Lecturer: Ilaria Tiddi
Fields: Artificial Intelligence
Content
Hybrid Intelligence (HI) is a rapidly growing field aiming at creating collaborative systems where humans and intelligent machines cooperate in mixed teams towards shared goals. In this course, we will get to know the field by discussing the vision, and basics, and the solutions that have been proposed so far. In particular, we will focus how symbolic AI techniques (knowledge graphs and semantic technologies) have been proposed as complementary building blocks to the subsymbolic (machine learning) methods, and how this combination has been used to help solving the main challenges in the field of Hybrid Intelligence.
The course comprehends 3 lectures:
1) Introduction to Hybrid Intelligence (1.5h). Here we will introduce the main research questions for the field of HI, present some of the solutions proposed so far, and finally discuss open challenges.
2) Introduction to Knowledge Graphs (1.5h). Here we will introduce the basics of Knowledge Engineering, including modelling information and reason about it using the RDF/RDFS/OWL languages, principles of knowledge/ontology engineering, and methods to query knowledge graphs.
3) Knowledge Engineering for Hybrid Intelligence (1.5h). The last lecture will introduce how ontologies and knowledge engineering methods can be used to design Hybrid Intelligence applications.
Learning objectives:
– familiarise with Hybrid Intelligence challenges and methods
– get to know the basics of Knowledge Graphs (RDF, OWL, SPARQL, ML over graphs)
– apply the principles of knowledge engineering for the design for Hybrid Intelligence applications
Literature
- Hogan, Aidan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard De Melo, Claudio Gutierrez, Sabrina Kirrane et al. “Knowledge graphs.” ACM Computing Surveys (Csur) 54, no. 4 (2021): 1-37.
- Akata, Zeynep, Dan Balliet, Maarten De Rijke, Frank Dignum, Virginia Dignum, Guszti Eiben, Antske Fokkens et al. “A research agenda for hybrid intelligence: augmenting human intellect with collaborative, adaptive, responsible, and explainable artificial intelligence.” Computer 53, no. 8 (2020): 18-28.
- Ilaria Tiddi, Victor De Boer, Stefan Schlobach, and André Meyer-Vitali. 2023. Knowledge Engineering for Hybrid Intelligence. In Proceedings of the 12th Knowledge Capture Conference 2023 (K-CAP \’23). Association for Computing Machinery, New York, NY, USA, 75–82. https://doi.org/10.1145/3587259.3627541
- Tiddi, Ilaria, and Stefan Schlobach. “Knowledge graphs as tools for explainable machine learning: A survey.” Artificial Intelligence 302 (2022): 103627.
Lecturer
Ilaria Tiddi is an Assistant Professor in Hybrid Intelligence at the Knowledge in AI (KAI) group of the Vrije Universiteit Amsterdam (NL). Her research focuses on creating systems that generate complex narratives through a combination of semantic technologies, open data and machine learning, applied mostly in scientific and robotics scenarios. She is Editor-in-Chief of the CEUR-WS publication, part of the Steering Committee for the Hybrid Human-AI Conference, and Coordinator of the international Staff Exchange for the Dutch Hybrid Intelligence consortium. Since 2014, she is regularly active in the OCs/PCs of the major venues in the KR field (ISWC/ESWC, HHAI, WWW, CIKM, IJCAI/ECAI).
Affiliation: Vrije Universiteit Amsterdam
Homepage: https://github.com/kmitd/