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1. Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining

Author

Emmanuel Bresso, Pierre Monnin, Cédric Bousquet, François-Elie Calvier, Ndeye-Coumba Ndiaye, Nadine Petitpain, Malika Smaïl-Tabbone, and Adrien Coulet

Subjects
Adverse drug reaction, Molecular mechanism, Mechanism of action, Explanation, Data mining, Machine learning, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Background Adverse drug reactions (ADRs) are statistically characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases. This is true even for hepatic or skin toxicities, which are classically monitored during drug design. Aside from clinical trials, many elements of knowledge about drug ingredients are available in open-access knowledge graphs, such as their properties, interactions, or involvements in pathways. In addition, drug classifications that label drugs as either causative or not for several ADRs, have been established. Methods We propose in this paper to mine knowledge graphs for identifying biomolecular features that may enable automatically reproducing expert classifications that distinguish drugs causative or not for a given type of ADR. In an Explainable AI perspective, we explore simple classification techniques such as Decision Trees and Classification Rules because they provide human-readable models, which explain the classification itself, but may also provide elements of explanation for molecular mechanisms behind ADRs. In summary, (1) we mine a knowledge graph for features; (2) we train classifiers at distinguishing, on the basis of extracted features, drugs associated or not with two commonly monitored ADRs: drug-induced liver injuries (DILI) and severe cutaneous adverse reactions (SCAR); (3) we isolate features that are both efficient in reproducing expert classifications and interpretable by experts (i.e., Gene Ontology terms, drug targets, or pathway names); and (4) we manually evaluate in a mini-study how they may be explanatory. Results Extracted features reproduce with a good fidelity classifications of drugs causative or not for DILI and SCAR (Accuracy = 0 .74 and 0 .81, respectively). Experts fully agreed that 7 3% and 3 8% of the most discriminative features are possibly explanatory for DILI and SCAR, respectively; and partially agreed (2/3) for 9 0% and 7 7% of them. Conclusion Knowledge graphs provide sufficiently diverse features to enable simple and explainable models to distinguish between drugs that are causative or not for ADRs. In addition to explaining classifications, most discriminative features appear to be good candidates for investigating ADR mechanisms further.

Published
2021
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2. PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison

Author

Pierre Monnin, Joël Legrand, Graziella Husson, Patrice Ringot, Andon Tchechmedjiev, Clément Jonquet, Amedeo Napoli, and Adrien Coulet

Subjects
Knowledge engineering, Knowledge comparison, Semantic web, Ontology, Pharmacogenomics, Linked open data, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Pharmacogenomics (PGx) studies how genomic variations impact variations in drug response phenotypes. Knowledge in pharmacogenomics is typically composed of units that have the form of ternary relationships gene variant – drug – adverse event. Such a relationship states that an adverse event may occur for patients having the specified gene variant and being exposed to the specified drug. State-of-the-art knowledge in PGx is mainly available in reference databases such as PharmGKB and reported in scientific biomedical literature. But, PGx knowledge can also be discovered from clinical data, such as Electronic Health Records (EHRs), and in this case, may either correspond to new knowledge or confirm state-of-the-art knowledge that lacks “clinical counterpart” or validation. For this reason, there is a need for automatic comparison of knowledge units from distinct sources. Results In this article, we propose an approach, based on Semantic Web technologies, to represent and compare PGx knowledge units. To this end, we developed PGxO, a simple ontology that represents PGx knowledge units and their components. Combined with PROV-O, an ontology developed by the W3C to represent provenance information, PGxO enables encoding and associating provenance information to PGx relationships. Additionally, we introduce a set of rules to reconcile PGx knowledge, i.e. to identify when two relationships, potentially expressed using different vocabularies and levels of granularity, refer to the same, or to different knowledge units. We evaluated our ontology and rules by populating PGxO with knowledge units extracted from PharmGKB (2701), the literature (65,720) and from discoveries reported in EHR analysis studies (only 10, manually extracted); and by testing their similarity. We called PGxLOD (PGx Linked Open Data) the resulting knowledge base that represents and reconciles knowledge units of those various origins. Conclusions The proposed ontology and reconciliation rules constitute a first step toward a more complete framework for knowledge comparison in PGx. In this direction, the experimental instantiation of PGxO, named PGxLOD, illustrates the ability and difficulties of reconciling various existing knowledge sources.

