Formal Concept Analysis on Its Way from Mathematics to Computer Science

Off to new shores: conceptual knowledge discovery and processing

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.

Adjunctive three-way concepts from positive and negative concepts in lattice-valued formal contexts

Recently the use of ontologies is becoming more widly spread, however building ontologies is remains a challenging issue, especially for maritime domain, which has no structural resources in existence. In this paper, we propose a method that is based on Formal Concept Analysis(FCA) and illustrate this method on some examples. Formal concept analysis is a theory of data analysis which identifies conceptual structures among data sets. This method intends to make ontology building more efficient and allows for discovering necessity for new concepts and relations in an ontology, which leads to an ontology that has these entities described in a way suitable for knowledge exchange.

PurposeWith the shift to an information-based society and to the de-centralisation of information, information overload has attracted a growing interest in the computer and information science research communities. However, there is no clear understanding of the meaning of the term, and while there have been many proposed definitions, there is no consensus. The goal of this work was to define the concept of “information overload”. In order to do so, a concept analysis using Rodgers' approach was performed.Design/methodology/approachA concept analysis using Rodgers' approach based on a corpus of documents published between 2010 and September 2020 was conducted. One surrogate for “information overload”, which is “cognitive overload” was identified. The corpus of documents consisted of 151 documents for information overload and ten for cognitive overload. All documents were from the fields of computer science and information science, and were retrieved from three databases: Association for Computing Machinery (ACM) Digital Library, SCOPUS and Library and Information Science s (LISA).FindingsThe themes identified from the authors’ concept analysis allowed us to extract the triggers, manifestations and consequences of information overload. They found triggers related to information characteristics, information need, the working environment, the cognitive abilities of individuals and the information environment. In terms of manifestations, they found that information overload manifests itself both emotionally and cognitively. The consequences of information overload were both internal and external. These findings allowed them to provide a definition of information overload.Originality/valueThrough the authors’ concept analysis, they were able to clarify the components of information overload and provide a definition of the concept.

FCANN: A new approach for extraction and representation of knowledge from ANN trained via Formal Concept Analysis

The paper first offers a parallel between two approaches to conceptual clustering, namely formal concept analysis (augmented with the introduction of new operators) and bipartite graph analysis. It is shown that a formal concept (as defined in formal concept analysis) corresponds to the idea of a maximal bi-clique, while sub-contexts, which correspond to independent “conceptual worlds” that can be characterized by means of the new operators introduced, are disconnected sub-graphs in a bipartite graph. The parallel between formal concept analysis and bipartite graph analysis is further exploited by considering “approximation” methods on both sides. It leads to suggest new ideas for providing simplified views of datasets, taking also inspiration from the search for approximate itemsets in data mining (with relaxed requirements), and the detection of communities in hierarchical small worlds.

Dual coding of knowledge in the human brain.

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.

The digital medium allows visitors, curators and art historians to gain new insights into their collections through data analysis and rich, interactive visualizations. Motivated by the rise of large-scale cultural heritage collections that have emerged on the Web, we argue that Formal Concept Analysis can be used to highlight the relationships between objects and their features within digital art collections and provide a means for visitors to explore these collections via interactive, narrated pathways. Our work presents four research projects that span 10 years from 2005 - 2015 – ImageSleuth, The Virtual Museum of the Pacific, A Place for Art and a scalability study of Formal Concept Analysis as applied to a data-set from the Brooklyn Museum. Our approach is based on the idea that much of the meaning that can be interpreted from museum collections lies – at least in part – in the way that objects are related to one another. Our work examines how Formal Concept Analysis can drive explorative, narrative-based visitor experiences and reveal new insights into cultural heritage collections.

Evaluation of IPAQ questionnaires supported by formal concept analysis

Generalized decision directed acyclic graphs for classification tasks

The concept lattice is a conceptual model firstly introduced by Wille in formal concept analysis, a theory of concept formation derived from lattice and order theory. Various concept lattice based applications have been reported in several domains such as conceptual clustering, conceptual knowledge representation and acquisition, and information retrieval. In this paper, we propose an object database approach for managing concept lattices in these applications. The goal of our work is two-fold. First, we extend the concept lattice model by basic operations supporting concept analysis. These operations allow to search and discover data directly from the concept lattice. Then, we present an approach for modeling and querying concept lattices within an object database framework.

Galois (concept) lattices and formal concept analysis have been proved useful in the resolution of many problems of theoretical and practical interest. Recent studies have put the emphasis on the need for both efficient and flexible algorithms to construct the lattice. In this paper, some equivalent conditions for an attributes subset to be a reduction of a formal concept are presented. Further more, the structure of concept lattice was analyzed and it is proved that each concept is the meet of some single attribute generalized concepts. Based on the above research, reduction-based approaches towards constructing concept lattice was presented.

With the exponential growth of the World Wide Web (WWW), it has become the most popular place to gather information. However the size of the WWW makes it difficult for people to locate relevant information. About 85% of all Web users use search engines of some kind for this purpose. However, existing search engines often do not return relevant information. The main focus of this paper is to improve search performance by using keywords and web pages which have been previously used or visited by other users. The Formal Concept Analysis (FCA) method has been adapted to maintain a concept map of keywords. This paper shows how both precision and recall has improved the specific-domain area, in which users can share the same knowledge.