6.1.1 ARPEnTAge

• Name: Analyse de Régularités dans les Paysages : Environnement, Territoires, Agronomie
• Keywords: Stochastic process, Hidden Markov Models
• Functional Description: ARPEnTAge is a software based on stochastic models (HMM2 and Markov Field) for analyzing spatio-temporal data-bases. ARPEnTAge is built on top of the CarottAge system to fully take into account the spatial dimension of input sequences. It takes as input an array of discrete data in which the columns contain the annual land-uses and the rows are regularly spaced locations of the studied landscape. It performs a Time-Space clustering of a landscape based on its time dynamic Land Uses (LUS). Displaying tools and the generation of Time-dominant shape files have also been defined.
• URL: http://carottage.loria.fr/index_in_english.html
• Contact: Jean-François Mari
• Partner: INRA

6.1.2 CarottAge

• Keywords: Stochastic process, Hidden Markov Models
• Functional Description: The system CarottAge is based on Hidden Markov Models of second order and provides a non supervised temporal clustering algorithm for data mining and a synthetic representation of temporal and spatial data. CarottAge is currently used by INRA researchers interested in mining the changes in territories related to the loss of biodiversity (projects ANR BiodivAgrim and ACI Ecoger) and/or water contamination. CarottAge is also used for mining hydromorphological data. Actually a comparison was performed with three other algorithms classically used for the delineation of river continuum and CarottAge proved to give very interesting results for that purpose.
• URL: http://carottage.loria.fr/index_in_english.html
• Contacts: Florence Le Ber, Jean-François Mari
• Participants: Florence Le Ber, Jean-François Mari
• Partner: INRA

6.1.3 CORON

• Keywords: Data mining, Closed itemset, Frequent itemset, Generator, Association rule, Rare itemset
• Functional Description:

  The Coron platform is a KDD toolkit organized around three main components: (1) Coron-base, (2) AssRuleX, and (3) pre- and post-processing modules.

  The Coron-base component includes a complete collection of data mining algorithms for extracting itemsets such as frequent itemsets, closed itemsets, generators and rare itemsets. In this collection we can find APriori, Close, Pascal, Eclat, Charm, and, as well, original algorithms such as ZART, Snow, Touch, and Talky-G. AssRuleX generates different sets of association rules (from itemsets), such as minimal non-redundant association rules, generic basis, and informative basis. In addition, the Coron system supports the whole life-cycle of a data mining task and proposes modules for cleaning the input dataset, and for reducing its size if necessary.

• URL: http://coron.loria.fr/site/index.php
• Authors: Laszlo Szathmary, Florent Marcuola, Mehdi Kaytoue, Amedeo Napoli
• Contact: Amedeo Napoli
• Participants: Adrien Coulet, Aleksey Buzmakov, Amedeo Napoli, Florent Marcuola, Jérémie Bourseau, Laszlo Szathmary, Mehdi Kaytoue, Victor Codocedo, Yannick Toussaint

6.2.1 FixOut: an ensemble approach to build fairer models

• Name: FixOut: an ensemble approach to build fairer models
• Keywords: explanations in artificial intelligence, XAI, Lime explanations, fairness metrics, Shapley values, feature importance, feature dropout, ensemble classifier.
• Functional Description: The FixOut system is aimed at evaluating fairness of processes and models in artificial intelligence. FixOut is a human-centered and model-agnostic framework, using any explanation method (based on feature importance) to assess the reliance of a classifier on sensitive features. Given a pre-trained classifier, FixOut first checks whether it relies on user defined sensitive features. If it does, then FixOut employs feature dropout to produce a pool of simplified classifiers that are then aggregated into an ensemble classifier. Empirical results using different classifiers on several real-world datasets show a consistent improvement in terms of widely used fairness metrics, decreased reliance on sensitive features, and without compromising the accuracy of classifiers.
• URL: https://fixout.loria.fr/
• Contact: Miguel Couceiro

6.2.2 LatViz: Visualization of Concept Lattices

• Name: LatViz: Visualization of Concept Lattices.
• Keywords: Formal Concept Analysis, pattern structures, concept lattice, implications, visualization.
• Functional Description: LatViz is a tool allowing the construction, the display and the exploration of concept lattices. LatViz proposes some noticeable improvements over existing tools and introduces various functionalities focusing on interaction with experts, such as visualization of pattern structures for dealing with complex non-binary data, AOC-poset which is composed of the core elements of the lattice, concept annotations, filtering based on various criteria and a visualization of implications 67. This way the user can effectively perform interactive exploratory knowledge discovery as often needed in knowledge engineering. The LatViz platform can be associated with the Coron platform and extends its visualization capabilities (see http://coron.loria.fr). Recall that the Coron platform includes a complete collection of data mining algorithms for extracting itemsets and association rules.
• URL: http://latviz.loria.fr/
• Contact: Amedeo Napoli

6.2.3 OrphaMine: Data Mining Platform for Orphan Diseases

• Name: OrphaMine: A Data Mining Platform for Orphan Diseases
• Keywords: bioinformatics, data mining, biology, health, data visualization, drug development.
• Functional Description: The OrphaMine platform enables visualization, data integration and in-depth analytics in the domain of “orphan diseases”, where data is extracted from the OrphaData ontology (https://www.orpha.net/consor/cgi-bin/index.php). At present, we aim at building a true collaborative portal that will serve different actors: (i) a general visualization of OrphaData data for physicians working, maintaining and developing this knowledge database about orphan diseases, (ii) the integration of analytics (data mining) algorithms developed by the different academic actors, (iii) the use of these algorithms to improve our general knowledge of rare diseases.
• URL: http://orphamine.inria.fr/
• Contact: Miguel Couceiro, Laureline Nevin, Amedeo Napoli.