Published
2019
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10. Results of the Ontology Alignment Evaluation Initiative 2023.

Author

Mina Abd Nikooie Pour, Alsayed Algergawy, Patrice Buche, Leyla Jael Castro, Jiaoyan Chen 0001, Adrien Coulet, Julien Cufi, Hang Dong 0002, Omaima Fallatah, Daniel Faria, Irini Fundulaki, Sven Hertling, Yuan He 0008, Ian Horrocks 0001, Martin Huschka, Liliana Ibanescu, Sarika Jain 0001, Ernesto Jiménez-Ruiz, Naouel Karam, Patrick Lambrix, Huanyu Li 0001, Ying Li 0029, Pierre Monnin, Engy Nasr, Heiko Paulheim, Catia Pesquita, Tzanina Saveta, Pavel Shvaiko, Guilherme Sousa, Cássia Trojahn, Jana Vatascinová, Mingfang Wu, Beyza Yaman, Ondrej Zamazal, and Lu Zhou 0005

Published
2023

38. Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities

Author

Jiaoyan Chen and Hang Dong and Janna Hastings and Ernesto Jiménez-Ruiz and Vanessa López and Pierre Monnin and Catia Pesquita and Petr Škoda and Valentina Tamma, Chen, Jiaoyan, Dong, Hang, Hastings, Janna, Jiménez-Ruiz, Ernesto, López, Vanessa, Monnin, Pierre, Pesquita, Catia, Škoda, Petr, Tamma, Valentina, Jiaoyan Chen and Hang Dong and Janna Hastings and Ernesto Jiménez-Ruiz and Vanessa López and Pierre Monnin and Catia Pesquita and Petr Škoda and Valentina Tamma, Chen, Jiaoyan, Dong, Hang, Hastings, Janna, Jiménez-Ruiz, Ernesto, López, Vanessa, Monnin, Pierre, Pesquita, Catia, Škoda, Petr, and Tamma, Valentina

Abstract

The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graph-structured. The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledge-driven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery. In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research.

Published
2023
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39. Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges

Author

Claudia d'Amato and Louis Mahon and Pierre Monnin and Giorgos Stamou, d'Amato, Claudia, Mahon, Louis, Monnin, Pierre, Stamou, Giorgos, Claudia d'Amato and Louis Mahon and Pierre Monnin and Giorgos Stamou, d'Amato, Claudia, Mahon, Louis, Monnin, Pierre, and Stamou, Giorgos

Abstract

The graph model is nowadays largely adopted to model a wide range of knowledge and data, spanning from social networks to knowledge graphs (KGs), representing a successful paradigm of how symbolic and transparent AI can scale on the World Wide Web. However, due to their unprecedented volume, they are generally tackled by Machine Learning (ML) and mostly numeric based methods such as graph embedding models (KGE) and deep neural networks (DNNs). The latter methods have been proved lately very efficient, leading the current AI spring. In this vision paper, we introduce some of the main existing methods for combining KGs and ML, divided into two categories: those using ML to improve KGs, and those using KGs to improve results on ML tasks. From this introduction, we highlight research gaps and perspectives that we deem promising and currently under-explored for the involved research communities, spanning from KG support for LLM prompting, integration of KG semantics in ML models to symbol-based methods, interpretability of ML models, and the need for improved benchmark datasets. In our opinion, such perspectives are stepping stones in an ultimate view of KGs as central assets for neuro-symbolic and explainable AI.

