semantic web

Computer Science Ontology v3.4.1

by

The SKM3 team, in collaboration with researchers at Stanford University (CA), has released version 3.4.1 of the Computer Science Ontology. This version significantly expands its coverage of Artificial Intelligence, adding about 100 new topics, including Generative AI, Large Language Models, Retrieval Augmented Generation, Generative Adversarial Networks, Zero-Shot Learning, Fine-tuning, Prompt Engineering, Out-of-Distribution Detection, Deep Reinforcement … Read more

R-Classify: Extracting Research Papers' Relevant Concepts from a Controlled Vocabulary

by

“R-Classify: Extracting Research Papers’ Relevant Concepts from a Controlled Vocabulary” is a software paper accepted at Software Impacts. Tanay Aggarwal, Angelo Antonio Salatino, Francesco Osborne, Enrico Motta Knowledge Media Institute, The Open University, Milton Keynes (UK) Abstract In the past few decades, we saw a proliferation of scientific articles available online. This data-rich environment offers several opportunities … Read more

Leveraging Knowledge Graph Technologies to Assess Journals and Conferences at Springer Nature

by

“Leveraging Knowledge Graph Technologies to Assess Journals and Conferences at Springer Nature” is an In-Use paper presented at the 21st International Semantic Web Conference (ISWC 2022).   Simone Angioni1, Angelo Antonio Salatino2, Francesco Osborne2,3,AliaksandrBirukou4, Diego Reforgiato Recupero1, Enrico Motta2 1 Department of Mathematics and Computer Science, University of Cagliari (Italy) 2 Knowledge Media Institute, The Open … Read more

The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas

by

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 14K topics and 162K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO, we have also released the CSO Classifier, a tool for automatically classifying research papers, and the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO. Users can use the portal to navigate and visualise sections of the ontology, rate topics and relationships, and suggest missing ones. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various research communities engaged with scholarly data.

Invited Talk – Early detection of Research Topics

by

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

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

by

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.

Springer Nature Hack Day – Berlin

by

On 26-27 April 2018, Francesco Osborne and I attended the third edition of the Springer Nature Hack Day, which was held in its headquarter in Berlin.

The Springer Nature Hack Day is an event that allows researchers, developers, tech companies, and Springer Nature itself, to gather together and tackle current research issues. Offering also opportunities for potential collaborations and networking.

This was my second time attending a hack day organised by Springer Nature. Indeed, with my colleagues Andrea Mannocci and Thiviyan Thanapalasingam, we attended the previous edition, back in November 2017, working on a Venue-centric trends project (read full story here). An extended version of this project has then been presented at the SAVE-SD workshop co-located with The Web Conference 2018 [1].

 

In this edition, the participants pitched six different ideas and projects, centred around “analytics and metrics to measure the impact of science”, such as: Disease Dashboard, Hot Topics (our project), Keyword Recommendation, Data mining for historians, Search-Assist, and Semantic Entity Marker. More information about the whole event can be found in this Springer Nature blog post.

Read more

SpringerNature Hackday – London

by

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 event aimed at joining together the skills and interests of many developers and researchers with SciGraph, for advancing discovery.

The main web page for the event is here: https://github.com/SN-HackDay/Advancing-discovery-with-research-data (or here in case someone removes it).

As a team, we worked on Venue-centric trends problem. In particular, our projects provides to editors, conference organizers and many others, a dashboard to understand how knowledge flows across countries and continents, who are the main producers and consumers of the research output for a given conference, whether the conference is open to interdisciplinarity, and many other questions.

Read more

2100 AI: Reflections on the mechanisation of scientific discovery

by

“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.

Read more

Smart Topic Miner

by

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 analyses in real time a set of publications provided by an editor and produces a structured set of research topics and a number of Springer Nature Classification tags, which best characterise the proceedings book. Indeed, if you regularly publish in the main Computer Science conferences, your work was probably already classified and indexed by using STM. **

During the classification of proceedings, editors are involved in different tasks and one of them is determining the list of related terms and categories. This is accomplished according to their own experience, like exploring titles and abstracts visually. However, this appears to be time-consuming as well as complex to perform. In addition, new emerging topics may not find their space while some other current topics could be considered still popular.

Read more