# Epistemic AI platform accelerates innovation by connecting biomedical knowledge

arXiv: https://arxiv.org/abs/2201.11331 · HTML: https://mlsanity.com/paper/2201.11331/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 75 references that are in the map.
- First posted on arXiv 27 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [End-to-End Differentiable Proving](https://mlsanity.com/paper/1705.11040/) (2017)
- [A Survey of Heterogeneous Information Network Analysis](https://mlsanity.com/paper/1511.04854/) (2015)
- [CliNER 2.0: Accessible and Accurate Clinical Concept Extraction](https://mlsanity.com/paper/1803.02245/) (2018)
- [SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers](https://mlsanity.com/paper/1804.08887/) (2018)

## Abstract (arXiv)

Epistemic AI accelerates biomedical discovery by finding hidden connections in the network of biomedical knowledge. The Epistemic AI web-based software platform embodies the concept of knowledge mapping, an interactive process that relies on a knowledge graph in combination with natural language processing (NLP), information retrieval, relevance feedback, and network analysis. Knowledge mapping reduces information overload, prevents costly mistakes, and minimizes missed opportunities in the research process. The platform combines state-of-the-art methods for information extraction with machine learning, artificial intelligence and network analysis. Starting from a single biological entity, such as a gene or disease, users may: a) construct a map of connections to that entity, b) map an entire domain of interest, and c) gain insight into large biological networks of knowledge. Knowledge maps provide clarity and organization, simplifying the day-to-day research processes.