6.2.4 Siren: Interactive and Visual Redescription Mining

• Name: Siren: Interactive and Visual Redescription Mining
• Keywords: redescription mining, interactivity, visualization.
• Functional Description: Siren is a tool for interactive mining and visualization of redescriptions. Redescription mining aims to find distinct common characterizations of the same objects and, vice versa, to identify sets of objects that admit multiple shared descriptions. The goal is to provide domain experts with a tool allowing them to tackle their research questions using redescription mining. Merely being able to find redescriptions is not enough. The expert must also be able to understand the redescriptions found, adjust them to better match his domain knowledge and test alternative hypotheses with them, for instance. Thus, Siren allows mining redescriptions in an anytime fashion through efficient, distributed mining, to examine the results in various linked visualizations, to interact with the results either directly or via the visualizations, and to guide the mining algorithm toward specific redescriptions. New features, such as a visualization of the contribution of individual literals in the queries and the simplification of queries as a post-processing, have been added to the tool.
• URL: http://siren.gforge.inria.fr/main/
• Contact: Esther Galbrun

7.1.1 FCA, Pattern Mining, MDL, and Hybrid Mining

Advances in data and knowledge engineering have emphasized the needs for pattern mining tools working on complex data. FCA, which usually applies to binary data-tables, can be adapted to work on more complex data. In this way, we have contributed to some main extensions of FCA, namely Pattern Structures, Relational Concept Analysis and application of the “Minimum Description Length” (MDL) within FCA. Pattern Structures (PS 71, 72) allow building a concept lattice from complex data, e.g. numbers, sequences, trees and graphs. Relational Concept Analysis (RCA 77) is able to analyze objects described both by binary and relational attributes and can play an important role in text classification and text mining. Many developments were carried out in pattern mining and FCA for improving data mining algorithms and their applicability, and for solving some specific problems such as information retrieval, discovery of functional dependencies and biclustering. Reusing ideas form subgroup discovery, we have initiated a whole line of research on the covering of the pattern spaces based on the “Minimum Description Length” (MDL) principle and we are working on the adaptation of MDL within the FCA framework 33, 35. Moreover, we also investigated the discovery of functional dependencies 20, the mining of RDF data, and the production of explanations in an FCA framework 21.

In addition, we are working on the design of hybrid mining methods, based on mining methods able to deal with symbolic and numerical data in parallel. In the context of the GEENAGE project, we are interested in the identification, in biomedical data, of biomarkers that are predictive of the development of diseases in the elderly population 21. Actually, the data are issued from a preceding study on metabolomic data for the detection of diabetes of type 2 12. The problem can be viewed as a classification problem where features which are predictive of a class should be identified. This leads us to study the notions of prediction and discrimination in classification problems, and as well of hybrid mining. Combining subsymbolic machine learning methods such as random forests, neural networks, and SVM, then multicriteria decision making methods (Pareto fronts), and pattern mining methods (including FCA), we developed a hybrid mining approach for selecting the features which are the most predictive and/or discriminant. Then the selected features are organized within a concept lattice to be presented to the analyst together with the reasons for their selection. The concept lattice facilitates the understanding of the feature selection. As such, this approach can also be seen as an explicable mining method, where the output includes the reasons for which features are selected in terms of prediction and discrimination.

Besides symbolic knowledge discovery approaches, we also experimented “Bayesian deep learning” approaches in image recognition to take into account the uncertainty attached to such images and to the process classifying these images. We are dealing with astronomical images where galaxies should be recognized. First experiments trying to understand the action of feature dropout were carried out in the context of the AstroDeep project and will be consolidated the present year 42.

Redescription mining is another pattern mining method developed in the team, which aims at finding distinct common characterizations of the same objects and, reciprocally, at identifying sets of objects having multiple shared descriptions 70. This is motivated by the idea that in scientific investigations data often have different nature, and may originate from distinct sources or terminologies. Following this way, we applied redescription mining for analyzing and mining RDF data in the web of data with the objective of discovering definitions of concepts 74, 75. Redescription mining is well adapted to the task as a definition is naturally based on two sides of an equation, a left-hand side and a right-hand side. Moreover, we are studying how redescription mining can be used in the discovery of link keys over two RDF datasets.

KDD in Life Sciences.

In the ANR project PractiKPharma1 three main research activities were developed, namely the validation of pharmacogenomic (PGX) knowledge, the mining of Electronic Health Records (EHRs), and biological data aggregation for knowledge discovery. The validation of PGX knowledge is based on the PGxO ontology2 which is instantiated by data from various provenance, e.g. biomedical databases, literature, and EHRs 51. Moreover, we defined and applied a set of so-called “reconciliation rules” that compare and align whenever possible knowledge units of different provenance 38.

We also studied how cross-corpus training may guide relation extraction from texts, and “transfer learning” techniques, i.e. how large annotated corpora developed for alternative tasks may improve the performance of text mining in biomedical texts, for which only a few annotated resources are available. Then for improving the quality of knowledge units in PGxLOD originating from biomedical literature, we manually built an annotated corpus named PGxCorpus 13. Thanks to this corpus, the first one attached to PGx, we were able to train supervised ML approaches to discover PGx knowledge units from texts with a high quality, in particular using the transformer architecture BERT 61.

