Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Gagnon, S.
"Use of AI to Fight Disinformation and Corruption" Article d'actes
Dans: Am. Conf. Inf. Syst., AMCIS, p. 4853–4854, Association for Information Systems, 2025, ISBN: 979-833132774-3 (ISBN).
Résumé | Liens | BibTeX | Étiquettes: Action research, Audio videos, Copyrights, Crime, Due diligence, Information leakage, Information use, Institutional change, Integrated strategy, Knowledge graph, Knowledge graphs, Open source intelligence, Open systems, Research advances
@inproceedings{gagnon_use_2025,
title = {"Use of AI to Fight Disinformation and Corruption"},
author = {S. Gagnon},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025353954&partnerID=40&md5=fc3ca73e0de02b06fd218e03588f3e22},
isbn = {979-833132774-3 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Am. Conf. Inf. Syst., AMCIS},
volume = {7},
pages = {4853–4854},
publisher = {Association for Information Systems},
abstract = {Disinformation (false information disseminated with the intent to cause harm) takes many forms, including fake news, audio-video impersonations, data falsification, and more. It is linked to allegations of corruption and fraud against elected officials, which, once disambiguated (whether true or false), will in all cases require immediate action to preserve democracy. An integrated strategy to combat disinformation and corruption is needed, requiring constant vigilance, due diligence, and open-source intelligence (OSINT) collaboration. Our PDS will explore how AI, especially LLMs and Knowledge Graphs, can serve this purpose and automate the necessary means to combat hybrid threats to democracy. The PDS will cover the following issues and latest research advances, allowing also interactive roundtables to ensure participants can contribute insight to the debate: 1. Presentation: fighting jointly against disinformation and corruption 2. Roundtable: what innovations are most effective in combating hybrid threats? 3. Demo: ai and knowledge graphs to disambiguate allegations against elected officials 4. Roundtable: what institutional changes are required to fight hybrid threats? 5. Conclusion: action research helping parliamentary institutions develop AI capabilities. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.},
keywords = {Action research, Audio videos, Copyrights, Crime, Due diligence, Information leakage, Information use, Institutional change, Integrated strategy, Knowledge graph, Knowledge graphs, Open source intelligence, Open systems, Research advances},
pubstate = {published},
tppubtype = {inproceedings}
}
Disinformation (false information disseminated with the intent to cause harm) takes many forms, including fake news, audio-video impersonations, data falsification, and more. It is linked to allegations of corruption and fraud against elected officials, which, once disambiguated (whether true or false), will in all cases require immediate action to preserve democracy. An integrated strategy to combat disinformation and corruption is needed, requiring constant vigilance, due diligence, and open-source intelligence (OSINT) collaboration. Our PDS will explore how AI, especially LLMs and Knowledge Graphs, can serve this purpose and automate the necessary means to combat hybrid threats to democracy. The PDS will cover the following issues and latest research advances, allowing also interactive roundtables to ensure participants can contribute insight to the debate: 1. Presentation: fighting jointly against disinformation and corruption 2. Roundtable: what innovations are most effective in combating hybrid threats? 3. Demo: ai and knowledge graphs to disambiguate allegations against elected officials 4. Roundtable: what institutional changes are required to fight hybrid threats? 5. Conclusion: action research helping parliamentary institutions develop AI capabilities. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.
2.
Fareedi, A. A.; Ismail, M.; Gagnon, S.; Ghazanweh, A.; Arooj, Z.
Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management Article de journal
Dans: Systems, vol. 13, no 2, 2025, ISSN: 20798954 (ISSN), (Publisher: Multidisciplinary Digital Publishing Institute (MDPI)).
Résumé | Liens | BibTeX | Étiquettes: conversational agent, CRISP-KG, Knowledge graphs, ontologies, SWRL
@article{fareedi_digital_2025,
title = {Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management},
author = {A. A. Fareedi and M. Ismail and S. Gagnon and A. Ghazanweh and Z. Arooj},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218906224&doi=10.3390%2fsystems13020072&partnerID=40&md5=53b218763023a1bec3d69899d4dd7a86},
doi = {10.3390/systems13020072},
issn = {20798954 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Systems},
volume = {13},
number = {2},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
abstract = {The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs. © 2025 by the authors.},
note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},
keywords = {conversational agent, CRISP-KG, Knowledge graphs, ontologies, SWRL},
pubstate = {published},
tppubtype = {article}
}
The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs. © 2025 by the authors.
