# A GenAI System for Improved FAIR Independent Biological Database Integration

arXiv: https://arxiv.org/abs/2506.17934 · HTML: https://mlsanity.com/paper/2506.17934/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 85 references that are in the map.
- First posted on arXiv 22 Jun 2025.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Multi-Stage Document Ranking with BERT](https://mlsanity.com/paper/1910.14424/) (2019)
- [A Deep Relevance Matching Model for Ad-hoc Retrieval](https://mlsanity.com/paper/1711.08611/) (2017)
- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [End-to-End Neural Ad-hoc Ranking with Kernel Pooling](https://mlsanity.com/paper/1706.06613/) (2017)
- [The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes](https://mlsanity.com/paper/2012.14210/) (2020)
- [Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community](https://mlsanity.com/paper/2307.09751/) (2023)
- [PubMed and Beyond: Biomedical Literature Search in the Age of Artificial Intelligence](https://mlsanity.com/paper/2307.09683/) (2023)
- [Optimizing Query Generation for Enhanced Document Retrieval in RAG](https://mlsanity.com/paper/2407.12325/) (2024)
- [Findability: A Novel Measure of Information Accessibility](https://mlsanity.com/paper/2310.09508/) (2023)
- [CUF-Links: Continuous and Ubiquitous FAIRness Linkages for reproducible research](https://mlsanity.com/paper/2201.08296/) (2022)

## Built on by (most cited first, 1 of 1)

- [AgentFAIR: A Multi-Agent Collaborative Framework for FAIRness Evaluation of Geospatial Datasets](https://mlsanity.com/paper/2607.15781/) (2026)

## Abstract (arXiv)

Life sciences research increasingly requires identifying, accessing, and effectively processing data from an ever-evolving array of information sources on the Linked Open Data (LOD) network. This dynamic landscape places a significant burden on researchers, as the quality of query responses depends heavily on the selection and semantic integration of data sources --processes that are often labor-intensive, error-prone, and costly. While the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles has aimed to address these challenges, barriers to efficient and accurate scientific data processing persist. In this paper, we introduce FAIRBridge, an experimental natural language-based query processing system designed to empower scientists to discover, access, and query biological databases, even when they are not FAIR-compliant. FAIRBridge harnesses the capabilities of AI to interpret query intents, map them to relevant databases described in scientific literature, and generate executable queries via intelligent resource access plans. The system also includes robust tools for mitigating low-quality query processing, ensuring high fidelity and responsiveness in the information delivered. FAIRBridge's autonomous query processing framework enables users to explore alternative data sources, make informed choices at every step, and leverage community-driven crowd curation when needed. By providing a user-friendly, automated hypothesis-testing platform in natural English, FAIRBridge significantly enhances the integration and processing of scientific data, offering researchers a powerful new tool for advancing their inquiries.