This post aims to act as a hub for all the relevant information about my doctoral work. It will be constantly updated with new sources and developments. Abstract 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 […]
“The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas” is a journal paper submitted to the Special Issue on Best Resources papers of the Data Intelligence Jornal (MIT Press). Authors Angelo A. Salatino1, Thiviyan Thanapalasingam1, Andrea Mannocci1, Aliaksandr Birukou2, Francesco Osborne1, Enrico Motta1 1 Knowledge Media Institute, The Open University, MK7 6AA, Milton Keynes, UK 2 […]
“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, […]
Identifying the research topics that best describe the scope of a scientific publication is a crucial task for editors, in particular because the quality of these annotations determine how effectively users are able to discover the right content in online libraries. For this reason, Springer Nature, the world’s largest academic book publisher, has traditionally entrusted this task to their most expert editors. These editors manually analyse all new books, possibly including hundreds of chapters, and produce a list of the most relevant topics. Hence, this process has traditionally been very expensive, time-consuming, and confined to a few senior editors. For these reasons, back in 2016 we developed Smart Topic Miner (STM), an ontology-driven application that assists the Springer Nature editorial team in annotating the volumes of all books covering conference proceedings in Computer Science. Since then STM has been regularly used by editors in Germany, China, Brazil, India, and Japan, for a total of about 800 volumes per year. Over the past three years the initial prototype has iteratively evolved in response to feedback from the users and evolving requirements.
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.
A fellow researcher (Tina Papathoma) asked me to record a video in which I discuss my experience with the writing and defending my PhD thesis. She presented it to the “How to finish your PhD thesis and successfully defend it” workshop at the 15th EATEL Summer School on Technology Enhanced Learning (JTELSS 2019) which took place on […]
On 31st May 2019, I defended my thesis on the “Early Detection of Research Trends”. The exam panel consisted of Dr Pallavi Anand from the Open University as chair, and two external examiners Prof Kalina Bontcheva from the University of Sheffield, and Prof Alun Preece from Cardiff University. Further Links Early Detection of Research Trends Media […]
We are pleased to announce that we recently created a new release of the CSO Classifier (v2.1), an application for automatically classifying research papers according to the Computer Science Ontology (CSO). Recently, we have been intensively working on improving its scalability, removing all its bottlenecks and making sure it could be run on large corpus. […]
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 page, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according […]
I have always been passionate about technology. When I bought my first computer (special thanks to my father for funding it), and got it connected to the internet, it soon became part of my life: downloading movies, music, studying, chatting, engaging with different communities, writing a blog, buying and selling stuff. The web gave me […]