3.
Fareedi, A. A.; Gagnon, S.; Ghazawneh, A.; Valverde, R.
Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System Article de journal
Dans: Future Internet, vol. 17, no 6, 2025, ISSN: 19995903 (ISSN), (Publisher: Multidisciplinary Digital Publishing Institute (MDPI)).
Résumé | Liens | BibTeX | Étiquettes: Data integration, Data interoperability, Federated information systems, Federated ontology, Graph framework, Interoperability, Knowledge graphs, Knowledge management, Medical computing, Ontology, Ontology's, Ontop, Query processing, Semantic interoperability, Semantic Web, Semantics, Virtual knowledge, virtual reality, Virtualization, Virtualized knowledge graph
@article{fareedi_semantic_2025,
title = {Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System},
author = {A. A. Fareedi and S. Gagnon and A. Ghazawneh and R. Valverde},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105009278332&doi=10.3390%2ffi17060245&partnerID=40&md5=72bd14d033887f72e7e0c8a6a1451415},
doi = {10.3390/fi17060245},
issn = {19995903 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Future Internet},
volume = {17},
number = {6},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
abstract = {Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques. © 2025 by the authors.},
note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},
keywords = {Data integration, Data interoperability, Federated information systems, Federated ontology, Graph framework, Interoperability, Knowledge graphs, Knowledge management, Medical computing, Ontology, Ontology's, Ontop, Query processing, Semantic interoperability, Semantic Web, Semantics, Virtual knowledge, virtual reality, Virtualization, Virtualized knowledge graph},
pubstate = {published},
tppubtype = {article}
}
Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques. © 2025 by the authors.
4.
Fareedi, A. A.; Ismail, M.; Gagnon, S.; Ghazanweh, A.; Arooj, Z.
Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management Article de journal
Dans: Systems, vol. 13, no 2, 2025, ISSN: 20798954 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: conversational agent, CRISP-KG, Knowledge graphs, ontologies, SWRL
@article{fareediDigitalHealthTransformation2025a,
title = {Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management},
author = {A. A. Fareedi and M. Ismail and S. Gagnon and A. Ghazanweh and Z. Arooj},
url = {https://www.scopus.com/pages/publications/85218906224?origin=resultslist},
doi = {10.3390/systems13020072},
issn = {20798954 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Systems},
volume = {13},
number = {2},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
abstract = {The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs. © 2025 by the authors.},
keywords = {conversational agent, CRISP-KG, Knowledge graphs, ontologies, SWRL},
pubstate = {published},
tppubtype = {article}
}
The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs. © 2025 by the authors.
5.
Gagnon, S.
"Use of AI to Fight Disinformation and Corruption" Article d'actes
Dans: Am. Conf. Inf. Syst., AMCIS, p. 4853–4854, Association for Information Systems, 2025, ISBN: 979-833132774-3 (ISBN), (Journal Abbreviation: Am. Conf. Inf. Syst., AMCIS).
