# A New Neural Search and Insights Platform for Navigating and Organizing AI Research

arXiv: https://arxiv.org/abs/2011.00061 · HTML: https://mlsanity.com/paper/2011.00061/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

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

- [CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example](https://mlsanity.com/paper/2103.12906/) (2021)
- [SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation](https://mlsanity.com/paper/2306.03535/) (2023)
- [Detect, Retrieve, Comprehend: A Flexible Framework for Zero-Shot Document-Level Question Answering](https://mlsanity.com/paper/2210.01959/) (2022)
- [MODOC: A Modular Interface for Flexible Interlinking of Text Retrieval and Text Generation Functions](https://mlsanity.com/paper/2408.14623/) (2024)

## Abstract (arXiv)

To provide AI researchers with modern tools for dealing with the explosive growth of the research literature in their field, we introduce a new platform, AI Research Navigator, that combines classical keyword search with neural retrieval to discover and organize relevant literature. The system provides search at multiple levels of textual granularity, from sentences to aggregations across documents, both in natural language and through navigation in a domain-specific Knowledge Graph. We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.