Papers › ELDEN: Improved Entity Linking Using Densified Knowledge Graphs
ELDEN: Improved Entity Linking Using Densified Knowledge Graphs
Priya Radhakrishnan, Partha Talukdar, Vasudeva Varma
Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG). Degree of connectivity of an entity in the KG directly affects an EL system{'}s ability to correctly link mentions in text to the entity in KG. This causes many EL systems to perform well for entities well connected to other entities in KG, bringing into focus the role of KG density in EL. In this paper, we propose Entity Linking using Densified Knowledge Graphs (ELDEN). ELDEN is an EL system which first densifies the KG with co-occurrence statistics from a large text corpus, and then uses the densified KG to train entity embeddings. Entity similarity measured using these trained entity embeddings result in improved EL. ELDEN outperforms state-of-the-art EL system on benchmark datasets. Due to such densification, ELDEN performs well for sparsely connected entities in the KG too. ELDEN{'}s approach is simple, yet effective. We have made ELDEN{'}s code and data publicly available.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Entity Disambiguation | AIDA-CoNLL | ELDEN | In-KB Accuracy | 93.0 | #12 of 20 | Archive leaderboard | report |
| Entity Linking | CoNLL-Aida | Radhakrishnan et al. 2018 | Accuracy | 93.0 | #3 of 3 | Archive leaderboard | report |
| Entity Linking | TAC-KBP 2010 | Radhakrishnan et al. 2018 | Accuracy | 89.6 | #3 of 3 | Archive leaderboard | report |
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