# NLP-KG: A System for Exploratory Search of Scientific Literature in Natural Language Processing

arXiv: https://arxiv.org/abs/2406.15294 · HTML: https://mlsanity.com/paper/2406.15294/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [SciRepEval: A Multi-Format Benchmark for Scientific Document Representations](https://mlsanity.com/paper/2211.13308/) (2022)
- [The Semantic Scholar Open Data Platform](https://mlsanity.com/paper/2301.10140/) (2023)
- [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings](https://mlsanity.com/paper/2202.06671/) (2022)
- [A Web-scale system for scientific knowledge exploration](https://mlsanity.com/paper/1805.12216/) (2018)
- [NLPExplorer: Exploring the Universe of NLP Papers](https://mlsanity.com/paper/1910.07351/) (2019)
- [NLP Scholar: An Interactive Visual Explorer for Natural Language Processing Literature](https://mlsanity.com/paper/2006.01131/) (2020)
- [Exploring the Landscape of Natural Language Processing Research](https://mlsanity.com/paper/2307.10652/) (2023)
- [CL Scholar: The ACL Anthology Knowledge Graph Miner](https://mlsanity.com/paper/1804.05514/) (2018)

## Built on by (most cited first, 2 of 2)

- [From An LLM Swarm To A PDDL-Empowered HIVE: Planning Self-Executed Instructions In A Multi-Modal Jungle](https://mlsanity.com/paper/2412.12839/) (2024)
- [MedSEBA: Synthesizing Evidence-Based Answers Grounded in Evolving Medical Literature](https://mlsanity.com/paper/2509.00414/) (2025)

## Abstract (arXiv)

Scientific literature searches are often exploratory, whereby users are not yet familiar with a particular field or concept but are interested in learning more about it. However, existing systems for scientific literature search are typically tailored to keyword-based lookup searches, limiting the possibilities for exploration. We propose NLP-KG, a feature-rich system designed to support the exploration of research literature in unfamiliar natural language processing (NLP) fields. In addition to a semantic search, NLP-KG allows users to easily find survey papers that provide a quick introduction to a field of interest. Further, a Fields of Study hierarchy graph enables users to familiarize themselves with a field and its related areas. Finally, a chat interface allows users to ask questions about unfamiliar concepts or specific articles in NLP and obtain answers grounded in knowledge retrieved from scientific publications. Our system provides users with comprehensive exploration possibilities, supporting them in investigating the relationships between different fields, understanding unfamiliar concepts in NLP, and finding relevant research literature. Demo, video, and code are available at: https://github.com/NLP-Knowledge-Graph/NLP-KG-WebApp.