Published
2023
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40. Projet ANR (2016-2021) « PractiKPharma » : extraction, comparaison et découverte de connaissances en pharmacogénomique

Author

Adrien Coulet, Pierre Monnin, Orange Labs [Belfort] (Orange Labs), France Télécom, Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), ANR-15-CE23-0028,PractiKPharma,Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique(2015), Monnin, Pierre, Interactions humain-machine, objets connectés, contenus numériques, données massives et connaissance - Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique - - PractiKPharma2015 - ANR-15-CE23-0028 - AAPG2015 - VALID, École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
2022
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41. Understand what you measure: Where climate transition risk metrics converge and why they diverge

Author

Julia Anna Bingler, Chiara Colesanti Senni, and Pierre Monnin

Subjects
Financial climate risks, Climate risk metrics, Transition risk, LASSO, Finance
Abstract

Climate risks are financial risks. To help manage them, researchers and practitioners are exploring which metrics to use to assess climate risks, and to what extent the metrics delivers heterogeneous results. We analyze a unique dataset including risk assessments from 9 providers for firms of the MSCI World Index. Convergence between metrics is higher for the firms most exposed to transition risk. The underlying modeling assumptions and scenario characteristics are associated with changes in the estimated transition risk. Users of climate risk metrics should properly understand the key assumptions underlying a metric to appropriately interpret its result., Finance Research Letters, 50, ISSN:1544-6123

Published
2022

42. Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining

Author

Emmanuel Bresso, Pierre Monnin, Cédric Bousquet, François-Elie Calvier, Ndeye-Coumba Ndiaye, Nadine Petitpain, Malika Smaïl-Tabbone, Adrien Coulet, Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Knowledge representation, reasonning (ORPAILLEUR), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Orange Labs [Belfort] (Orange Labs), France Télécom, Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Centre Hospitalier Universitaire de Saint-Etienne [CHU Saint-Etienne] (CHU ST-E), Nutrition-Génétique et Exposition aux Risques Environnementaux (NGERE), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre Régional de PharmacoVigilance de Lorraine (CRPV Lorraine), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), ANR-15-CE23-0028,PractiKPharma,Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique(2015), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Orange Labs, ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015), Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre Hospitalier Universitaire de Saint-Etienne (CHU de Saint-Etienne), CRHU Nancy, École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Monnin, Pierre, Interactions humain-machine, objets connectés, contenus numériques, données massives et connaissance - Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique - - PractiKPharma2015 - ANR-15-CE23-0028 - AAPG2015 - VALID, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Coulet, Adrien, and Combattre l'insuffisance cardiaque - - FIGHT-HF2015 - ANR-15-RHUS-0004 - RHUS - VALID

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Knowledge graph, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Drug-Related Side Effects and Adverse Reactions, Explanation, Computer applications to medicine. Medical informatics, R858-859.7, Adverse drug reaction, Mechanism of action, Pattern Recognition, Automated, Molecular mechanism, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Pharmacovigilance, Artificial Intelligence, Machine learning, Explainable AI, Adverse Drug Reaction Reporting Systems, Feasibility Studies, Humans, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Drug mechanism of action, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Data mining, Research Article, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

Background Adverse drug reactions (ADRs) are statistically characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases. This is true even for hepatic or skin toxicities, which are classically monitored during drug design. Aside from clinical trials, many elements of knowledge about drug ingredients are available in open-access knowledge graphs, such as their properties, interactions, or involvements in pathways. In addition, drug classifications that label drugs as either causative or not for several ADRs, have been established. Methods We propose in this paper to mine knowledge graphs for identifying biomolecular features that may enable automatically reproducing expert classifications that distinguish drugs causative or not for a given type of ADR. In an Explainable AI perspective, we explore simple classification techniques such as Decision Trees and Classification Rules because they provide human-readable models, which explain the classification itself, but may also provide elements of explanation for molecular mechanisms behind ADRs. In summary, (1) we mine a knowledge graph for features; (2) we train classifiers at distinguishing, on the basis of extracted features, drugs associated or not with two commonly monitored ADRs: drug-induced liver injuries (DILI) and severe cutaneous adverse reactions (SCAR); (3) we isolate features that are both efficient in reproducing expert classifications and interpretable by experts (i.e., Gene Ontology terms, drug targets, or pathway names); and (4) we manually evaluate in a mini-study how they may be explanatory. Results Extracted features reproduce with a good fidelity classifications of drugs causative or not for DILI and SCAR (Accuracy = 0.74 and 0.81, respectively). Experts fully agreed that 73% and 38% of the most discriminative features are possibly explanatory for DILI and SCAR, respectively; and partially agreed (2/3) for 90% and 77% of them. Conclusion Knowledge graphs provide sufficiently diverse features to enable simple and explainable models to distinguish between drugs that are causative or not for ADRs. In addition to explaining classifications, most discriminative features appear to be good candidates for investigating ADR mechanisms further. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-021-01518-6.

