Artificial Intelligence

The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles

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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.

New release: CSO Classifier v2.1

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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. … Read more

CSO Classifier

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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 to the Computer Science Ontology (CSO), a comprehensive ontology of research areas in the field of Computer Science.

Invited Talk – Early detection of Research Topics

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On 2nd of August 2018, I have been invited by Boris Veytsman, Principal Research Scientist at Chan Zuckerberg Initiative (formerly Meta), to give a talk about my PhD work. Differently from my previous talk to the ORNL group, I had the opportunity to describe my doctoral work more comprehensively. More specifically, I initially showed what is available … Read more

Invited Talk – AUGUR: Forecasting the Emergence of New Research Topics

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On 30th Jul 2018, I have been invited from Dasha Herrmannova, former PhD student at the KMi, to give a talk at the “Machine Learning and Graph Mining for Big Scholarly Data” workshop organised for the Computational Data Analytics Group at Oak Ridge National Laboratory (ORNL). In this talk, named “AUGUR: Forecasting the Emergence of New … Read more

Classifying Research Papers with the Computer Science Ontology

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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.

The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas

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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.

AUGUR: Forecasting the Emergence of New Research Topics

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“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, … Read more

Smart Book Recommender

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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 … Read more

2100 AI: Reflections on the mechanisation of scientific discovery

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“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 produced and made available on the Web at a rate so unprecedented that digesting the information conveyed by such a “data deluge” stretches far beyond human analytical capabilities. Data science, artificial intelligence, machine learning and big data analytics are providing researchers with new methodologies capable of coping and getting insight in an automated fashion from the overload of information conveyed. Nonetheless major advances in AI solutions for knowledge discovery risk to exacerbate some negative phenomena, which are already observable on a global scale and disrupt irremediably the way of doing science as we know it.

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