/) (2023)
- [Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing](https://mlsanity.com/paper/2407.11988/) (2024)
- [A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution](https://mlsanity.com/paper/2404.01921/) (2024)
- [Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking](https://mlsanity.com/paper/2210.10695/) (2022)
- [Exploring Multiple Strategies to Improve Multilingual Coreference Resolution in CorefUD](https://mlsanity.com/paper/2408.16893/) (2024)
- [DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models](https://mlsanity.com/paper/2406.11087/) (2024)
- [From Dialogue to Diagram: Task and Relationship Extraction from Natural Language for Accelerated Business Process Prototyping](https://mlsanity.com/paper/2312.10432/) (2023)
- [Semantic-Geometric-Physical-Driven Robot Manipulation Skill Transfer via Skill Library and Tactile Representation](https://mlsanity.com/paper/2411.11714/) (2024)
- [NumCoKE: Ordinal-Aware Numerical Reasoning over Knowledge Graphs with Mixture-of-Experts and Contrastive Learning](https://mlsanity.com/paper/2411.12950/) (2024)
- [Automated Extraction and Creation of FBS Design Reasoning Knowledge Graphs from Structured Data in Product Catalogues Lacking Contextual Information](https://mlsanity.com/paper/2412.05868/) (2024)

## Built on by (most cited first, 1 of 1)

- [Do Lexical and Contextual Coreference Resolution Systems Degrade Differently under Mention Noise? An Empirical Study on Scientific Software Mentions](https://mlsanity.com/paper/2604.02171/) (2026)

## Abstract (arXiv)

Coreference resolution across multiple documents poses a significant challenge in natural language processing, particularly within the domain of knowledge graphs. This study introduces an innovative method aimed at identifying and resolving references to the same entities that appear across differing texts, thus enhancing the coherence and collaboration of information. Our method employs a dynamic linking mechanism that associates entities in the knowledge graph with their corresponding textual mentions. By utilizing contextual embeddings along with graph-based inference strategies, we effectively capture the relationships and interactions among entities, thereby improving the accuracy of coreference resolution. Rigorous evaluations on various benchmark datasets highlight notable advancements in our approach over traditional methodologies. The results showcase how the contextual information derived from knowledge graphs enhances the understanding of complex relationships across documents, leading to better entity linking and information extraction capabilities in applications driven by knowledge. Our technique demonstrates substantial improvements in both precision and recall, underscoring its effectiveness in the area of cross-document coreference resolution.