Danilo Dessí, Francesco Osborne, Davide Buscaldi, Diego Reforgiato Recupero, Enrico Motta
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CS-KG 2.0: A Large-scale Knowledge Graph of Computer Science.
The rapid evolution of AI and the increased accessibility of scientific articles through open access marks a pivotal moment in research. AI-driven tools are reshaping how scientists explore, interpret, and contribute to the body of scientific knowledge, offering unprecedented opportunities. Nonetheless, a significant challenge remains: dealing with the overwhelming number of papers published every year. A promising solution is the use of knowledge graphs, which provide structured, interconnected, and formalized frameworks that improve the capabilities of AI systems to integrate information from the literature. This paper presents the last version of the Computer Science Knowledge Graph (CS-KG 2.0), an extensive knowledge base generated from 15 million research papers. CS-KG 2.0 describes 25 million entities linked by 67 million relationships, offering a nuanced representation of the scientific knowledge within the field of computer science. This innovative resource facilitates new research opportunities in key areas such as analysis and forecasting of research trends, hypothesis generation, smart literature search, automatic production of literature review, and scientific question-answering.
期刊介绍：
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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