Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs

“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 […]

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CSO Classifier 3.0: A Scalable Unsupervised Method for Classifying Documents in Terms of Research Topics

“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, […]

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Trans4E: Link Prediction on Scholarly Knowledge Graphs

“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 […]

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Scientific Knowledge Graphs: an Overview

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 […]

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Applying Machine Learning Techniques to Big Data in the Scholarly Domain

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.

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The AIDA Dashboard: Analysing Conferences with Semantic Technologies

“The AIDA Dashboard: Analysing Conferences with Semantic Technologies” is a demo paper submitted to the Posters and Demos tracks of the 19th International Semantic Web Conference.   Simone Angioni1, Francesco Osborne2, Angelo A. Salatino2, Diego Reforgiato Recupero1, Enrico Motta2 1 University of Cagliari, Via Università 40, 09124 Cagliari 2 Knowledge Media Institute, The Open University, […]

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AIDA Dashboard

The AIDA Dashboard is a web application that allows users to visualize several kind of analytics about a specific conference (see Figure 1). The backend is developed in Python, while the frontend is in HTML5 and Javascript. The AIDA Dashboard builds on the Academia/Industry DynAmics knowledge graph (AIDA), a large knowledge base describing 14M articles […]

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Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium

“Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium” is the introductory chapter for the ADBIS , TPDL and EDA 2020 Common Workshops and Doctoral Consortium proceedings as satellite events of the 2020 International Conference on Theory and Practice of Digital Libraries (TPDL2020). Ladjel Bellatreche, […]

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ResearchFlow: Understanding the Knowledge Flow between Academia and Industry

“ResearchFlow: Understanding the Knowledge Flow between Academia and Industry” is a conference paper submitted to Knowledge Engineering and Knowledge Management – 22nd International Conference, EKAW 2020. Angelo Salatino, Francesco Osborne, Enrico Motta Abstract Understanding, monitoring, and predicting the flow of knowledge between academia and industry is of critical importance for a variety of stakeholders, including governments, funding […]

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Integrating Knowledge Graphs for Analysing Academia and Industry Dynamics

Academia and industry are constantly engaged in a joint effort for producing scientific knowledge that will shape the society of the future. Analysing the knowledge flow between them and understanding how they influence each other is a critical task for researchers, governments, funding bodies, investors, and companies. However, current corpora are unfit to support large-scale analysis of the knowledge flow between academia and industry since they lack of a good characterization of research topics and industrial sectors. In this short paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which characterizes 14M papers and 8M patents according to the research topics drawn from the Computer Science Ontology. 4M papers and 5M patents are also classified according to the type of the author’s affiliations (academy, industry, or collaborative) and 66 industrial sectors (e.g., automotive, financial, energy, electronics) obtained from DBpedia. AIDA was generated by an automatic pipeline that integrates several knowledge graphs and bibliographic corpora, including Microsoft Academic Graph, Dimensions, English DBpedia, the Computer Science Ontology, and the Global Research Identifier Database.

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