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 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 includes 14,164 topics and 162,121 semantic relationships. The main root is Computer Science; however, the ontology includes also a few secondary roots, such as Linguistics, Geometry, Semantics, and so on.
CSO presents two main advantages over manually crafted categorisations used in Computer Science (e.g., 2012 ACM Classification, Microsoft Academic Search Classification). First, it can characterise higher-level research areas by means of hundreds of sub-topics and related terms, which enables to map very specific terms to higher-level research areas. Secondly, it can be easily updated by running Klink-2 on a set of new publications.
The CSO Classifier is an application for automatically classifying academic papers according to the rich taxonomy of topics from CSO. The aim is to facilitate the adoption of CSO across the various communities engaged with scholarly data and to foster the development of new applications based on this knowledge base.
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.
The project aims at fostering Springer Nature editorial activities by supporting them with a variety of smart solutions leveraging artificial intelligence, data mining, and semantic technologies. In particular, the KMi team will support Springer Nature editorial team in classifying proceedings and other editorial products, taking informed decisions about their marketing strategy, and improve their internal classification.
“AUGUR: Forecasting the Emergence of New Research Topics” is a paper submitted to the ACM/IEEE Joint Conference on Digital Libraries 2018, presented on June 5 2018, in Fort Worth, TX, USA Angelo Salatino, Francesco Osborne and Enrico Motta Abstract Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, […]
CityLabs is a collaborative project that is grant funded by ERDF. The Open University, ZTE and Fronesys have pooled their skills to create an environment that engages SMEs in research and business development. CityLabs is a place for SMEs to work with academic and industry leaders to develop concepts into prototypes for new products and […]
The Computer Science Ontology Portal (also referred to simply as CSO Portal) is a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. This last feature allows us to periodically review the status ontology and release new version according to the received feedbacks.
Couple of months ago, with my team, we attended the Springer Nature HackDay (here is the post). Just not long ago, Springer Nature released a short video featuring us. Summarised is also my interview, in which I discuss the advantages of making scholarly datasets, as SciGraph, available to anyone. Other media Building on the success […]
On the 29th November 2017, myself with two KMi colleagues (Andrea Mannocci and Thiviyan Thanapalasingam) attended the second edition of SpringerNature HackDay in London (@ SpringerNature Campus). Aliaksandr Birukou, Executive Editor of Computer Science at Springer Nature and collaborator of our research team at the Knowledge Media Institute, also joined our group on the HackDay. The whole […]