Artificial intelligence/Intelligence artificielle in areas (2024-03-15)
Luisa Werner, Pierre Genevès, Nabil Layaïda, Jérôme Euzenat, Damien Graux, Reproduce, replicate, reevaluate: the long but safe way to extend machine learning methods, in: Proc. 38th AAAI Conference on Artificial Intelligence (AAAI), Vancouver (CA), 2024
Reproducibility is a desirable property of scientific research. On the one hand, it increases confidence in results. On the other hand, reproducible results can be extended on a solid basis. In rapidly developing fields such as machine learning, the latter is particularly important to ensure the reliability of research. In this paper, we present a systematic approach to reproducing (using the available implementation), replicating (using an alternative implementation) and reevaluating (using different datasets) state-of-the-art experiments. This approach enables the early detection and correction of deficiencies and thus the development of more robust and transparent machine learning methods. We detail the independent reproduction, replication, and reevaluation of the initially published experiments with a method that we want to extend. For each step, we identify issues and draw lessons learned. We further discuss solutions that have proven effective in overcoming the encountered problems. This work can serve as a guide for further reproducibility studies and generally improve reproducibility in machine learning.
Jérôme Euzenat, Stepwise functional refoundation of relational concept analysis, Research report 9518, INRIA, Grenoble (FR), 68p., October 2023
Relational concept analysis (RCA) is an extension of formal concept analysis allowing to deal with several related contexts simultaneously. It has been designed for learning description logic theories from data and used within various applications. A puzzling observation about RCA is that it returns a single family of concept lattices although, when the data feature circular dependencies, other solutions may be considered acceptable. The semantics of RCA, provided in an operational way, does not shed light on this issue. In this report, we define these acceptable solutions as those families of concept lattices which belong to the space determined by the initial contexts (well-formed), cannot scale new attributes (saturated), and refer only to concepts of the family (self-supported). We adopt a functional view on the RCA process by defining the space of well-formed solutions and two functions on that space: one expansive and the other contractive. We show that the acceptable solutions are the common fixed points of both functions. This is achieved step-by-step by starting from a minimal version of RCA that considers only one single context defined on a space of contexts and a space of lattices. These spaces are then joined into a single space of context-lattice pairs, which is further extended to a space of indexed families of context-lattice pairs representing the objects manipulated by RCA. We show that RCA returns the least element of the set of acceptable solutions. In addition, it is possible to build dually an operation that generates its greatest element. The set of acceptable solutions is a complete sublattice of the interval between these two elements. Its structure and how the defined functions traverse it are studied in detail.
Formal Concept Analysis, Relational concept analysis, Fixed point, Fixed-point semantics, Circular dependency
Isabelle Bloch, Jérôme Euzenat, Jérôme Lang, François Schwarzentruber (éds), Post-actes de la Conférence Nationale en Intelligence Artificielle (CNIA 2018-2020), Revue ouverte d'intelligence artificielle (numéro spécial) 3(3-4):193-413, 2022
Isabelle Bloch, Jérôme Euzenat, Jérôme Lang, François Schwarzentruber, Introduction, Revue ouverte d'intelligence artificielle 3(3-4):193-199, 2022
Jérôme Euzenat, Fixed-point semantics for barebone relational concept analysis, in: Proc. 16th international conference on formal concept analysis (ICFCA), Strasbourg (FR), (Agnès Braud, Aleksey Buzmakov, Tom Hanika, Florence Le Ber (eds), Proc. 16th international conference on formal concept analysis (ICFCA), Lecture notes in computer science 12733, 2021), pp20-37, 2021
Relational concept analysis (RCA) extends formal concept analysis (FCA) by taking into account binary relations between formal contexts. It has been designed for inducing description logic TBoxes from ABoxes, but can be used more generally. It is especially useful when there exist circular dependencies between objects. In this case, it extracts a unique stable concept lattice family grounded on the initial formal contexts. However, other stable families may exist whose structure depends on the same relational context. These may be useful in applications that need to extract a richer structure than the minimal grounded one. This issue is first illustrated in a reduced version of RCA, which only retains the relational structure. We then redefine the semantics of RCA on this reduced version in terms of concept lattice families closed by a fixed-point operation induced by this relational structure. We show that these families admit a least and greatest fixed point and that the well-grounded RCA semantics is characterised by the least fixed point. We then study the structure of other fixed points and characterise the interesting lattices as the self-supported fixed points.
Formal Concept Analysis
Jérôme Euzenat, A map without a legend: the semantic web and knowledge evolution, Semantic web journal 11(1):63-68, 2020
The current state of the semantic web is focused on data. This is a worthwhile progress in web content processing and interoperability. However, this does only marginally contribute to knowledge improvement and evolution. Understanding the world, and interpreting data, requires knowledge. Not knowledge cast in stone for ever, but knowledge that can seamlessly evolve; not knowledge from one single authority, but diverse knowledge sources which stimulate confrontation and robustness; not consistent knowledge at web scale, but local theories that can be combined. We discuss two different ways in which semantic web technologies can greatly contribute to the advancement of knowledge: semantic eScience and cultural knowledge evolution.
Semantic web, Linked data, Big data, Open data, Knowledge representation, Knowledge, Ontology, Machine learning, Reproducible research, eScience, Cultural evolution
Armen Inants, Jérôme Euzenat, So, what exactly is a qualitative calculus?, Artificial intelligence 289:103385, 2020
The paradigm of algebraic constraint-based reasoning, embodied in the notion of a qualitative calculus, is studied within two alternative frameworks. One framework defines a qualitative calculus as "a non-associative relation algebra (NA) with a qualitative representation", the other as "an algebra generated by jointly exhaustive and pairwise disjoint (JEPD) relations". These frameworks provide complementary perspectives: the first is intensional (axiom-based), whereas the second one is extensional (based on semantic structures). However, each definition admits calculi that lie beyond the scope of the other. Thus, a qualitatively representable NA may be incomplete or non-atomic, whereas an algebra generated by JEPD relations may have non-involutive converse and no identity element. The divergence of definitions creates a confusion around the notion of a qualitative calculus and makes the "what" question posed by Ligozat and Renz actual once again. Here we define the relation-type qualitative calculus unifying the intensional and extensional approaches. By introducing the notions of weak identity, inference completeness and Q-homomorphism, we give equivalent definitions of qualitative calculi both intensionally and extensionally. We show that "algebras generated by JEPD relations" and "qualitatively representable NAs" are embedded into the class of relation-type qualitative algebras.
Algebraic constraint-based reasoning, Qualitative reasoning, Qualitative calculus, Relation algebra
Jérôme Euzenat, François Schwarzentruber (éds), Actes Conférence NationaleAFIA sur d'Intelligence Artificielle et Rencontres Jeunes Chercheurs en Intelligence Artificielle (CNIA+RJCIA), Nancy (FR), 133p., 2018