About

The SKM3 team is developing innovative approaches to generate value out of scholarly data by leveraging large-scale data mining, semantic technologies, machine learning, and visual analytics .

We released a number of knowledge graph for exploring research dynamics including:

  • The Computer Science Ontology (CSO), the largest taxonomy of research topics in the field.
  • The Academia/Industry DynAmics (AIDA) Knowledge Graph, an innovative resource describing  14M publications and 8M patents according to their topics and industrial sectors.
  • The Artificial Intelligence Knowledge Graph (AI-KG),  a large-scale automatically generated knowledge graph that describes 850K entities (e.g., tasks, methods, metrics, materials, others) relevant to AI according to 1,2M statements extracted from 333K articles.

We are pursuing several research avenues, including:

We collaborate with major publishers and universities to generate scalable applications, such as search engines, recommender systems, and analytics tools. In particular, we are currently working closely with Springer Nature in the development of a number of semantically-enhanced solutions, such as Smart Topic Miner, a web application that supports editors in classifying books with relevant metadata, and the Smart Book Recommender, a system that assists editors in deciding which products should be marketed at scientific venues.

In 2019, we released the Computer Science Ontology (CSO), which is currently the largest taxonomy of research areas in the field and has been officially adopted by Springer Nature.  In the context of our collaboration with Springer Nature, I have also designed and co-developed the Smart Topic Miner, a tool is used by editors at Springer Nature to generate automatically the scholarly metadata for all their computer science proceedings, including flagship series, such as Lecture Notes in Computer Science (LNCS), Lecture Notes in Artificial Intelligence, and others.

In 2020, we generated the Academia/Industry DynAmics (AIDA) Knowledge Graph, an innovative resource for supporting large-scale analyses of research trends across academia and industry.  AIDA describes 14M publications and 8M patents according to the research topics drawn from CSO, the type of the author’s affiliations (e.g., academy, industry, collaborative), and 66 industrial sectors (e.g., automotive, financial, energy, electronics). In the same year we also produced the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically generated knowledge graph that describes 850K entities (e.g., tasks, methods, metrics, materials, others) relevant to AI according to 1,2M statements extracted from 333K articles. AI-KG was designed to support a large variety of intelligent services for analysing and making sense of research dynamics, assisting researchers, and informing decision of founding bodies and research policy makers.