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QuAre: Question Answering for MonitoRed Fuel Cell systEms



Modern advanced and high value fuel cell systems are monitored by multiple embedded sensors which transmit a large amount of data every few seconds. Unfortunately, service engineers are still faced with the challenging task of identifying the causes of a failure by manually investigating not only the streaming sensor data but also a wide range of structured, semi-structured and unstructured monitoring data. At the same time, they are required to have a thorough knowledge of the full operating mechanism. Our overarching aim is to utilise next generation deep learning and knowledge technology paradigms (i.e. ontologybased systems, knowledge-graph based systems) to represent this monitoring knowledge in a human and machine processible form such that decision-making processes can be automated and deeper engineering insights can be obtained. To achieve this, we will implement a radically cross-disciplinary methodological approach, by developing new spatio-temporal knowledge representations and reasoning and instilling them with natural language processing techniques. This will result in a novel paradigm for truly intelligent cyber physical systems. The QuAre paradigm will be put to test and fine tuned on the diagnosis and prognosis of polymer electrolyte fuel cell systems. On the training side, this project is designed to instill the applicant with a niche set of core skills on question answering over knowledge graph embeddings, knowledge management retrieval, and natural language generation; these will position the researcher at the fore-front of intelligent knowledge representation and establish her as a leading researcher in the field of question answering. The project is further designed to provide the researcher with cutting edge teaching, leadership, and communication skills so that by the end of this project she will be ready to pursue her first permanent academic position.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101032307


Scientific Director: Prof Manolis Koubarakis

Research Fellow: Dr Eleni Tsalapati 


! New paper: Angelos Poulis, Eleni Tsalapati, Manolis Koubarakis: Reasoning over Description Logic-based contexts with Transformers. CoRR abs/2311.08941 (2023).

! New paper: Eleni Tsalapati, Markos Iliakis, Manolis Koubarakis: Location Query Answering Using Box Embeddings. Workshop on Deep Learning for Knowledge Graphs hosted by ISWC2023, Athens, Greece, 2023. Best paper award.

! New paper: Eleni Tsalapati, Manolis Koubarakis: SHM: A Light-weight, Mid-level Ontology for Reliable System Health Monitoring. 2nd International Workshop on Semantic Industrial Information Modelling hosted by ISWC2023, Athens, Greece, 2023.


Invited Talks

  1. “Reasoning over ontological knowledge bases with standard and non-standard methods”, SKEL | The AI Lab, Institute of Informatics & Telecommunications, Dimokritos, 2023 (left picture).

  2. “A KG-based System for Early System Diagnosis” Stream Reasoning workshop at Siemens Munich, 2022 (right picture).

  3. “Stream Reasoning for PEM Fuel Cell System Diagnosis”, Stream Reasoning workshop, virtual event, 2021