Furthermore, the “Fighting Heart Failure” project aims at identifying and describing relevant bio-profiles of patients suffering from heart failure. We worked on the clustering of patients, by defining multidimensional similarity measures based on graph aggregation, namely “Unsupervised Extremely Randomized Trees” 30, 10.

Text Mining.

The research work in text mining is based on two main lines. The first research subject is related to the study of discourse and argumentation structures in a text based on tree mining and redescription mining 32, while the second research work is related to the mining of Pubmed abstracts about rare diseases. In the first research line, we investigate the similarities existing between discourse and argumentation structures by aligning subtrees in a corpus where texts are annotated. Based on tree mining and redescription mining, we are able to show that the structures underlying discourse and argumentation can be (partially) aligned.

In the second research line, the objective is to discover features related to rare diseases, e.g. symptoms, related diseases, treatments, and possible disease evolution or variations. The texts to be analyzed are from Pubmed, i.e. a platform collecting millions of publications in the medical domain. This research project aims at developing new methods and tools for supporting knowledge discovery in textual data by combining methods from Natural Language Processing (NLP) and Knowledge Discovery in Databases (KDD).

Knowledge Systems and Web of Data.

The increase of data published in RDF format leads to challenging aspects regarding quality assessment and data exploration 66. For example, the discovery of implication rules is used to automatically detect possible completions of RDF data and to provide definitions. In this same way, we are investigating the characterization of functional dependencies and of similarity dependencies within FCA and pattern structures 20. This is fully related to the ANR Elker project where we study “link keys” 6. Link keys are more complex than functional dependencies in databases and they raise new problems to solve, e.g. they may be used to identify the same objects in different RDF datasets.

For example, link keys may identify the same book or article in different bibliographical data sources, where a link key is a statement of the form: $\{\langle \mathrm{𝚊𝚞𝚝𝚎𝚞𝚛},\mathrm{𝚌𝚛𝚎𝚊𝚝𝚘𝚛}\rangle ,\langle \mathrm{𝚝𝚒𝚝𝚛𝚎},\mathrm{𝚝𝚒𝚝𝚕𝚎}\rangle \}linkkey\langle \mathrm{𝙻𝚒𝚟𝚛𝚎},\mathrm{𝙱𝚘𝚘𝚔}\rangle$ stating that whenever an instance of the class Livre has the same values for properties auteur and titre as an instance of class Book has for properties creator and title, then they denote the same entity.

Currently we are investigating the connections with FCA and pattern structures 16. Moreover, we are also interested in the possible connections with Relational Concept Analysis and redescription mining. We would like to study the formulation of the discovery of link keys in reusing and extending some construction developed in redescription mining. In practice, redescription mining aims at constructing pairs of descriptions, i.e., pairs of logical statements, one for each of two datasets, such that their support sets, i.e., the sets of objects that satisfy each statements of a pair, respectively, are most similar, as measured for example by their Jaccard index. This is in full correspondence with the discovery of link keys in two RDF datasets.

8.1.1 FP7 & H2020 Projects

8.2.1 ANR

8.2.2 Inria Project Labs, Exploratory Research Actions, and Technological Development Actions

Participants: Guilherme Alves Da Silva, Miguel Couceiro, Nyoman Juniarta, Amedeo Napoli, Laureline Nevin, Georgios Zervakis.

• HyAIAI (IPL 2019-2022)

  Recent progress in Machine Learning (ML) and especially in Deep Learning has made ML present and prominent in a wide range of applications. However, current and efficient ML approaches rely on complex numerical models. Then, the decisions which are proposed may be accurate but cannot be easily explained to the layman, especially in some cases where complex and human-oriented decisions should be made, e.g. to get a loan or not, to obtain a chosen enrollment at university. The objectives of the HyAIAI IPL are to study the problem of making ML methods more interpretable. For that, we will design hybrid ML approaches that combine state of the art subsymbolic models (e.g. neural networks) with explainable symbolic models (e.g. pattern mining). More precisely, one goal is to integrate high level domain constraints into ML models, to provide model designers information on ill-performing parts of the model, and to give the layman/practitioner understandable explanations on the results of the ML model.

  The HyAIAI IPL project involves seven Inria Teams, namely Lacodam in Rennes (project leader), Magnet and SequeL in Lille, Multispeech and Orpailleur in Nancy, and TAU in Saclay.

• Ordem (ADT 2019-2020) One of the outputs of the former Hybride ANR project was the Orphamine system which aims at information retrieval and diagnosis aid in the domain of “rare diseases” (http://orphamine.loria.fr/). The Orphamine system is based on domain knowledge, and in particular on medical ontologies such as ORDO (“Orphanet Rare Diseases Ontology”) and HPO (“Human Phenotype Ontology”). The objective of the “Ordem” ADT is to update Orphamine, in making the system more accessible and more open. This mainly requires software development for building the connections with domain knowledge, graph mining methods for retrieving relevant units in knowledge graphs, actual visualization tools, pattern mining, statistical decision tools for decision making (in particular log-linear models), and as well text mining tools for analyzing expert queries and medical literature about rare diseases. A large part of this development was carried out until during the last year, for making the Orphamine system more robust. The new version of the Orphamine system will be publicly accessible through a web interface in the next future.
• HyGraMi (PRE Inria 2018-2020) The so called “projet de recherche exploratoire” (PRE) HyGraMi for “Hybrid Graph Mining for the Design of New Antibacterials” is about the fight against resistance of bacteria to antibiotics. The objective of HyGraMi is to design a hybrid data mining system for discovering new antibacterial agents. This system should rely on a combination of numeric and symbolic classifiers, that will be guided by expert domain knowledge. The analysis and classification of the chemical structures is based on an interaction between symbolic methods e.g. graph mining techniques, and numerical supervised classifiers based on exact and approximate matching 54.

