# Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models

arXiv: https://arxiv.org/abs/2008.05190 · HTML: https://mlsanity.com/paper/2008.05190/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 23 of 23)

- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [A Survey of Knowledge-Intensive NLP with Pre-Trained Language Models](https://mlsanity.com/paper/2202.08772/) (2022)
- [Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications](https://mlsanity.com/paper/2206.07922/) (2022)
- [Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks](https://mlsanity.com/paper/2104.01569/) (2021)
- [Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale](https://mlsanity.com/paper/2204.07309/) (2022)
- [Visual Named Entity Linking: A New Dataset and A Baseline](https://mlsanity.com/paper/2211.04872/) (2022)
- [KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction](https://mlsanity.com/paper/2106.00459/) (2021)
- [Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking](https://mlsanity.com/paper/2202.13404/) (2022)
- [Improving Entity Disambiguation by Reasoning over a Knowledge Base](https://mlsanity.com/paper/2207.04106/) (2022)
- [Joint Coreference Resolution and Character Linking for Multiparty Conversation](https://mlsanity.com/paper/2101.11204/) (2021)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)
- [Triple Classification for Scholarly Knowledge Graph Completion](https://mlsanity.com/paper/2111.11845/) (2021)
- [Contextual Augmentation for Entity Linking using Large Language Models](https://mlsanity.com/paper/2510.18888/) (2025)
- [Knowledge Graphs for Enhancing Large Language Models in Entity Disambiguation](https://mlsanity.com/paper/2505.02737/) (2025)
- [VOGUE: Answer Verbalization through Multi-Task Learning](https://mlsanity.com/paper/2106.13316/) (2021)
- [Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings](https://mlsanity.com/paper/2305.12027/) (2023)
- [Question Answering with Deep Neural Networks for Semi-Structured Heterogeneous Genealogical Knowledge Graphs](https://mlsanity.com/paper/2307.16214/) (2023)
- [Self-Distillation with Meta Learning for Knowledge Graph Completion](https://mlsanity.com/paper/2305.12209/) (2023)
- [Named Entity Normalization Model Using Edge Weight Updating Neural Network: Assimilation Between Knowledge-Driven Graph and Data-Driven Graph](https://mlsanity.com/paper/2106.07549/) (2021)
- [Matching of Descriptive Labels to Glossary Descriptions](https://mlsanity.com/paper/2310.18385/) (2023)
- [Mathematical Entities: Corpora and Benchmarks](https://mlsanity.com/paper/2406.11577/) (2024)
- [SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables](https://mlsanity.com/paper/2601.07638/) (2026)
- [KARAMEL: Knowledge-Aware Ranking for Multimodal Entity Linking in the Arts via Heterogeneous Graphs](https://mlsanity.com/paper/2610.16502/) (2026)

## Abstract (arXiv)

Pretrained Transformer models have emerged as state-of-the-art approaches that learn contextual information from text to improve the performance of several NLP tasks. These models, albeit powerful, still require specialized knowledge in specific scenarios. In this paper, we argue that context derived from a knowledge graph (in our case: Wikidata) provides enough signals to inform pretrained transformer models and improve their performance for named entity disambiguation (NED) on Wikidata KG. We further hypothesize that our proposed KG context can be standardized for Wikipedia, and we evaluate the impact of KG context on state-of-the-art NED model for the Wikipedia knowledge base. Our empirical results validate that the proposed KG context can be generalized (for Wikipedia), and providing KG context in transformer architectures considerably outperforms the existing baselines, including the vanilla transformer models.