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.
Publications:
Dessì, Danilo, Osborne, Francesco, Reforgiato Recupero, Diego, Buscaldi, Davide, Motta, Enrico, Sack, Harald, AI-KG: an Automatically Generated Knowledge Graph of Artificial Intelligence
Angioni, Simone, Salatino, Angelo, Osborne, Francesco, Reforgiato Recupero, Diego, Motta, Enrico, The AIDA Dashboard: Analysing Conferences with Semantic Technologies
Salatino, Angelo, Osborne, Francesco, Motta, Enrico, ResearchFlow: Understanding the Knowledge Flow between Academia and Industry
Salatino, Angelo, Thanapalasingam, Thiviyan, Mannocci, Andrea, Birukou, Aliaksandr, Osborne, Francesco, Motta, Enrico, The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas
Angioni, Simone, Salatino, Angelo, Osborne, Francesco, Reforgiato Recupero, Diego, Motta, Enrico, Integrating Knowledge Graphs for Analysing Academia and Industry Dynamics