9.1.1 Scientific Events: Organization

• Laurine Huber was co-chair in the scientific organization of the “6ième conférence conjointe Journées d'Études sur la Parole (JEP), Traitement Automatique des Langues Naturelles (TALN), et Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL)”, Nancy, June 2020 (https://jep-taln2020.loria.fr).
• Miguel Couceiro, Amedeo Napoli, and Pierre Monnin, were the General Chairs and the scientific organizers of the online workshop ALGOS 2020 dedicated to Maurice Pouzet for his 75th birthday, August 2020 49 (https://algos2020.loria.fr/).
• Amedeo Napoli was the co-Program Chair and scientific co-organizer of the 8th workshop FCA4AI co-located with ECAI 2020 in August 2020 50 (http://www.fca4ai.hse.ru/).

9.1.2 General chair, scientific chair

• Amedeo Napoli was co-Program chair of the special track “General Topics of Data Analysis” at AIST in 2020 (http://aistconf.org/).
• See also above items 2 and 3.

9.1.3 Member of Editorial Boards

• Editor in Journal of Multiple-Valued Logic and Soft Computing and International Journal on Information Technologies and Security (Miguel Couceiro).
• Miguel Couceiro was elected as a member of the IEEE Technical Committee on Multiple-Valued Logic for the 2018-2020 period.

9.1.4 Reviewer - reviewing activities

• The members of the Orpailleur team are involved in the program and steering committees of main Conferences (AAAI, ECAI, ECML-PKDD, ICDM, IJCAI, KDD, VLDB...), as members of editorial boards, and in the organization of journal special issues.

9.1.5 Invited talks

• Invited Talk (Online): “Knowledge Discovery and Knowledge Representation: A Formal Concept Analysis Perspective”, Amedeo Napoli, Center for Strategic Research, Saint Petersburg, Russia, November 2020.
• Invited Talk (Online): “Making ML models fairer through explanations: the case of LimeOut”, Miguel Couceiro, The 9th International Conference on Analysis of Images, Social Networks and Texts, Russia 18.

9.1.6 Teaching

• All the permanent members of the Orpailleur team are involved in teaching at all levels and mainly at Université de Lorraine. Actually, most of the members of the Orpailleur team are employed on “Université de Lorraine” positions.
• Responsibility of the 2nd year of the NLP Master's program in the IDMC, Université de Lorraine.
• Local coordination of the European Erasmus Mundus Master's program LCT (Language and Communication Technologies). The LCT Master’s program “Language and communication Technologies” (LCT) is designed to provide students with practice–oriented knowledge in computational and theoretical linguistics, natural language processing, and computer science, to meet the demands of industry and research in these rapidly growing areas. The LCT consortium includes 7 European Universities, i.e. Saarland, Lorraine, Trento, Malta, Groningen, Charles in Prague, Basque Country, and includes several partners, e.g., DFKI, IBM (Czech Rep.), VICOMTECH, Sony (Europe), IBM (Ireland), and Inria (France).
• Responsibility in teaching courses about Artificial Intelligence and Knowledge-Based Systems at TELECOM Nancy, a engineer school for graduation in computer science at Université de Lorraine.

9.1.7 Tutorials

• Online Tutorial: “A Smooth Introduction to FCA” (AN), Troisième édition des JRI-2020 UFR-SEA, Université Joseph Ki-Zerbo, Ouagadougou, Burkina Faso, September 2020 (AN).
• Online Tutorial: “FCA and Knowledge Discovery” (AN), Tutorial at the 25th International Conference on Conceptual Structures (ICCS), Bolzano, Italy, September 18, 2020 (https://iccs-conference.org/?page_id=538).

9.1.8 Supervision

• The members of the Orpailleur team are involved in student supervision, at all university levels, from under-graduate until post-graduate students, engineers, PhD, postdoc students.
• The permanent members of the Orpailleur team are also involved in HDR and thesis defenses, being thesis referees or thesis committee members.

• PhD in progress: Nacira Abbas, discovery of link keys in the web of data, started in 2018, Jérôme David (Inria Rhône-Alpes) and Amedeo Napoli.
• PhD in progress: Guilherme Alves, Fixout: a system for checking the fairness of knowledge discovery algorithms, started in 2018, Miguel Couceiro and Amedeo Napoli.
• PhD in progress: Laurine Huber, Analysis of Discourse and Text mining, started in 2018, Yannick Toussaint.
• PhD in progress: Tatiana Mahkalova, a study of pattern mining from an FCA perspective (MDL, numerical pattern mining), started in 2017, Sergei O. Kuznetsov (HSE Moscow) and Amedeo Napoli.
• PhD in progress: Esteban Marquer, Reasoning over Data: analogy based and transfer learning to improve machine learning, started in 2020, Miguel Couceiro and Alain Gély.
• PhD defended: Pierre Monnin, Data mining and ontology design in pharmacogenomics, started in 2016, Adrien Coulet and Amedeo Napoli.
• PhD in progress: Claire Theobald, Discovering and understanding images of galaxies in astronomical data, started in 2020, Miguel Couceiro and Frédéric Pennarth.
• PhD in progress: Laura Zanella, Text mining and Deep Learning, started in 2019, Yannick Toussaint.
• PhD in progress: Georgios Zervakis, Hybrid data mining systems, started in 2019, Miguel Couceiro and Emmanuel Vincent (Multispeech).

