Machine Learning

Supporting Springer Nature Editors by means of Semantic Technologies

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“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 in the development of an array of semantically-enhanced solutions supporting editors in i) classifying proceedings and other editorial products with respect to the relevant research areas and ii) taking informed decisions about their marketing strategy. These solutions include i) the Smart Topic API, which automatically maps keywords associated with published papers to semantically characterized topics, which are drawn from a very large and automatically-generated ontology of Computer Science topics; ii) the Smart Topic Miner, which helps editors to associate scholarly metadata to books; and iii) the Smart Book Recommender, which assists editors in deciding which editorial products should be marketed in a specific venue.

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Smart Book Recommender: A Semantic Recommendation Engine for Editorial Products

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“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 … Read more

BigDat2017: certificate of attendance

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I have recently been attending in Bari (IT) a winter school about Big Data: BigDat2017. At the moment, Big Data is gaining great attention in research, since it allows to provide data-driven solutions in several contexts. As part of my postgraduate research I decided to attend it and follow the new developments in this field. … Read more

BigDat2017: a review

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This week I have been attending the 3rd edition of the Big Data winter school: BigDat2017. It was held in my former campus, at the University of Bari (IT). It was a really nice feeling to be back for a while, sitting on those benches and following courses, once again.
Big Data has recently gained a lot of interest in research and many believe that it will still play its leading role for many years. Nowadays, we live in a world in which all information seems to be available, we are surrounded by data-driven applications (Google, Facebook, Twitter, Spotify, just to name a few), which gather data and try to provide tailor-made solutions for their users. To this end, having such event like BigDat2017 with its clear mission —introduce and update new researchers into this fast advancing research area—is really important.

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A Visual Introduction to Machine Learning: Italian Translation

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The R2D3 team (http://www.r2d3.us/) developed a visual introduction to Machine Learning. This introduction uses data visualization technologies to show a workflow that can help for the creation of a machine learning model able to make accurate predictions. Lately, many people volunteered to translate this introduction in different languages. I took care of the Italian version: Una introduzione visuale … Read more

Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction based on Innovation-Adoption Priors

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Semantic Innovation Forecasting Model
Semantic Innovation Forecasting Model

“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 ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.

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Early Detection and Forecasting of Research Trends

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“Early Detection and Forecasting of Research Trends” is a paper presented at the Doctoral Consortium of the 14th International Semantic Web Conference (ISWC2015) in Bethlehem (PA, USA). Abstract Identifying and forecasting research trends is of critical importance for a variety of stakeholders, including researchers, academic publishers, institutional funding bodies, companies operating in the innovation space and others.Currently, … Read more

Advanced classification of Alzheimer's disease and healthy subjects based on EEG markers

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Authors:

Vitoantonio Bevilacqua, Angelo Antonio Salatino, Carlo Di Leo, Giacomo Tattoli, Domenico Buongiorno, Domenico Signorile, Claudio Babiloni, Claudio Del Percio, Antonio Ivano Triggiani, Loreto Gesualdo

Abstract:

In this study, we compared several classifiers for the supervised distinction between normal elderly and Alzheimer’s disease individuals, based on resting state electroencephalographic markers, age, gender and education.

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SSSW 2015 – 11th Summer School on Ontology Engineering and the Semantic Web

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From the 5th to 11th of July the 11th Summer School on Ontology Engineering and the Semantic Web took place. Unlike other years, it was held in Bertinoro (IT) instead of Cercedilla (Spain). About the Summer School [from the sssw.org]: The Semantic Web Summer School, SSSW, was founded in 2003 by Enrico Motta and Asun Gomez-Perez as … Read more