# MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach

arXiv: https://arxiv.org/abs/1707.05288 · HTML: https://mlsanity.com/paper/1707.05288/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [Knowledge Graph Question Answering using Graph-Pattern Isomorphism](https://mlsanity.com/paper/2103.06752/) (2021)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)
- [Contextual Augmentation for Entity Linking using Large Language Models](https://mlsanity.com/paper/2510.18888/) (2025)
- [MedPath: Multi-Domain Cross-Vocabulary Hierarchical Paths for Biomedical Entity Linking](https://mlsanity.com/paper/2511.10887/) (2025)
- [An Unsupervised Language-Independent Entity Disambiguation Method and its Evaluation on the English and Persian Languages](https://mlsanity.com/paper/2102.00395/) (2021)
- [Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge](https://mlsanity.com/paper/2601.10485/) (2026)

## Abstract (arXiv)

Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-based agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG, on the other hand, achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.