5 research outputs found
Supporting scientific knowledge discovery with extended, generalized Formal Concept Analysis
In this paper we fuse together the Landscapes of Knowledge of Wille's and Exploratory Data Analysis by leveraging Formal Concept Analysis (FCA) to support data-induced scientific enquiry and discovery. We use extended FCA first by allowing K-valued entries in the incidence to accommodate other, non-binary types of data, and second with different modes of creating formal concepts to accommodate diverse conceptualizing phenomena. With these extensions we demonstrate the versatility of the Landscapes of Knowledge metaphor to help in creating new scientific and engineering knowledge by providing several successful use cases of our techniques that support scientific hypothesis-making and discovery in a range of domains: semiring theory, perceptual studies, natural language semantics, and gene expression data analysis. While doing so, we also capture the affordances that justify the use of FCA and its extensions in scientific discovery.FJVA and AP were partially supported by EUFP7 project LiMo-
SINe (contract288024) for this research. CPM was partially supported
by the Spanish Ministry of Economics and Competitiveness projects
TEC2014-61729-EXP and TEC2014-53390-P
Mining for Unknown Unknowns
Unknown unknowns are future relevant contingencies that lack an ex ante
description. While there are numerous retrospective accounts showing that
significant gains or losses might have been achieved or avoided had such
contingencies been previously uncovered, getting hold of unknown unknowns still
remains elusive, both in practice and conceptually. Using Formal Concept
Analysis (FCA) - a subfield of lattice theory which is increasingly applied for
mining and organizing data - this paper introduces a simple framework to
systematically think out of the box and direct the search for unknown unknowns.Comment: In Proceedings TARK 2023, arXiv:2307.0400
Non-Redundant Implicational Base of Many-Valued Context Using SAT
Some attribute implications in an implicational base of a derived context of many-valued context can be inferred from some other attribute implications together with its scales. The scales are interpretation of some values in the many-valued context therefore they are a prior or an existing knowledge. In knowledge discovery, the such attribute implications are redundant and cannot be considered as new knowledge. Therefore the attribute implicational should be eliminated. This paper shows that the redundancy problem exists and formalizes a model to check the redundancy
A New Model Oriented on The Values of Science, Islamic, and Problem-Solving in Elementary Schools
Problem-Based Learning (PBL) is a learning model that is directly related to students' abilities in problem-solving and scientific thinking. The PBL model is an active and structured pedagogical learning where students are placed as the center of the learning process. Learning activities used are presenting scenarios in the form of problems for study groups by researching and presenting solutions adjusted to the main problem