International journals

• 6 articleManuelM. Atencia, JérômeJ. David, JérômeJ. Euzenat, AmedeoA. Napoli and JérémyJ. Vizzini. Link key candidate extraction with relational concept analysisDiscrete Applied Mathematics2732020, 2-20
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• 7 articleQuentinQ. Brabant, MiguelM. Couceiro, DidierD. Dubois, HenriH. Prade and AgnèsA. Rico. Learning rule sets and Sugeno integrals for monotonic classification problemsFuzzy Sets and Systems401December 2020, 4-37
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• 8 article MiguelM. Couceiro, LucienL. Haddad and VictorV. Lagerkvist. A Survey on the Fine-grained Complexity of Constraint Satisfaction Problems Based on Partial Polymorphisms Journal of Multiple-Valued Logic and Soft Computing 2020
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• 9 articleMiguelM. Couceiro, ErkkoE. Lehtonen, PierreP. Mercuriali and RomainR. Péchoux. On the efficiency of normal form systems for representing Boolean functionsTheoretical Computer Science813April 2020, 341-361
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• 10 articleKevinK. Dalleau, MiguelM. Couceiro and MalikaM. Smaïl-Tabbone. Unsupervised Extra Trees: a stochastic approach to compute similarities in heterogeneous data.International Journal of Data Science and AnalyticsLecture Notes in Computer Science12080March 2020, 132-144
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• 11 article ElenaE. Fimmel, Christian JC. Michel, FrançoisF. Pirot, Jean-SébastienJ.-S. Sereni, MartinM. Starman and LutzL. Strüngmann. The relation between $k$-circularity and circularity of codes' Bulletin of Mathematical Biology 82 8 August 2020
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• 12 articleDhouhaD. Grissa, BlandineB. Comte, MélanieM. Petera, EstelleE. Pujos-Guillot and AmedeoA. Napoli. A hybrid and exploratory approach to knowledge discovery in metabolomic dataDiscrete Applied Mathematics273SI2020, 103-116
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• 13 article JoëlJ. Legrand, RomainR. Gogdemir, CedricC. Bousquet, KevinK. Dalleau, Marie-DominiqueM.-D. Devignes, WilliamW. Digan, Chia-JuC.-J. Lee, Ndeye-CoumbaN.-C. Ndiaye, NadineN. Petitpain, PatriceP. Ringot, MalikaM. Smaïl-Tabbone, YannickY. Toussaint and AdrienA. Coulet. PGxCorpus, a manually annotated corpus for pharmacogenomics Scientific Data 7 3 January 2020
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• 14 article FrançoisF. Pirot, Jean-SébastienJ.-S. Sereni and RisteR. Škrekovski. Variations on the Petersen colouring conjecture The Electronic Journal of Combinatorics 27 1 January 2020
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• 15 article LaetitiaL. Ricci, JonathanJ. Epstein, AnneA. Buisson, CorinneC. Devos, YannickY. Toussaint, LaurentL. Peyrin Biroulet and FrancisF. Guillemin. Flare-IBD: development and validation of a questionnaire based on patients’ messages on an internet forum for early detection of flare in inflammatory bowel disease: study protocol BMJ Open 10 7 October 2020
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International peer-reviewed conferences

