# A Practical Entity Linking System for Tables in Scientific Literature

arXiv: https://arxiv.org/abs/2306.10044 · HTML: https://mlsanity.com/paper/2306.10044/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned](https://mlsanity.com/paper/2004.05125/) (2020)
- [Wikidata-lite for Knowledge Extraction and Exploration](https://mlsanity.com/paper/2211.05416/) (2022)
- [Wembedder: Wikidata entity embedding web service](https://mlsanity.com/paper/1710.04099/) (2017)

## Abstract (arXiv)

Entity linking is an important step towards constructing knowledge graphs that facilitate advanced question answering over scientific documents, including the retrieval of relevant information included in tables within these documents. This paper introduces a general-purpose system for linking entities to items in the Wikidata knowledge base. It describes how we adapt this system for linking domain-specific entities, especially for those entities embedded within tables drawn from COVID-19-related scientific literature. We describe the setup of an efficient offline instance of the system that enables our entity-linking approach to be more feasible in practice. As part of a broader approach to infer the semantic meaning of scientific tables, we leverage the structural and semantic characteristics of the tables to improve overall entity linking performance.