NLP Knowledge Graph

This repository contains the code, link to the demo video, and link to the NLP-KG web application for the study: "NLP-KG: A System for Exploratory Search of Scientific Literature in Natural Language Processing" accepted to ACL 2024 System Demonstrations.

🖥️ NLP-KG web application: https://nlpkg.sebis.cit.tum.de

📄 Paper: https://aclanthology.org/2024.acl-demos.13

📽️ Demo video: https://youtu.be/HOUjFxDySOg

🤗 NLP Survey Paper Classifier: https://huggingface.co/TimSchopf/nlp_survey_classifier

Citation information

When citing our work in academic papers and theses, please use this BibTeX entry:

@inproceedings{schopf-matthes-2024-nlp,
    title = "{NLP}-{KG}: A System for Exploratory Search of Scientific Literature in Natural Language Processing",
    author = "Schopf, Tim  and
      Matthes, Florian",
    editor = "Cao, Yixin  and
      Feng, Yang  and
      Xiong, Deyi",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-demos.13",
    pages = "127--135",
    abstract = "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.",
}