NLP - KG : A System for Exploratory Search of Scientific Literature in Natural Language Processing (2024)

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.

## introduction the body of natural language processing (nlp) literature has experienced remarkable growth in recent years, with articles on various topics and applications being published in an increasing number of journals and conferences @xcite . to browse and search the increasing amount of nlp-related literature, researchers may use systems such as google scholar 1 or semantic scholar @xcite . both systems cover a wide variety of academic disciplines. although this has advantages, the lack of focus on nlp literature also has disadvantages, e.g., the potential to retrieve lots of search results containing many irrelevant papers (mohammad, 2020). for example, when interested in nlp literature on emotion or privacy, searching for it on google scholar is less efficient than searching for it on a platform dedicated to nlp literature. further, scholarly literature searches are often exploratory, whereby users are not yet familiar with a particular field or concept and are interested in learning more about it @xcite . how-ever, commonly used search systems are usually optimized for targeted lookup searches, limiting search and exploration to keyword-based searches and citation-based exploration. in this paper, we present a system to support the exploration of nlp research literature from unfamiliar fields using a knowledge graph (kg) and state-of-the-art retrieval approaches. our main contributions comprise the following features: ## nlp-kg a well-organized hierarchical structure of fos and an accurate mapping between these fos and scholarly publications can enable a streamlined and satisfactory exploration experience @xcite . further, semantic relations between scholarly entities can be easily modeled in a graph representation. therefore, we construct the natural language processing knowledge graph (nlp-kg) as the core of our system that links fos, publications, authors, and venues via semantic relations. in addition, we integrate a llm in our retrieval pipeline that can enhance the exploration experience by providing accurate responses to user queries @xcite . figure 1 illustrates how the knowledge graph and the llm are integrated into our system. ## conclusion this paper introduces nlp-kg, a system for search and exploration of nlp research literature. nlp-kg supports the exploration of unfamiliar fields by providing a high-quality knowledge graph of fos in nlp and advanced retrieval features such as semantic search and filtering for survey papers. in addition, a llm integration allows users to ask questions about the content of specific papers and unfamiliar concepts in nlp and provides answers based on knowledge found in scientific publications. our model evaluations demonstrate strong classification and retrieval performances, making our system well-suited for literature exploration.