InnoGraph will build a holistic knowledge graph of innovation based on Artificial Intelligence (AI), and more generally of the global “hitech” ecosystem. It is a key use case of the Horizon Europe research project enrichMyData. InnoGraph originated from a partnership between OECD and the Jožef Stefan Institute (JSI) on the OECD.AI Policy Observatory, and prior Ontotext experience with Science KGs, e.g., the Tracking of Research Results project. Unlike OECD.AI, here we want to track AI elements not just at the summary level, but also at individual level.
InnoGraph aims to comprehensively cover all elements of AI. As the first key step, we have built a comprehensive taxonomy of topics: AI technical topics and application areas (verticals).
This paper describes our approach to developing such a taxonomy by integrating and coreferencing data from numerous sources. It covers:
You can also check out the short version of this research.
About the authors:
Vladimir Alexiev is Chief Data Architect at Ontotext. His passion is data modeling, ontologies, and data representation standards. He is a member of the DBpedia and Europeana quality committees and a frequent speaker at conferences and events. His favorite topics are Linked Open Data and its application in cultural heritage and digital humanities.
Boyan Bechev is a data engineer for the solutions team at Ontotext. He’s involved in several projects that require precise data modeling and data quality and is not afraid to get his hands dirty. Having an academic background in distributed computing he’s passionate about dealing with large datasets in an efficient as possible manner.
Aleksandr Ositsyn is a machine learning engineer who has experience with Continuous Integration and Continuous Delivery (CI/CD), Django REST Framework, Python (Programming Language), SQL, Machine Learning & Docker Products.