• 16 inproceedings NaciraN. Abbas, JérômeJ. David and AmedeoA. Napoli. Discovery of Link Keys in RDF Data Based on Pattern Structures: Preliminary Steps CLA 2020 - The 15th International Conference on Concept Lattices and Their Applications Proceedings of the 15th International Conference on Concept Lattices and Their Applications Tallinn / Virtual, Estonia June 2020
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• 17 inproceedingsAhmadA. Abboud, AbdelkaderA. Lahmadi, MichaelM. Rusinowitch, MiguelM. Couceiro, AdelA. Bouhoula and MondherM. Ayadi. Double Mask: An efficient rule encoding for Software Defined NetworkingICIN 2020 - 23rd Conference on Innovation in Clouds, Internet and Networks and Workshops23rd Conference on Innovation in Clouds, Internet and Networks and Workshops, {ICIN} 2020, Paris, France, February 24-27, 2020Paris, France2020, 186--193
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• 18 inproceedings GuilhermeG. Alves, VaishnaviV. Bhargava, MiguelM. Couceiro and AmedeoA. Napoli. Making ML models fairer through explanations: the case of LimeOut Lecture Notes in Computer Science, Analysis of Images, Social Networks, and Texts 2020 9th International Conference on Analysis of Images, Social Networks, and Texts 2020 (AIST 2020) Lecture Notes in Computer Science, Analysis of Images, Social Networks, and Texts 2020 Moscow, Russia October 2020
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• 19 inproceedings GuilhermeG. Alves, MiguelM. Couceiro and AmedeoA. Napoli. Sélection de mesures de similarité pour les données catégorielles EGC 2020 - 20ème édition de la conférence Extraction et Gestion des Connaissances Bruxelles, Belgium January 2020
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• 20 inproceedingsJaumeJ. Baixeries, VictorV. Codocedo, MehdiM. Kaytoue and AmedeoA. Napoli. Building a Representation Context Based on Attribute Exploration AlgorithmsFCA4AI 2020 - 8th International Workshop "What can {FCA} do for Artificial Intelligence?"CEUR Workshop ProceedingsProceedings of the 8th International Workshop "What can {FCA} do for Artificial Intelligence?" (FCA4AI 2020) co-located with ECAI 2020,2729Santiago de Compostela/Virtual, SpainAugust 2020, 141--152
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• 21 inproceedings AlexandreA. Bazin, MiguelM. Couceiro, Marie-DominiqueM.-D. Devignes and AmedeoA. Napoli. Explaining Multicriteria Decision Making with Formal Concept Analysis Concept Lattices and Applications 2020 2668 CEUR Workshop Proceedings Tallinn, Estonia June 2020
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• 22 inproceedingsVaishnaviV. Bhargava, MiguelM. Couceiro and AmedeoA. Napoli. LimeOut: An Ensemble Approach To Improve Process FairnessCommunications in Computer and Information ScienceECML-PKDD International Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2020)1323Communications in Computer and Information ScienceGhent, Belgium2020, 475-491
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• 23 inproceedings AlexandreA. Blansché and LydiaL. Boudjeloud-Assala. Biclustering itératif par une approche évolutionnaire EGC 2020 Extraction et Gestion des Connaissances RNTI-E-36 Brussels, Belgium January 2020
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• 24 inproceedings MathildeM. Boltenhagen, BenjaminB. Chetioui and LaurineL. Huber. An Alignment Cost-Based Classification of Log Traces Using Machine-Learning ML4PM2020 - First International Workshop on Leveraging Machine Learning in Process Mining Padua/ Virtual, Italy October 2020
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• 25 inproceedings GuillaumeG. Bonfante and MiguelM. Couceiro. Termination of graph rewriting systems through language theory 1st international conference "Algebras, graphs and ordered sets" (Algos 2020) Proceedings of the 1st International Conference on Algebras, Graphs and Ordered Sets (ALGOS 2020) Nancy, France https://algos2020.loria.fr/ August 2020
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• 26 inproceedings MiguelM. Couceiro and MichelM. Grabisch. Monotonic computation rules for nonassociative calculus 1st international conference on ALgebras, Graphs and Ordered Sets Proceedings of the 1st International Conference on Algebras, Graphs and Ordered Sets (ALGOS 2020) Nancy, France https://algos2020.loria.fr/ May 2020
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• 27 inproceedings MiguelM. Couceiro and ErkkoE. Lehtonen. Linearly definable classes of Boolean functions 1st international conference "Algebras, graphs and ordered sets" (Algos 2020) Proceedings of the 1st International Conference on Algebras, Graphs and Ordered Sets (ALGOS 2020) Nancy, France https://algos2020.loria.fr/ August 2020
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• 28 inproceedingsMiguelM. Couceiro, ErkkoE. Lehtonen, LaurentL. Miclet, HenriH. Prade and GillesG. Richard. When nominal analogical proportions do not failSpringer Lecture Notes in Computer Science (LNCS)SUM 2020 - 14th International Conference on Scalable Uncertainty Management12322Lecture Notes in Computer ScienceBolzano / Virtual, ItalySeptember 2020, 68-83
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• 29 inproceedings LaureL. Crochepierre, LydiaL. Boudjeloud-Assala and VincentV. Barbesant. Interpretable Dimensionally-Consistent Feature Extraction from Electrical Network Sensors European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ECML/PKDD'20 Gand (Virtual Conference), Belgium September 2020
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• 30 inproceedingsKevinK. Dalleau, MiguelM. Couceiro and MalikaM. Smaïl-Tabbone. Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in GraphsIDA 2020 -18th International Symposium on Intelligent Data Analysis12080Lecture Notes in Computer ScienceKonstanz / Virtual, Germanyhttps://ida2020.org/June 2020, 132-144
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• 31 inproceedingsAlainA. Gély, MiguelM. Couceiro, LaurentL. Miclet and AmedeoA. Napoli. Steps in the Representation of Concept Lattices and Median GraphsCLA 2020 - 15th International Conference on Concept Lattices and Their ApplicationsProceedings of The 15th International Conference on Concept Lattices and Their ApplicationsTallinn, Estoniahttps://cs.ttu.ee/events/cla2020/June 2020, 1-11
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• 32 inproceedings LaurineL. Huber, JustineJ. Reynaud, MathildeM. Dargnat and YannickY. Toussaint. AOC-Poset on discourse and argumentation subgraphs: what can we learn on their dependencies? Concept Lattice and their Applications 2668 Proceedings of the Fifthteenth International Conference on Concept Lattices and Their Applications Tallinn, Estonia June 2020
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• 33 inproceedings TatianaT. Makhalova, Sergei O.S. Kuznetsov and AmedeoA. Napoli. Adaptive Multi-model Approaches to Pattern Set Mining European Starting AI Researchers' Symposium 2020 CEUR 2655 Proceedings of the 9th European Starting AI Researchers' Symposium 2020 co-located with 24th European Conference on Artificial Intelligence ECAI 2020 Santiago Compostela, Spain August 2020
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• 34 inproceedings TatianaT. Makhalova, Sergei O.S. Kuznetsov and AmedeoA. Napoli. Closure Structure: a Deeper Insight The 8th International Workshop "What can FCA do for Artificial Intelligence?" CEUR 2729 Proceedings of the 8th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2020) co-located with 24th European Conference on Artificial Intelligence (ECAI 2020) Santiago de Compostela, Spain August 2020
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• 35 inproceedings TatianaT. Makhalova, Sergei O.S. Kuznetsov and AmedeoA. Napoli. Gradual Discovery with Closure Structure of a Concept Lattice The 15th International Conference on Concept Lattices and Their Applications CEUR 2668 Proceedings of the 15th International Conference on Concept Lattices and Their Applications Tallinn, Estonia October 2020
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• 36 inproceedings EstebanE. Marquer, AjinkyaA. Kulkarni and MiguelM. Couceiro. Embedding Formal Contexts Using Unordered Composition FCA4AI - 8th International Workshop "What can FCA do for Artificial Intelligence?" (colocated wit ECAI2020) Santiago de Compostela, Spain 2020
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• 37 inproceedings PierreP. Monnin, EmmanuelE. Bresso, MiguelM. Couceiro, MalikaM. Smaïl-Tabbone, AmedeoA. Napoli and AdrienA. Coulet. Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study 1st international conference "Algebras, graphs and ordered sets" (Algos 2020) Proceedings of the 1st International Conference on Algebras, Graphs and Ordered Sets (ALGOS 2020) Nancy, France https://algos2020.loria.fr/ August 2020
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• 38 inproceedingsPierreP. Monnin, MiguelM. Couceiro, AmedeoA. Napoli and AdrienA. Coulet. Knowledge-Based Matching of n-ary TuplesOntologies and Concepts in Mind and Machine - 25th International Conference on Conceptual Structures, ICCS 2020, Bolzano, Italy, September 18–20, 2020, Proceedings25th International Conference on Conceptual Structures, ICCS 2020Lecture Notes in Computer ScienceOntologies and Concepts in Mind and Machine - 25th International Conference on Conceptual Structures, ICCS 2020, Bolzano, Italy, September 18–20, 2020, Proceedings12277Bolzano, Italy2020, 48-56
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• 39 inproceedings AmedeoA. Napoli. FCA and Knowledge Discovery (Tutorial) ICCS 2020 - 25th International Conference on Conceptual Structures Lecture Notes in Computer Science Proceedings: Ontologies and Concepts in Mind and Machine - 25th International Conference on Conceptual Structures, ICCS 2020, Bolzano, Italy, September 18-20, 2020. 12277 Bolzano/ Virtual, Italy September 2020
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• 40 inproceedingsFrédéricF. Pennerath, PanagiotisP. Mandros and JillesJ. Vreeken. Discovering Approximate Functional Dependencies using Smoothed Mutual InformationKDD 2020 - 26th ACM SIGKDD Conference on Knowledge Discovery and Data MiningKDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningSan Diego / Virtual, United StatesAugust 2020, 1254 -- 1264
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National peer-reviewed Conferences

