MetaInfoSci: An Integrated Web Tool for Scholarly Data Analysis

2025-06-04

AI Summary

The exponential growth of academic literature has impeded systematic knowledge integration, while existing tools lack integrated support for bibliometrics, scientometrics, and network analysis; cross-database interoperability; and AI-powered summarization. To address these gaps, we present the first web-based platform unifying all three analytical paradigms. It automatically ingests and harmonizes metadata from heterogeneous sources—including Web of Science, Scopus, and OpenAlex—enables graph neural network (GNN)-enhanced citation network modeling, and leverages large language models (LLMs) to generate interpretable analytical reports. The platform supports customizable queries and offers over ten interactive, publication-ready visualizations. By embedding a unified end-to-end analytical framework, it reduces time required for systematic literature reviews by over 50% in empirical evaluations. The platform is publicly accessible and actively deployed.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Web data visualization

Abstract

The exponential increase in academic publications has made it increasingly difficult for researchers to remain up to date and systematically synthesize knowledge scattered across vast and fragmented research domains. Literature reviews, particularly those supported by bibliometric methods, have become essential in organizing prior findings and guiding future research directions. While numerous tools exist for bibliometric analysis and network science, there is currently no single platform that integrates the full range of features from both domains. Researchers are often required to navigate multiple software environments, many of which lack customizable visualizations, cross-database integration, and AI-assisted result summarization. Addressing these limitations, this study introduces MetaInfoSci at www.metainfosci.com, a comprehensive, web-based platform designed to unify bibliometric, scientometric, and network analytical capabilities. The platform supports tailored query design, merges data from diverse sources, enables rich and adaptable visual outputs, and provides automated, AI-driven summaries of analytical results. This integrated approach aims to enhance the accessibility, efficiency, and depth of scientific literature analysis for scholars across disciplines.

Problem

• Integrates bibliometric and network analysis tools into one platform
• Addresses lack of customizable visualizations and cross-database integration
• Provides AI-assisted summarization for efficient literature analysis

Innovation

• Integrated web platform for bibliometric analysis
• AI-driven automated summarization of results
• Customizable visualizations from multiple data sources