Résumé | Liens | BibTeX | Étiquettes: Action research, Audio videos, Copyrights, Crime, Due diligence, Information leakage, Information use, Institutional change, Integrated strategy, Knowledge graph, Knowledge graphs, Open source intelligence, Open systems, Research advances
@inproceedings{gagnonUseAIFight2025,
title = {"Use of AI to Fight Disinformation and Corruption"},
author = {S. Gagnon},
url = {https://www.scopus.com/pages/publications/105025353954?origin=resultslist},
isbn = {979-833132774-3 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Am. Conf. Inf. Syst., AMCIS},
volume = {7},
pages = {4853–4854},
publisher = {Association for Information Systems},
abstract = {Disinformation (false information disseminated with the intent to cause harm) takes many forms, including fake news, audio-video impersonations, data falsification, and more. It is linked to allegations of corruption and fraud against elected officials, which, once disambiguated (whether true or false), will in all cases require immediate action to preserve democracy. An integrated strategy to combat disinformation and corruption is needed, requiring constant vigilance, due diligence, and open-source intelligence (OSINT) collaboration. Our PDS will explore how AI, especially LLMs and Knowledge Graphs, can serve this purpose and automate the necessary means to combat hybrid threats to democracy. The PDS will cover the following issues and latest research advances, allowing also interactive roundtables to ensure participants can contribute insight to the debate: 1. Presentation: fighting jointly against disinformation and corruption 2. Roundtable: what innovations are most effective in combating hybrid threats? 3. Demo: ai and knowledge graphs to disambiguate allegations against elected officials 4. Roundtable: what institutional changes are required to fight hybrid threats? 5. Conclusion: action research helping parliamentary institutions develop AI capabilities. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.},
note = {Journal Abbreviation: Am. Conf. Inf. Syst., AMCIS},
keywords = {Action research, Audio videos, Copyrights, Crime, Due diligence, Information leakage, Information use, Institutional change, Integrated strategy, Knowledge graph, Knowledge graphs, Open source intelligence, Open systems, Research advances},
pubstate = {published},
tppubtype = {inproceedings}
}
Disinformation (false information disseminated with the intent to cause harm) takes many forms, including fake news, audio-video impersonations, data falsification, and more. It is linked to allegations of corruption and fraud against elected officials, which, once disambiguated (whether true or false), will in all cases require immediate action to preserve democracy. An integrated strategy to combat disinformation and corruption is needed, requiring constant vigilance, due diligence, and open-source intelligence (OSINT) collaboration. Our PDS will explore how AI, especially LLMs and Knowledge Graphs, can serve this purpose and automate the necessary means to combat hybrid threats to democracy. The PDS will cover the following issues and latest research advances, allowing also interactive roundtables to ensure participants can contribute insight to the debate: 1. Presentation: fighting jointly against disinformation and corruption 2. Roundtable: what innovations are most effective in combating hybrid threats? 3. Demo: ai and knowledge graphs to disambiguate allegations against elected officials 4. Roundtable: what institutional changes are required to fight hybrid threats? 5. Conclusion: action research helping parliamentary institutions develop AI capabilities. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.
6.
Fareedi, A. A.; Gagnon, S.; Ghazawneh, A.; Valverde, R.
Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System Article de journal
Dans: Future Internet, vol. 17, no 6, 2025, ISSN: 19995903 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Data integration, Data interoperability, Federated information systems, Federated ontology, Graph framework, Interoperability, Knowledge graphs, Knowledge management, Medical computing, Ontology, Ontology's, Ontop, Query processing, Semantic interoperability, Semantic Web, Semantics, Virtual knowledge, virtual reality, Virtualization, Virtualized knowledge graph
@article{fareediSemanticFusionHealth2025a,
title = {Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System},
author = {A. A. Fareedi and S. Gagnon and A. Ghazawneh and R. Valverde},
url = {https://www.scopus.com/pages/publications/105009278332?origin=resultslist},
doi = {10.3390/fi17060245},
issn = {19995903 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Future Internet},
volume = {17},
number = {6},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
abstract = {Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques. © 2025 by the authors.},
keywords = {Data integration, Data interoperability, Federated information systems, Federated ontology, Graph framework, Interoperability, Knowledge graphs, Knowledge management, Medical computing, Ontology, Ontology's, Ontop, Query processing, Semantic interoperability, Semantic Web, Semantics, Virtual knowledge, virtual reality, Virtualization, Virtualized knowledge graph},
pubstate = {published},
tppubtype = {article}
}
Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques. © 2025 by the authors.
7.
Amamou, H.; Gagnon, S.; Davoust, A.; Avila, A. R.
Towards Robust Retrieval-Augmented Generation Based on Knowledge Graph: A Comparative Analysis Article d'actes
Dans: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern., p. 80–85, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 1062922X (ISSN); 979-833153358-8 (ISBN), (Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.).