Published
2022
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44. Climate-related risks in financial assets

Author

Emanuele Campiglio, Louis Daumas, Pierre Monnin, Adrian von Jagow, Campiglio, Emanuele, Daumas, Loui, Monnin, Pierre, and von Jagow, Adrian

Subjects
Economics and Econometrics, 502042 Umweltökonomie, 502042 Environmental economics, asset pricing, climate change, climate-related financial risks, financial stability, low-carbon transition
Abstract

The financial risks and potential systemic impacts induced by climate change and the transition to a low-carbon economy have become a central issue for both financial investors and their regulators. In this article, we develop a critical review of the empirical and theoretical literature concerning the impact of climate-related risks on the price of financial assets. We first present the theoretical links between asset pricing and climate-related risks and develop a theory of how climate risk drivers transmit costs to firms and lead to asset price changes. We then discuss studies looking at past climate-related events, which show that both climate physical impacts and transition dynamics can trigger a revaluation of financial assets through multiple direct and indirect channels. Finally, we review the emerging literature that uses forward-looking methodologies to estimate future climate-related asset price changes, which suggests that climate financial risks can indeed have significant implications on financial stability.

Published
2022

46. Proceedings of the 1st International Conference on Algebras, Graphs and Ordered Sets (ALGOS 2020)

Author

Miguel Couceiro, Pierre Monnin, Amedeo Napoli, Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Miguel Couceiro, Pierre Monnin, Amedeo Napoli, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO]Computer Science [cs], [MATH]Mathematics [math]
Abstract

International audience; Originating in arithmetics and logic, the theory of ordered sets is now a field of combinatorics that is intimately linked to graph theory, universal algebra and multiple-valued logic, and that has a wide range of classical applications such as formal calculus, classification, decision aid and social choice.This international conference “Algebras, graphs and ordered set” (ALGOS) brings together specialists in the theory of graphs, relational structures and ordered sets, topics that are omnipresent in artificial intelligence and in knowledge discovery, and with concrete applications in biomedical sciences, security, social networks and e-learning systems. One of the goals of this event is to provide a common ground for mathematicians and computer scientists to meet, to present their latest results, and to discuss original applications in related scientific fields. On this basis, we hope for fruitful exchanges that can motivate multidisciplinary projects.The first edition of ALgebras, Graphs and Ordered Sets (ALGOS 2020) has a particular motivation, namely, an opportunity to honour Maurice Pouzet on his 75th birthday! For this reason, we have particularly welcomed submissions in areas related to Maurice’s many scientific interests:• Lattices and ordered sets• Combinatorics and graph theory• Set theory and theory of relations• Universal algebra and multiple valued logic• Applications: formal calculus, knowledge discovery, biomedical sciences, decision aid and social choice, security, social networks, web semantics...

Published
2020

47. Climate Transition Risk Metrics: Understanding Convergence and Divergence across Firms and Providers

Author

Chiara Colesanti Senni, Julia Anna Bingler, and Pierre Monnin

Subjects
Corporate finance, Actuarial science, Climate risk, Financial risk, Financial market, Metric (unit), Asset (economics), Business, Risk assessment, Financial market participants
Abstract

Climate risks are now fully recognized as financial risks by asset managers, investors, central banks, and financial supervisors. Against this background, a rapidly growing number of market participants and financial authorities are exploring which metrics to use to capture climate risks, as well as to what extent the use of different metrics delivers heterogeneous results. To shed a light on these questions, we analyse a sample of 69 transition risk metrics delivered by 9 different climate transition risk providers and covering the 1,500 firms of the MSCI World index. Our findings show that convergence between metrics is significantly higher for the firms most exposed to transition risk. We also show that metrics with similar scenarios (i.e. horizon, temperature target and transition paths) tend to deliver more coherent risk assessments. Turning to the variables that might drive the outcome of the risk assessment, we find evidence that variables on metric’s assumptions and scenario’s characteristics are associated with changes in the estimated firms’ transition risk. Our findings bear important implications for policy making and research. First, climate transition risk metrics, if applied by the majority of financial market participants in their risk assessment, might translate into relatively coherent market pricing signals for least and most exposed firms. Second, it would help the correct interpretation of metrics in financial markets if supervisory authorities defined a joint baseline approach to ensure basic comparability of disclosed metrics, and asked for detailed assumption documentations alongside the metrics. Third, researchers should start to justify the use of the specific climate risk metrics and interpret their findings in the light of the metric assumptions.

