A Proposition for Combining Pattern Structures and Relational Concept Analysis

International audienceIn this paper we propose an adaptation of the RCA process enabling the relational scaling of pattern structures. In a nutshell, this adaptation allows the scenario where RCA needs to be applied in a relational context family com-posed by pattern structures instead of formal contexts. To achieve this we define the heterogeneous pattern structures as a model to describe objects in a com-bination of spaces, namely the original object description space and the set of relational attributes derived from the RCA scaling process. We frame our ap-proach in the problem of characterizing latent variables (LV) in a latent variable model of documents and terms. LVs are used as compact and improved dataset representations. We approach the problem of LV characterization missing from the original LV-model, through the application of the adapted RCA process using pattern structures. Finally, we discuss the implications of our proposition

Enhancing layered enterprise architecture development through conceptual structures

Enterprise Architecture (EA) enables organisations to align their information technology with their business needs. Layered EA Development (LEAD) enhances EA by using meta-models made up of layered meta-objects, interconnected by semantic relations. Organisations can use these meta-models to benefit from a novel, ontology-based, object-oriented way of EA thinking and working. Furthermore, the meta-models are directed graphs that can be read linearly from a Top Down View (TDV) or a Bottom Up View (BUV) perspective. Conceptual Structures through CG-FCA (where CG refers to Conceptual Graph and FCA to Formal Concept Analysis) is thus used to traverse the TDV and BUV directions using the LEAD Industry 4.0 meta-model as an illustration. The motivation for CG-FCA is stated. It is discovered that CG-FCA: (a) identifies any unwanted cycles in the ‘top-down’ or ‘bottom-up’ directions, and (b) conveniently arranges the many pathways by which the meta-models can be traversed and understood in a Formal Concept Lattice. Through the LEAD meta-model exemplar, the wider appeal of CG-FCA and directed graphs are also identified

Why and How Knowledge Discovery Can Be Useful for Solving Problems with CBR

International audienceIn this talk, we discuss and illustrate links existing between knowledge discovery in databases (KDD), knowledge representation and reasoning (KRR), and case-based reasoning (CBR). KDD techniques especially based on Formal Concept Analysis (FCA) are well formalized and allow the design of concept lattices from binary and complex data. These concept lattices provide a realistic basis for knowledge base organization and ontology engineering. More generally, they can be used for representing knowledge and reasoning in knowledge systems and CBR systems as well

Molecular genetic differentiation of native populations of Mediterranean blue mussels, Mytilus galloprovincialis Lamarck, 1819, and the relationship with environmental variables

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Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery

International audienceKnowledge Discovery in Databases (KDD) and especially pattern mining can be interpreted along several dimensions, namely data, knowledge, problem-solving and interactivity. These dimensions are not disconnected and have a direct impact on the quality, applicability, and efficiency of KDD. Accordingly, we discuss some objectives of KDD based on these dimensions, namely exploration, knowledge orientation, hybridization, and explanation. The data space and the pattern space can be explored in several ways, depending on specific evaluation functions and heuristics, possibly related to domain knowledge. Furthermore, numerical data are complex and supervised numerical machine learning methods are usually the best candidates for efficiently mining such data. However, the work and output of numerical methods are most of the time hard to understand, while symbolic methods are usually more intelligible. This calls for hybridization, combining numerical and symbolic mining methods to improve the applicability and interpretability of KDD. Moreover, suitable explanations about the operating models and possible subsequent decisions should complete KDD, and this is far from being the case at the moment. For illustrating these dimensions and objectives, we analyze a concrete case about the mining of biological data, where we characterize these dimensions and their connections. We also discuss dimensions and objectives in the framework of Formal Concept Analysis and we draw some perspectives for future research

RCA as a Data Transforming Method: A Comparison with Propositionalisation

International audienceThis paper aims at comparing transformation-based appro-aches built to deal with relational data, and in particular two approaches which have emerged in two different communities: Relational Concept Analysis (RCA), based on an iterative use of the classical Formal Con-cept Analysis (FCA) approach, and Propositionalisation coming from the Inductive Logic Programming community. Both approaches work by transforming a complex problem into a simpler one, namely transform-ing a database consisting of several tables into a single table. For this purpose, a main table is chosen and new attributes capturing the in-formation from the other tables are built and added to this table. We show the similarities between those transformations for what concerns the principles underlying them, the semantics of the built attributes and the result of a classification performed by FCA on the enriched table. This is illustrated on a simple dataset and we also present a synthetic comparison based on a larger dataset from the hydrological domain

P.: Querying relational concept lattices

Abstract. Relational Concept Analysis (RCA) constructs conceptual abstractions from objects described by both own properties and interobject links, while dealing with several sorts of objects. RCA produces lattices for each category of objects and those lattices are connected via relational attributes that are abstractions of the initial links. Navigating such interrelated lattice family in order to find concepts of interest is not a trivial task due to the potentially large size of the lattices and the need to move the expert’s focus from one lattice to another. In this paper, we investigate the navigation of a concept lattice family based on a query expressed by an expert. The query is defined in the terms of RCA. Thus it is either included in the contexts (modifying the lattices when feasible), or directly classified in the concept lattices. Then a navigation schema can be followed to discover solutions. Different navigation possibilities are discussed

Answering Complex Queries on Legal Networks: A Direct and a Structured IR Approaches

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Exploring Heterogeneous Sequential Data on River Networks with Relational Concept Analysis

International audienceNowadays, many heterogeneous relational data are stored in databases to be further explored for discovering meaningful patterns. Such databases exist in various domains and we focus here on river monitoring. In this paper, a limited number of river sites that make up a river network (seen as a directed graph) is given. Periodically, for each river site three types of data are collected. Our aim is to reveal user-friendly results for visualising the intrinsic structure of these data. To that end, we present an approach for exploring heterogeneous sequential data using Relational Concept Analysis. The main objective is to enhance the evaluation step by extracting heterogeneous closed partially-ordered patterns organised into a hierarchy. The experiments and qualitative interpretations show that our method outputs instructive results for the hydro-ecological domain