Résumé | Liens | BibTeX | Étiquettes: Benchmarking, Comparative analyzes, External sources, Generation systems, Information integration, Information retrieval, Knowledge graph, Knowledge graphs, Language model, Model response, Noise robustness, Pre-training, Prior-knowledge
@inproceedings{amamouRobustRetrievalAugmentedGeneration2025,
title = {Towards Robust Retrieval-Augmented Generation Based on Knowledge Graph: A Comparative Analysis},
author = {H. Amamou and S. Gagnon and A. Davoust and A. R. Avila},
url = {https://www.scopus.com/pages/publications/105033145604?origin=resultslist},
doi = {10.1109/SMC58881.2025.11343466},
isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
pages = {80–85},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Retrieval-Augmented Generation (RAG) was first introduced to enhance the capabilities of Large Language Models (LLMs) beyond their encoded-prior knowledge. This is achieved by providing LLMs with an external source of knowledge, which helps to reduce factual hallucinations and enables the access to new information, typically not available during their pretraining phase. Despite its benefits, there is an increasing concern with the impact of inconsistent retrieved information towards LLMs' responses. Hence, the Retrieval-Augmented Generation Benchmark (RGB) was introduced as a new testbed for RAG evaluation, meant to assess the robustness of LLMs towards inconsistency in the retrieved information. In this work, we use the RGB corpus to evaluate LLMs in four scenarios: (1) noise robustness; (2) information integration; (3) negative rejection; and (4) counterfactual robustness. We perform a comparative analysis between the RAG baseline defined by the RGB and variations of GraphRAG, which is a RAG system based on a Knowledge Graph (KG) and developed to retrieve relevant information from large documents. We tested GraphRAG under three customization to improve its robustness. Our approach demonstrates improvements compared to the RGB baseline, providing insights on how to design more reliable RAG systems, tailored for real-world scenarios. © 2025 IEEE.},
note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
keywords = {Benchmarking, Comparative analyzes, External sources, Generation systems, Information integration, Information retrieval, Knowledge graph, Knowledge graphs, Language model, Model response, Noise robustness, Pre-training, Prior-knowledge},
pubstate = {published},
tppubtype = {inproceedings}
}
Retrieval-Augmented Generation (RAG) was first introduced to enhance the capabilities of Large Language Models (LLMs) beyond their encoded-prior knowledge. This is achieved by providing LLMs with an external source of knowledge, which helps to reduce factual hallucinations and enables the access to new information, typically not available during their pretraining phase. Despite its benefits, there is an increasing concern with the impact of inconsistent retrieved information towards LLMs' responses. Hence, the Retrieval-Augmented Generation Benchmark (RGB) was introduced as a new testbed for RAG evaluation, meant to assess the robustness of LLMs towards inconsistency in the retrieved information. In this work, we use the RGB corpus to evaluate LLMs in four scenarios: (1) noise robustness; (2) information integration; (3) negative rejection; and (4) counterfactual robustness. We perform a comparative analysis between the RAG baseline defined by the RGB and variations of GraphRAG, which is a RAG system based on a Knowledge Graph (KG) and developed to retrieve relevant information from large documents. We tested GraphRAG under three customization to improve its robustness. Our approach demonstrates improvements compared to the RGB baseline, providing insights on how to design more reliable RAG systems, tailored for real-world scenarios. © 2025 IEEE.
8.
Fareedi, A. A.; Gagnon, S.; Ghazawneh, A.; Valverde, R.
Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System Article de journal
Dans: Future Internet, vol. 17, no 6, 2025, ISSN: 19995903 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Data integration, Data interoperability, Federated information systems, Federated ontology, Graph framework, Interoperability, Knowledge graphs, Knowledge management, Medical computing, Ontology, Ontology's, Ontop, Query processing, Semantic interoperability, Semantic Web, Semantics, Virtual knowledge, virtual reality, Virtualization, Virtualized knowledge graph
@article{fareediSemanticFusionHealth2025b,
title = {Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System},
author = {A. A. Fareedi and S. Gagnon and A. Ghazawneh and R. Valverde},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105009278332&doi=10.3390%2Ffi17060245&partnerID=40&md5=8f53ce5a912bf16f4ac8bb24fb7338bf},
doi = {10.3390/fi17060245},
issn = {19995903 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Future Internet},
volume = {17},
number = {6},
abstract = {Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques. © 2025 by the authors.},
keywords = {Data integration, Data interoperability, Federated information systems, Federated ontology, Graph framework, Interoperability, Knowledge graphs, Knowledge management, Medical computing, Ontology, Ontology's, Ontop, Query processing, Semantic interoperability, Semantic Web, Semantics, Virtual knowledge, virtual reality, Virtualization, Virtualized knowledge graph},
pubstate = {published},
tppubtype = {article}
}
Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques. © 2025 by the authors.
9.
Gagnon, S.
