# KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph

arXiv: https://arxiv.org/abs/2103.08893 · HTML: https://mlsanity.com/paper/2103.08893/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 16 Mar 2021.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [Enhanced LSTM for Natural Language Inference](https://mlsanity.com/paper/1609.06038/) (2016)
- [Training Very Deep Networks](https://mlsanity.com/paper/1507.06228/) (2015)
- [Biomedical Entity Representations with Synonym Marginalization](https://mlsanity.com/paper/2005.00239/) (2020)
- [Improving Natural Language Inference Using External Knowledge in the Science Questions Domain](https://mlsanity.com/paper/1809.05724/) (2018)
- [Natural Language Inference by Tree-Based Convolution and Heuristic Matching](https://mlsanity.com/paper/1512.08422/) (2015)
- [Differentiating Concepts and Instances for Knowledge Graph Embedding](https://mlsanity.com/paper/1811.04588/) (2018)
- [Medical Entity Linking using Triplet Network](https://mlsanity.com/paper/2012.11164/) (2020)
- [Medical Synonym Extraction with Concept Space Models](https://mlsanity.com/paper/1506.00528/) (2015)
- [Mining Entity Synonyms with Efficient Neural Set Generation](https://mlsanity.com/paper/1811.07032/) (2018)
- [SurfCon: Synonym Discovery on Privacy-Aware Clinical Data](https://mlsanity.com/paper/1906.09285/) (2019)

## Built on by (most cited first, 4 of 4)

- [Differentially Private Federated Knowledge Graphs Embedding](https://mlsanity.com/paper/2105.07615/) (2021)
- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [Large Language Models for Medicine: A Survey](https://mlsanity.com/paper/2405.13055/) (2024)
- [Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey](https://mlsanity.com/paper/2310.17894/) (2023)

## Abstract (arXiv)

Entity synonyms discovery is crucial for entity-leveraging applications. However, existing studies suffer from several critical issues: (1) the input mentions may be out-of-vocabulary (OOV) and may come from a different semantic space of the entities; (2) the connection between mentions and entities may be hidden and cannot be established by surface matching; and (3) some entities rarely appear due to the long-tail effect. To tackle these challenges, we facilitate knowledge graphs and propose a novel entity synonyms discovery framework, named KGSynNet. Specifically, we pre-train subword embeddings for mentions and entities using a large-scale domain-specific corpus while learning the knowledge embeddings of entities via a joint TransC-TransE model. More importantly, to obtain a comprehensive representation of entities, we employ a specifically designed fusion gate to adaptively absorb the entities' knowledge information into their semantic features. We conduct extensive experiments to demonstrate the effectiveness of our KGSynNet in leveraging the knowledge graph. The experimental results show that the KGSynNet improves the state-of-the-art methods by 14.7% in terms of hits@3 in the offline evaluation and outperforms the BERT model by 8.3% in the positive feedback rate of an online A/B test on the entity linking module of a question answering system.