“AIDA: a Knowledge Graph about Research Dynamics in Academia and Industry” is a research paper published at the Special Issue on “Scientific Knowledge Graphs and Research Impact Assessment” at Quantitative Science Studies (QSS by MIT Press). Simone Angioni1, Angelo Antonio Salatino2, Francesco Osborne2, Diego Reforgiato Recupero1, Enrico Motta2 1 Department of Mathematics and Computer Science, University […]
“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 […]
“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 […]
“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 […]
Ontologies of research areas have been proven to be useful in many application for analysing and making sense of scholarly data. In this chapter, we present 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, metadata extraction, and recommendation of books.
In the last decade, we experienced an urgent need for a flexible, context-sensitive, fine-grained, and machine-actionable representation of scholarly knowledge and corresponding infrastructures for knowledge curation, publishing and processing. Such technical infrastructures are becoming increasingly popular in representing scholarly knowledge as structured, interlinked, and semantically rich Scholarly Knowledge Graphs (SKG).
The 1st Workshop on Scientific Knowledge Graphs (SKG2020) aims at bringing together researchers and practitioners from different fields (including, but not limited to, Digital Libraries, Information Extraction, Machine Learning, Semantic Web, Knowledge Engineering, Natural Language Processing, Scholarly Communication, and Bibliometrics) in order to explore innovative solutions and ideas for the production and consumption of Scientific Knowledge Graphs (SKGs).
Last week — 18th to 21st September 2019 — the first International Competition on Smart Cities and Robotics took place in Milton Keynes (UK). Different teams from Spain, UK, Germany, France, Portugal and others took part in this competition. As the name suggests, SCiRoC aims at bringing robots in the context of smart cities. Indeed, their primary objective was to interact both with smart cities infrastructures, such as the MK Data Hub, and citizens.