Our goals are to develop the theory of FCA, especially for higher-adic data sets as well as to develop efficient algorithms for mining and representing knowledge structures.

FCA uses an intuitive yet powerful graphical representation for knowledge structures as well as efficient algorithms to mine for knowledge patterns encoded in various data sets.


FCA Tools Bundle

FCA Tools Bundle is a website containing a collection of tools that can be used for conceptual structure analysis.

Research Seminars - Academic year 2017-2018

Usually the meetings are held in 406 room, 58-60 Teodor Mihali street, each Monday at 14:00.


Conceptual Graphs Based Modeling of Semi-Structured Data

Abstract: Due to the fast growing of data in the digital world, not only in volume but also in its variety (structured, unstructured or hybrid), traditional RDBMS are complemented with a rich set of systems, known as NoSQL. One of the main categories of NoSQL databases are document stores which are speci cally designed to handle semi-structured data, for instance XML documents. In this paper, we present a modeling method for semi-structured data based on Conceptual Graphs and exemplify the method on an XML document. The expressive power of Conceptual Graphs makes them particularly suitable for conceptual modeling of semi-structured data.

Keywords: Conceptual Graphs, conceptual modeling, semi-structured data

Using analogical complexes to improve human reasoning and decision making in Electronic Health Record Systems

Abstract: A key ability of human reasoning is analogical reasoning. In this context, an important notion is that of analogical proportions that have been formalized and analyzed in the last decade. A bridging to Formal Concept Analysis (FCA) has been brought by introducing analogical complexes, i.e. formal concepts that share a maximal analogical relation enabling by this analogies between (formal) concepts. Electronic Health Record (EHR) systems are nowadays widespread and used in different scenarios. In this paper we consider the problem of improving EHR systems by using analogical complexes in an FCA based setting. Moreover, we present a study case of analogical complexes in a medical field. We analyze analogical proportions in Electronic Health Record Systems and prove that EHRs can be improved with an FCA grounded analogical reasoning component. This component offers methods for knowledge discovery and knowledge acquisition for medical experts based on patterns revealed by analogies. We also show that combining analogical reasoning with FCA brings a new perspective on the analyzed data that can improve the understanding of the subsequent knowledge structures and offering a valuable support for decision making.

Keywords: Formal Concept Analysis, Analogical Complexes, Electronic Health Record Systems

Visualizing Conceptual Structures Using FCA Tools Bundle

Abstract: Formal Concept Analysis (FCA) is a prominent field of applied mathematics organising collections of knowledge - formal concepts - as conceptual landscapes of knowledge. FCA proved to be a promising theory to extract, analyse and visualise conceptual structures arising from various data structures. One of the strengths of FCA is the elegant, intuitive and powerful graphical representation of landscapes of knowledge as concept lattices. The purpose of this paper is to present FCA Tools Bundle and its various features, which is a bundle of tools for dyadic, many-valued, triadic and even polyadic FCA.

Keywords: Formal Concept Analysis, FCA Tools Bundle, Knowledge, concept lattice

Symptoms investigation by means of Formal Concept Analysis for enhancing medical diagnoses

Abstract: Pattern extraction is one of the major topics in Knowledge Discovery. Out of numerous data mining techniques we propose to use a new approach: Formal Concept Analysis (FCA) together with Graph Databases with the implementation Neo4j. FCA is a prominent field of applied mathematics using maximal clusters of object-attribute relationships to discover and represent knowledge structures. The use of FCA gained much importance in many research domains in recent years. Despite the similarity of the graph representation between FCA and Neo4j, the structure and relations among the elements are represented differently and can offer a different perspective on the analysis. This paper gives more insight into how patterns can be extracted from medical data and interpreted by means of FCA and Neo4j in order to help medical staff improve the accuracy of diagnoses. We examine the factors related to patients' symptoms and diagnostics and then compare the results with the ones provided by a medical care center. We apply knowledge discovery techniques and conceptual landscapes paradigm in order to obtain an in-depth and high qualitative knowledge representation of medical data. By making use of the effectiveness and the graphical representation of conceptual hierarchies we extract valuable knowledge from medical data sets through which we solve the knowledge discovery, processing and representation task in Electronic Health Record systems.

Keywords: Formal Concept Analysis, Electronic Health Record, medical data, many-valued contexts, conceptual scaling, Triadic FCA, Neo4J

Investigating Educational Attractors and Life Tracks in e-Learning Environments Using Formal Concept Analysis

Abstract: E-learning platforms are widely used in modern education. While in the traditional education the instructor does not have a comprehensive insight on how his students are using the educational resources, the situation is different for online learning environments. Web usage logs comprise a variety of information regarding the visited pages.  These web-logs become a rich resource for data analysis. Understanding usage patterns from web-logs is widely used in order to improve web-based applications. In our research we are interested in distilling valuable knowledge on users behavior in online educational platforms using the knowledge discovery and processing methods of Formal Concept Analysis (FCA). This knowledge can then be used to understand how students are using the educational resources, to gain insight on their online behavior as well as how they use these resources over time. In this paper, we focus on the detection of behavioral patterns in web-based e-learning environments and on how users adhere to intended educational attractors. For this, we first use FCA to investigate so-called educational attractors and then distill users life tracks using Temporal Concept Analysis. We exemplify the developed methods on a locally developed e-learning platform called PULSE.

Keywords: Life tracks, Temporal Concept Analysis, Web logs analysis, Conceptual structures, User behavior, Attractors