supporting editorial activities at springer nature project full details
Timeline:01 May 2018 - 31 Jan 2021
Supporting Editorial Activities at Springer Nature
The project aims at fostering Springer Nature editorial activities by supporting them with a variety of smart solutions leveraging artificial intelligence, data mining, and semantic technologies. In particular, the KMi team will support Springer Nature editorial team in classifying proceedings and other editorial products, taking informed decisions about their marketing strategy, and improve their internal classification.
The main objectives of the project are:
- Producing several analytics solutions for the analysis of big scholarly data.
- Automatically generating a large-scale ontology describing research topics in the field of Engineering.
- Enhancing the Smart Topics Miner, a tool developed to support the Springer Nature editorial team in classifying proceedings.
- Releasing the Computer Science Ontology, the largest ontology of research areas in the field of Computer Science, which currently includes about 15K topics and 70K semantic relationships.
23 Feb 2021
10 Jan 2019
Dessi', D., Osborne, F., Recupero, D., Buscaldi, D., Motta, E. and Sack, H. (2020) AI-KG: an Automatically Generated Knowledge Graph of Artificial Intelligence, International Semantic Web Conference 2020, Online
Angioni, S., Salatino, A.A., Osborne, F., Recupero, D. and Motta, E. (2020) The AIDA Dashboard: Analysing Conferences with Semantic Technologies, 19th International Semantic Web Conference (ISWC 2020), Athens (Greece) - Virtual
Salatino, A.A., Osborne, F. and Motta, E. (2020) ResearchFlow: Understanding the Knowledge Flow between Academia and Industry, 22nd International Conference on Knowledge Engineering and Knowledge Management (EKAW 2020), Bolzano (Italy) (Online)
Salatino, A.A., Thanapalasingam, T., Mannocci, A., Birukou, A., Osborne, F. and Motta, E. (2020) The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas, Data Intelligence, 2, 2, pp. 379-416
Angioni, S., Salatino, A.A., Osborne, F., Recupero, D. and Motta, E. (2020) Integrating Knowledge Graphs for Analysing Academia and Industry Dynamics, 1st Workshop on Scientific Knowledge Graphs, Lyon, France