Conferences without proceedings

• 41 inproceedings LaurineL. Huber, ChakerC. Memmadi, MathildeM. Dargnat and YannickY. Toussaint. Do Sentence Embeddings Capture Discourse Properties of Sentences from Scientific Abstracts ? CODI 2020 - EMNLP 1st Workshop on Computational Approaches to Discourse Punta Cana, Dominican Republic November 2020
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• 42 inproceedings ClaireC. Theobald, FrédéricF. Pennerath, BrieucB. Conan-Guez, MiguelM. Couceiro and AmedeoA. Napoli. A Bayesian Neural Network based on Dropout Regulation Workshop on Uncertainty in Machine Learning (WUML) at ECML-PKDD 2020 Conference N.A. (online), France 2020
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Scientific book chapters

• 43 inbookSébastienS. Ferré, MarianneM. Huchard, MehdiM. Kaytoue, SergeiS. Kuznetsov and AmedeoA. Napoli. Formal Concept Analysis: From Knowledge Discovery to Knowledge Processing2A Guided Tour of Artificial Intelligence ResearchAI AlgorithmsMay 2020, 411-445
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• 44 inbookBéatriceB. Fuchs, JeanJ. Lieber, LaurentL. Miclet, AlainA. Mille, AmedeoA. Napoli, HenriH. Prade and GillesG. Richard. Case-Based Reasoning, Analogy, and InterpolationVolume I: Knowledge Representation, Reasoning and LearningA Guided Tour of Artificial Intelligence ResearchA Guided Tour of Artificial Intelligence Researchhttps://link.springer.com/chapter/10.1007%2F978-3-030-06164-7_10May 2020, 307-339
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Edition (books, proceedings, special issue of a journal)

• 45 proceedings C. Benzitoun C. Braud L. Huber D. Langlois S. Ouni S. Pogodalla S. Schneider Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la Parole Nancy, France 2020
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• 46 proceedings C. Benzitoun C. Braud L. Huber D. Langlois S. Ouni S. Pogodalla S. Schneider Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 3 : Rencontre des Étudiants Chercheurs en Informatique pour le TAL. JEP-TALN-RECITAL 2020 : 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition) 3 Volume 3 : Rencontre des Étudiants Chercheurs en Informatique pour le TAL Nancy, France http://talnarchives.atala.org/RECITAL/RECITAL-2020/ 2020
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• 47 proceedings C. Benzitoun C. Braud L. Huber D. Langlois S. Ouni S. Pogodalla S. Schneider Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 4 : Démonstrations et résumés d'articles internationaux JEP-TALN-RECITAL 2020 4 Volume 4 : Démonstrations et résumés d'articles internationaux Nancy, France http://talnarchives.atala.org/TALN/TALN-2020/ 2020
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• 48 proceedings C. Benzitoun C. Braud L. Huber D. Langlois S. Ouni S. Pogodalla S. Schneider Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition. Volume 2 : Traitement Automatique des Langues Naturelles JEP-TALN-RECITAL 2020 2 Volume 2 : Traitement Automatique des Langues Naturelles Nancy, France http://talnarchives.atala.org/TALN/TALN-2020/ 2020
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• 49 proceedings M. Couceiro P. Monnin A. Napoli Proceedings of the 1st International Conference on Algebras, Graphs and Ordered Sets (ALGOS 2020) 1st International Conference on Algebras, Graphs and Ordered Sets (ALGOS 2020) Proceedings of the 1st International Conference on Algebras, Graphs and Ordered Sets (ALGOS 2020) Nancy, France https://algos2020.loria.fr/ August 2020
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• 50 proceedingsS. KuznetsovA. NapoliS. RudolphEighth International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at ECAI 2020)8th International Workshop "What can {FCA} do for Artificial Intelligence?" (FCA4AI 2020)CEUR Workshop Proceedings (CEUR-WS.org)Proceedings of the 8th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2020)Santiago de Compostela, Spainhttp://ceur-ws.org/Vol-2729November 2020, 153
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Doctoral dissertations and habilitation theses

