The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas

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.

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Smart Book Recommender

The Smart Book Recommender (SBR) is a semantic application designed to support the Springer Nature editorial team in promoting their publications at Computer Science venues. It takes as input the proceedings of a conference and suggests books, journals, and other conference proceedings that are likely to be relevant to the attendees of the conference in question. It […]

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2100 AI: Reflections on the mechanisation of scientific discovery

“2100 AI: Reflections on the mechanisation of scientific discovery” is a paper submitted to the RE-CODING BLACK MIRROR Workshop co-located with the International Semantic Web Conference (ISWC) 2017, 21-25 October 2017, Vienna, Austria. Authors Andrea Mannocci, Angelo Salatino, Francesco Osborne and Enrico Motta Abstract The pace of nowadays research is hectic. Datasets and papers are […]

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Smart Book Recommender: A Semantic Recommendation Engine for Editorial Products

“Smart Book Recommender: A Semantic Recommendation Engine for Editorial Products” is a poster paper that will be presented at the International Semantic Web Conference (ISWC) 2017, 21-25 October 2017, Vienna, Austria. Authors Francesco Osborne, Thiviyan Thanapalasingam, Angelo Salatino, Aliaksandr Birukou and Enrico Motta Abstract Academic publishers, such as Springer Nature, need to constantly make informed decisions […]

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Early Detection of Research Trends

Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge.

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