Published
2021
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48. Knowledge-Based Matching of $n$-ary Tuples

Author

Adrien Coulet, Pierre Monnin, Miguel Couceiro, Amedeo Napoli, Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Supported by the PractiKPharma project, founded by the French National Research Agency (ANR) under Grant ANR15-CE23-0028, by the IDEX 'Lorraine Université d’Excellence' (15-IDEX-0004), and by the Snowball Inria Associate Team., Mehwish Alam, Tanya Braun, Bruno Yun, Snowball Inria Associate Team, ANR-15-CE23-0028,PractiKPharma,Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique(2015), ANR-15-IDEX-0004,LUE,Isite LUE(2015), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Computer and information sciences, Matching (statistics), Computer Science - Artificial Intelligence, Computer science, Ontology (information science), Domain (software engineering), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, Matching, Semantic Web, 030304 developmental biology, Alignment, 0303 health sciences, Information retrieval, business.industry, Ontology, Artificial Intelligence (cs.AI), Knowledge base, n-ary Tuple, Software agent, 030220 oncology & carcinogenesis, Domain knowledge, Order, Tuple, business
Abstract

International audience; An increasing number of data and knowledge sources are accessible by human and software agents in the expanding Semantic Web. Sources may differ in granularity or completeness, and thus be complementary. Consequently, they should be reconciled in order to unlock the full potential of their conjoint knowledge. In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar. This task is challenging since knowledge units can be heterogeneously represented in sources (e.g., in terms of vocabularies). In this paper, we focus on matching $n$-ary tuples in a knowledge base with a rule-based methodology. To alleviate heterogeneity issues, we rely on domain knowledge expressed by ontologies. We tested our method on the biomedical domain of pharmacogenomics by searching alignments among 50,435 n-ary tuples from four different real-world sources. Results highlight noteworthy agreements and particularities within and across sources.

Published
2020
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49. Climate Financial Risks: Assessing Convergence, Exploring Diversity

Author

Pierre Monnin, Chiara Colesanti Senni, and Julia Anna Bingler

Subjects
Corporate bond, Actuarial science, business.industry, Financial risk, Climate risk, Portfolio, Context (language use), Asset (economics), business, Risk assessment, Risk management
Abstract

Climate risks are now fully recognized as financial risks by asset managers, investors, central banks, and financial supervisors. As a result, the integration of climate risk metrics into risk management processes is moving up agendas worldwide. In that context, a rapidly growing number of market participants and financial authorities are exploring which metrics to use to capture climate risks, and to what extent the use of different metrics delivers heterogeneous results. This discussion note takes a first step in analyzing the convergence in assessments of climate-related transition risks across metrics providers, based on the ECB corporate bond portfolio. Our findings show that firms’ risk assessments across metrics are fairly heterogeneous but tend to converge on which firms are most and least exposed to transition risks. We also show that the temperature targets and time horizons underlying the metrics matter, although moderately, for the assessment of firms’ risk exposure and that providers using similar methodologies tend to deliver more convergent assessments. Our findings contribute to the growing recognition that asset managers, investors, central banks and financial supervisors can and should use available metrics to better integrate climate risks into risk management and financial supervision.

Published
2020
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50. Monetary policy, macroprudential regulation and inequality

Author

Prakash Loungani, Kees Koedijk, Pierre Monnin, Department of Finance, and Research Group: Finance

Subjects
Economics and Econometrics, 050208 finance, Inequality, media_common.quotation_subject, 05 social sciences, Monetary policy, Monetary economics, Macroprudential regulation, 0502 economics and business, Economics, Special section, 050207 economics, Finance, media_common
Published
2018
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