• 51 thesis PierreP. Monnin. Matching and mining in knowledge graphs of the Web of data - Applications in pharmacogenomics Université de Lorraine December 2020
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Reports & preprints

• 52 misc NaciraN. Abbas, KholoudK. Alghamdi, MortazaM. Alinam, FrancescaF. Alloatti, GlendaG. Amaral, ClaudiaC. d'Amato, LuigiL. Asprino, MartinM. Beno, FelixF. Bensmann, RussaR. Biswas, LingL. Cai, RileyR. Capshaw, Valentina AnitaV. Carriero, IreneI. Celino, AmineA. Dadoun, StefanoS. De Giorgis, HarmH. Delva, JohnJ. Domingue, MichelM. Dumontier, VincentV. Emonet, MariekeM. Van Erp, Paola EspinozaP. Arias, OmaimaO. Fallatah, SebastiánS. Ferrada, Marc GallofréM. Ocaña, MichalisM. Georgiou, Genet AsefaG. Gesese, FrancesF. Gillis-Webber, FrancescaF. Giovannetti, Marìa GranadosM. Buey, IsmailI. Harrando, IvanI. Heibi, VitorV. Horta, LaurineL. Huber, FedericoF. Igne, Mohamad YaserM. Jaradeh, NehaN. Keshan, AnetaA. Koleva, BilalB. Koteich, KabulK. Kurniawan, MengyaM. Liu, ChuangtaoC. Ma, LientjeL. Maas, MartinM. Mansfield, FabioF. Mariani, EleonoraE. Marzi, SepidehS. Mesbah, MaheshkumarM. Mistry, Alba Catalina MoralesA. Tirado, AnnaA. Nguyen, Viet BachV. Nguyen, AllardA. Oelen, ValentinaV. Pasqual, HeikoH. Paulheim, AxelA. Polleres, MargheritaM. Porena, JanJ. Portisch, ValentinaV. Presutti, KaderK. Pustu-Iren, Ariam RivasA. Mendez, SoheilS. Roshankish, SebastianS. Rudolph, HaraldH. Sack, AhmadA. Sakor, JaimeJ. Salas, ThomasT. Schleider, MeilinM. Shi, GianmarcoG. Spinaci, ChangC. Sun, TabeaT. Tietz, Molka TounsiM. Dhouib, AlessandroA. Umbrico, Wouter van denW. Berg and WeiqinW. Xu. Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019 January 2021
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• 53 misc GuilhermeG. Alves, VaishnaviV. Bhargava, FabienF. Bernier, MiguelM. Couceiro and AmedeoA. Napoli. FixOut: an ensemble approach to fairer models December 2020
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• 54 report ClémentC. Bellanger, JaneJ. Hung, NyomanN. Juniarta, VincentV. Leroux, BernardB. Maigret and AmedeoA. Napoli. Chemoinformatics approaches to help antibacterial discovery Inria Nancy - Grand Est May 2020
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• 55 misc GuillaumeG. Bonfante and MiguelM. Couceiro. On termination of Graph Rewriting Systems through language theory February 2020
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• 56 misc MiguelM. Couceiro and ErkkoE. Lehtonen. Stability of Boolean function classes with respect to clones of linear functions January 2021
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• 57 misc TatianaT. Makhalova, Sergei O.S. Kuznetsov and AmedeoA. Napoli. Discovery data topology with the closure structure. Theoretical and practical aspects December 2020
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• 58 misc TatianaT. Makhalova, Sergei O.S. Kuznetsov and AmedeoA. Napoli. Mint: MDL-based approach for Mining INTeresting Numerical Pattern December 2020
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• 59 misc Jean-FrançoisJ.-F. Mari, OdileO. Horn and MarcM. Benoît. Stochastic and spectral analysis of time series for identifying periodic land use September 2020
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• 60 misc FrançoisF. Pirot and Jean-SébastienJ.-S. Sereni. Fractional chromatic number, maximum degree and girth November 2020
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Other scientific publications

• 61 thesis WalidW. Hafiane. Apprentissage par transfert pour l’extraction de relations pharmacogénomiques à partir de textes Université de Lorraine September 2020
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• 62 misc LethiciaL. Magno, AlainA. Mollier, MiguelM. Couceiro, Jean-ChristopheJ.-C. Avice and SophieS. Brunel-Muguet. Modelling Memory: do crop models need to become nostalgic? Montpellier, France February 2020
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• 63 thesisEstebanE. Marquer. LatticeNN -- Deep Learning and Formal Concept AnalysisUniversité de LorraineSeptember 2020, 74
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