“Smart Topics Miner 2: Improving Proceedings Retrievability at Springer Nature” is a demo paper submitted to the poster and demo session of the International Semantic Web Conference, October 26 – 30, 2019 The University of Auckland, New Zealand. Authors Angelo A. Salatino1, Francesco Osborne1, Aliaksandr Birukou2, Enrico Motta1 1 Knowledge Media Institute, The Open University, MK7 6AA, Milton Keynes, […]
“Integrating Knowledge Graphs for Comparing the Scientific Output of Academia and Industry” is a poster paper submitted to the poster and demo session of the International Semantic Web Conference, October 26 – 30, 2019 The University of Auckland, New Zealand. Authors Simone Angioni1, Francesco Osborne2, Angelo A. Salatino2, Diego Reforgiato Recupero1, Enrico Motta2 1 University of Cagliari, […]
Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this paper, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of research areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods.
Awesome Scholarly Data Analysis is a curated collection of resources that can support Scholarly Data analytics. This list ranges from: Datasets, which includes different corpora of papers, citations, authors and others, as well as taxonomies and ontologies of research concepts; Tools for collecting and classifying research papers, information extraction, and visualization; and Venues, Summer Schools, […]
The new ontology portal is the largest taxonomy of research topics in computer science available to date Springer Nature and the Knowledge Media Institute (KMi) of The Open University are partnering to provide a comprehensive Computer Science Ontology (CSO) to a broad range of communities engaged with scholarly data. CSO can be accessed free of […]
The Computer Science Ontology is a large-scale ontology of research areas that was automatically generated using the Klink-2 algorithm  on a dataset of about 16 million publications, mainly in the field of Computer Science. In the rest of the paper, we will refer to this corpus as the Rexplore dataset . The current version of CSO […]
Simple answer: no. However, before getting into a more detailed answer, allow me to briefly introduce the concept of citation networks, then I will describe why citation networks cannot be considered acyclic anymore. In the scholarly domain, citation networks is an information network in which each node represents a scientific paper and a link between […]