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 […]
Category: World Wide Web
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 […]
Supporting Springer Nature Editors by means of Semantic Technologies
“Supporting Springer Nature Editors by means of Semantic Technologies” is a research paper accepted to the Industry Track at the International Semantic Web Conference (ISWC) 2017 , 21-25 October 2017, Vienna, Austria. Authors Francesco Osborne, Angelo Salatino, Thiviyan Thanapalasingam, Aliaksandr Birukou and Enrico Motta Abstract The Open University and Springer Nature have been collaborating since 2015 […]
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 […]
Department Research Seminar: Early Detection of Research Topics
On the 8th February I delivered a seminar to my department (KMi @ OU) in which I described the work I have been doing in the last two years for my postgraduate research. I started with a little bit of introduction about science. Shortly, I moved to the currently available technologies for keeping track of the […]
Smart Topic Miner
Smart Topic Miner (STM) is a web application which uses Semantic Web technologies to classify scholarly publications on the basis of Computer Science Ontology (CSO), a very large automatically generated ontology of research areas. STM was developed to support the Springer Nature Computer Science editorial team in classifying proceedings in the LNCS family. It […]
Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction based on Innovation-Adoption Priors
“Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction based on Innovation-Adoption Priors” is a peer-reviewed paper presented on Tuesday 22nd November 2016 at the “Entity detection, matching and evolution” session at the 20th International Conference on Knowledge Engineering and Knowledge Management, Bologna, Italy Authors: Amparo Elizabeth Cano-Basave, Francesco Osborne and Angelo Antonio Salatino Abstract: The […]
Smart Topic Miner: Supporting Springer Nature Editors with Semantic Web Technologies
“Smart Topic Miner: Supporting Springer Nature Editors with Semantic Web Technologies” is poster paper presented at the Poster and Demo session [D45] on Wednesday 19th October 2016 at the 15th International Semantic Web Conference in Kobe, Japan Authors: Francesco Osborne, Angelo Antonio Salatino, Aliaksandr Birukou and Enrico Motta Abstract: Academic publishers, such as Springer Nature, annotate scholarly products […]
Automatic Classification of Springer Nature Proceedings with Smart Topic Miner
“Automatic Classification of Springer Nature Proceedings with Smart Topic Miner” is conference paper presented on Friday 21st October 2016 at the 15th International Semantic Web Conference in Kobe, Japan Authors: Francesco Osborne, Angelo Antonio Salatino, Aliaksandr Birukou and Enrico Motta Abstract: The process of classifying scholarly outputs is crucial to ensure timely access to knowledge. However, this […]
Clique Percolation Method in R: a fast implementation
Clique Percolation Method (CPM) is an algorithm for finding overlapping communities within networks, introduced by Palla et al. (2005, see references). This implementation in R, firstly detects communities of size k, then creates a clique graph. Each community will be represented by each connected component in the clique graph. Algorithm The algorithm performs the following […]
