معرفی
Fabian Suchanek is a full professor at Institut Polytechnique de Paris, specifically affiliated with Télécom Paris. He leads research in the Data, Intelligence, and Graphs (DIG) team within the Computer Science department. His academic career focuses on bridging artificial intelligence with structured knowledge representations.
Suchanek's research interests span artificial intelligence, knowledge bases, and natural language processing, with particular emphasis on knowledge graph construction, rule mining, knowledge-based language models, and explainable AI. His work demonstrates how structured knowledge can enhance machine learning systems, particularly large language models, by providing factual grounding and interpretability. The research group he leads develops practical systems that address real-world knowledge management challenges.
His recent publications showcase a strong trajectory in knowledge-intensive AI, with notable contributions to knowledge graph completion, rule mining techniques, and neural approaches to knowledge base validation. The research demonstrates increasing integration between symbolic and neural approaches to AI.
- Best Student Paper Award at KR 2024 for work on contextual reasoning
- Best Demo Award of IJCAI 2024 for rule mining in knowledge graphs
- French Open Research Award for the YAGO project
- Best Paper Award of ESWC 2021 for Neural Knowledge Base Repairs
Suchanek has secured significant research funding, evidenced by his active recruitment of PhD students for knowledge-based language model research. He has held visiting positions, including at Nanyang Technological University (June-September 2023), and is recognized internationally through keynote invitations such as the Singapore ACM SIGKDD Symposium 2023. He has deliberately stepped back from administrative duties at Institut Polytechnique de Paris to focus on research.
His laboratory maintains strong industry connections through open-source software projects including the YAGO knowledge base, AMIE for rule mining, STACI for explainable AI, and several other tools that have become standard in knowledge representation research.