"Use of AI to Fight Disinformation and Corruption" Article d'actes
Dans: Am. Conf. Inf. Syst., AMCIS, p. 4853–4854, Association for Information Systems, 2025, ISBN: 979-833132774-3 (ISBN).
Résumé | Liens | BibTeX | Étiquettes: Action research, Audio videos, Copyrights, Crime, Due diligence, Information leakage, Information use, Institutional change, Integrated strategy, Knowledge graph, Knowledge graphs, Open source intelligence, Open systems, Research advances
@inproceedings{gagnonUseAIFight2025a,
title = {"Use of AI to Fight Disinformation and Corruption"},
author = {S. Gagnon},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025353954&partnerID=40&md5=fc3ca73e0de02b06fd218e03588f3e22},
isbn = {979-833132774-3 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Am. Conf. Inf. Syst., AMCIS},
volume = {7},
pages = {4853–4854},
publisher = {Association for Information Systems},
abstract = {Disinformation (false information disseminated with the intent to cause harm) takes many forms, including fake news, audio-video impersonations, data falsification, and more. It is linked to allegations of corruption and fraud against elected officials, which, once disambiguated (whether true or false), will in all cases require immediate action to preserve democracy. An integrated strategy to combat disinformation and corruption is needed, requiring constant vigilance, due diligence, and open-source intelligence (OSINT) collaboration. Our PDS will explore how AI, especially LLMs and Knowledge Graphs, can serve this purpose and automate the necessary means to combat hybrid threats to democracy. The PDS will cover the following issues and latest research advances, allowing also interactive roundtables to ensure participants can contribute insight to the debate: 1. Presentation: fighting jointly against disinformation and corruption 2. Roundtable: what innovations are most effective in combating hybrid threats? 3. Demo: ai and knowledge graphs to disambiguate allegations against elected officials 4. Roundtable: what institutional changes are required to fight hybrid threats? 5. Conclusion: action research helping parliamentary institutions develop AI capabilities. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.},
keywords = {Action research, Audio videos, Copyrights, Crime, Due diligence, Information leakage, Information use, Institutional change, Integrated strategy, Knowledge graph, Knowledge graphs, Open source intelligence, Open systems, Research advances},
pubstate = {published},
tppubtype = {inproceedings}
}
Disinformation (false information disseminated with the intent to cause harm) takes many forms, including fake news, audio-video impersonations, data falsification, and more. It is linked to allegations of corruption and fraud against elected officials, which, once disambiguated (whether true or false), will in all cases require immediate action to preserve democracy. An integrated strategy to combat disinformation and corruption is needed, requiring constant vigilance, due diligence, and open-source intelligence (OSINT) collaboration. Our PDS will explore how AI, especially LLMs and Knowledge Graphs, can serve this purpose and automate the necessary means to combat hybrid threats to democracy. The PDS will cover the following issues and latest research advances, allowing also interactive roundtables to ensure participants can contribute insight to the debate: 1. Presentation: fighting jointly against disinformation and corruption 2. Roundtable: what innovations are most effective in combating hybrid threats? 3. Demo: ai and knowledge graphs to disambiguate allegations against elected officials 4. Roundtable: what institutional changes are required to fight hybrid threats? 5. Conclusion: action research helping parliamentary institutions develop AI capabilities. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.
10.
Fareedi, A. A.; Ismail, M.; Gagnon, S.; Ghazanweh, A.; Arooj, Z.
Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management Article de journal
Dans: Systems, vol. 13, no 2, 2025, ISSN: 20798954 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: conversational agent, CRISP-KG, Knowledge graphs, ontologies, SWRL
@article{fareediDigitalHealthTransformation2025,
title = {Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management},
author = {A. A. Fareedi and M. Ismail and S. Gagnon and A. Ghazanweh and Z. Arooj},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218906224&doi=10.3390%2fsystems13020072&partnerID=40&md5=53b218763023a1bec3d69899d4dd7a86},
doi = {10.3390/systems13020072},
issn = {20798954 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Systems},
volume = {13},
number = {2},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
abstract = {The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs. © 2025 by the authors.},
keywords = {conversational agent, CRISP-KG, Knowledge graphs, ontologies, SWRL},
pubstate = {published},
tppubtype = {article}
}
The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs. © 2025 by the authors.