Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 26K topics and 226K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: (i) it includes a very large number of topics that do not appear in other classifications, and (ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.

Knowledge organization systems (KOSs), such as term lists, thesauri, taxonomies, and ontologies, play a fundamental role in categorizing, managing, and retrieving information. In the academic domain, KOSs are often adopted for representing research areas and their relationships, primarily aiming to classify research articles, academic courses, patents, books, scientific venues, domain experts, grants, software, experiment materials, and several other relevant products and agents. These structured representations of research areas, widely embraced by many academic fields, have proven effective in empowering AI-based systems to enhance the retrievability of relevant documents, enable advanced analytic solutions to quantify the impact of academic research, and analyze and forecast research dynamics. We aim to present a comprehensive survey of the current KOS for academic disciplines. We analyzed and compared 45 KOSs according to five main dimensions: scope, structure, curation, usage, and links to other KOSs. Our results reveal a very heterogeneous scenario in terms of scope, scale, quality, and usage, highlighting the need for more integrated solutions for representing research knowledge across academic fields. We conclude by discussing the main challenges and the most promising future directions.

Purpose: – The purpose of this paper is to present the architectural principles and the services of the D-NET software toolkit. D-NET is a framework where designers and developers find the tools for constructing and operating aggregative infrastructures (systems for aggregating data sources with heterogeneous data models and technologies) in a cost-effective way. Designers and developers can select from a variety of D-NET data management services, can configure them to handle data according to given data models, and can construct autonomic workflows to obtain personalized aggregative infrastructures. Design/methodology/approach: – The paper provides a definition of aggregative infrastructures, sketching architecture, and components, as inspired by real-case examples. It then describes the limits of current solutions, which find their lacks in the realization and maintenance costs of such complex software. Finally, it proposes D-NET as an optimal solution for designers and developers willing to realize aggregative infrastructures. The D-NET architecture and services are presented, drawing a parallel with the ones of aggregative infrastructures. Finally, real-cases of D-NET are presented, to show-case the statement above. Findings: – The D-NET software toolkit is a general-purpose service-oriented framework where designers can construct customized, robust, scalable, autonomic aggregative infrastructures in a cost-effective way. D-NET is today adopted by several EC projects, national consortia and communities to create customized infrastructures under diverse application domains, and other organizations are enquiring for or are experimenting its adoption. Its customizability and extendibility make D-NET a suitable candidate for creating aggregative infrastructures mediating between different scientific domains and therefore supporting multi-disciplinary research. Originality/value: – D-NET is the first general-purpose framework of this kind. Other solutions are available in the literature but focus on specific use-cases and therefore suffer from the limited re-use in different contexts. Due to its maturity, D-NET can also be used by third-party organizations, not necessarily involved in the software design and maintenance.