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 to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge.
“AIDA-Bot: A Conversational Agent to ExploreScholarly Knowledge Graphs” is a demo paper accepted for presentation at the International Semantic Web Conference (ISWC 2021) poster and demo session. Antonello Meloni1, Simone Angioni1, Angelo Antonio Salatino2, Francesco Osborne2, Diego Reforgiato Recupero1, Enrico Motta2 1 Department of Mathematics and Computer Science, University of Cagliari (Italy) 2 Knowledge Media […]
“Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain” is a journal paper accepted at IEEE Access Mojtaba Nayyeri1,2, Gökce Müge Cil1, Sahar Vahdati2, Francesco Osborne3, Andrey Kravchenko4, Simone Angioni5, Angelo Salatino3, Diego Reforgiato Recupero5, Enrico Motta3, Jens Lehmann1,6 1 SDA Research Group, University of Bonn, 53115 Bonn, Germany 2 […]
Abstract 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 repository, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers […]
“Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs” is a book chapter of “Predicting the Dynamics of Research Impact” edited by Springer. Angelo A. Salatino1, Andrea Mannocci2, and Francesco Osborne1 1Knowledge Media Institute – The Open University, Milton Keynes, United Kingdom 2Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo”, Italian National Research […]
“CSO Classifier 3.0: A Scalable Unsupervised Method for Classifying Documents in Terms of Research Topics” is a journal paper accepted at the Special Issue of “TPDL 2019 & 2020” at Scientometrics. Angelo Salatino, Francesco Osborne, Enrico Motta Abstract Classifying scientific articles, patents, and other documents according to the relevant research topics is an important task, […]
“Trans4E: Link Prediction on Scholarly Knowledge Graphs” is a journal paper submitted to the Special Issue on “Knowledge Graph Representation & Reasoning” at the Neurocomputing Journal Mojtaba Nayyeria, Gokce Muge Cila, Sahar Vahdatib, Francesco Osborned, Mahfuzur Rahmana,Simone Angionie, Angelo Salatinod, Diego Reforgiato Recuperoe, Nadezhda Vassilyevaa, Enrico Mottad and Jens Lehmanna,c aSDA Research Group, University […]
On 12th May 2021, I have been invited by Dimitris Sacharidis to give a lecture to the master course is INFO-H509 “XML and Web Technologies” at the Université Libre de Bruxelles. Abstract In the last decade, several Scientific Knowledge Graphs (SKG) were released, representing scientific knowledge in a structured, interlinked, and semantically rich manner. But, what […]
Clique Percolation Method (CPM) is an algorithm for finding overlapping communities within networks, introduced by Palla et al. (2005, see references). This implementation in Python, 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 […]
The Smart Topic Miner, which is an innovative state-of-the-art AI application for automating editorial processes at Springer Nature and improving access to scientific knowledge, has been shortlisted for the “Most Innovative use of AI” DataIQ 2020 Awards. Smart Topic Miner analyses scientific publications in Computer Science and classifies them with very high accuracy in terms […]
Ontologies of research areas have been proven to be useful in many application for analysing and making sense of scholarly data. In this lecture, I will present how we produced the Computer Science Ontology (CSO), which is the largest ontology of research areas in the field of Computer Science, and discuss a number of applications that build on CSO, to support high-level tasks, such as topic classification, research trends forecasting, metadata extraction, and recommendation of books.