Exploration tools in formal concept analysis

The development of conceptual knowledge systems specifically requests knowledge acquisition tools within the framework of formal concept analysis. In this paper, the existing tools are presented, and furhter developments are discussed.

Conceptual structures represented by conceptual graphs and formal concept analysis

Conceptual Graphs and Formal Concept Analysis have in common basic concerns: the focus on conceptual structures, the use of diagrams for supporting communication, the orientation by Peirce's Pragmatism, and the aim of representing and processing knowledge. These concerns open rich possibilities of interplay and integration. We discuss the philosophical foundations of both disciplines, and analyze their specific qualities. Based on this analysis, we discuss some possible approaches of interplay and integration.

Intelligent structuring and reducing of association rules with formal concept analysis

Association rules are used to investigate large databases. The analyst is usually confronted with large lists of such rules and has to find the most relevant ones for his purpose. Based on results about knowledge representation within the theoretical framework of Formal Concept Analysis, we present relatively small bases for association rules from which all rules can be deduced. We also provide algorithms for their calculation.

Efficient data mining based on formal concept analysis

Formal Concept Analysis is an unsupervised learning technique for conceptual clustering. We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are designed for analyzing very large databases. In particular they serve as a condensed representation of frequent patterns as known from association rule mining. In order to show the interplay between Formal Concept Analysis and association rule mining, we discuss the algorithm TITANIC. We show that iceberg concept lattices are a starting point for computing condensed sets of association rules without loss of information, and are a visualization method for the resulting rules.

Conceptual knowledge processing with formal concept analysis and ontologies

Among many other knowledge representations formalisms, Ontologies and Formal Concept Analysis (FCA) aim at modeling ‘concepts’. We discuss how these two formalisms may complement another from an application point of view. In particular, we will see how FCA can be used to support Ontology Engineering, and how ontologies can be exploited in FCA applications. The interplay of FCA and ontologies is studied along the life cycle of an ontology: (i) FCA can support the building of the ontology as a learning technique. (ii) The established ontology can be analyzed and navigated by using techniques of FCA. (iii) Last but not least, the ontology may be used to improve an FCA application.

Ontology merging with formal concept analysis

Ontologies have been established for knowledge sharing and are widely used as a means for conceptually structuring domains of interest. With the growing usage of ontologies, the problem of overlapping knowledge in a common domain becomes critical. In this short paper, we address two methods for merging ontologies based on Formal Concept Analysis: FCA-Merge and ONTEX. --- FCA-Merge is a method for merging ontologies following a bottom-up approach which offers a structural description of the merging process. The method is guided by application-specific instances of the given source ontologies. We apply techniques from natural language processing and formal concept analysis to derive a lattice of concepts as a structural result of FCA-Merge. The generated result is then explored and transformed into the merged ontology with human interaction. --- ONTEX is a method for systematically structuring the top-down level of ontologies. It is based on an interactive, top-down- knowledge acquisition process, which assures that the knowledge engineer considers all possible cases while avoiding redundant acquisition. The method is suited especially for creating/merging the top part(s) of the ontologies, where high accuracy is required, and for supporting the merging of two (or more) ontologies on that level.

Efficient mining of association rules based on formal concept analysis

Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to Formal Concept Analysis (FCA). In this survey, we will first introduce some basic ideas of this connection along a specific algorithm, TITANIC, and show how FCA helps in reducing the number of resulting rules without loss of information, before giving a general overview over the history and state of the art of applying FCA for association rule mining.

A geometrical heuristic for drawing concept lattices

Concept lattices are used in formal concept analysis to represent data conceptually so that the original data are still recognizable. Their line diagrams should reflect the semantical relationships within the data. Up to now, no satisfactory automatic drawing programs for this task exist. The geometrical heuristic is the most successful tool for drawing concept lattices manually. It ueses a geometric representation as intermediate step between the list of upper covers and the line diagram of the lattice.

Concept exploration - a tool for creating and exploring conceptual hierarchies

Concept exploration is a knowledge acquisition tool for interactively exploring the hierarchical structure of finitely generated lattices. Applications comprise the support of knowledge engineers by constructing a type lattice for conceptual graphs, and the exploration of large formal contexts in formal concept analysis.

Conceptual knowledge discovery and data analysis

In this paper, we discuss Conceptual Knowledge Discovery in Databases (CKDD) in its connection with Data Analysis. Our approach is based on Formal Concept Analysis, a mathematical theory which has been developed and proven useful during the last 20 years. Formal Concept Analysis has led to a theory of conceptual information systems which has been applied by using the management system TOSCANA in a wide range of domains. In this paper, we use such an application in database marketing to demonstrate how methods and procedures of CKDD can be applied in Data Analysis. In particular, we show the interplay and integration of data mining and data analysis techniques based on Formal Concept Analysis. The main concern of this paper is to explain how the transition from data to knowledge can be supported by a TOSCANA system. To clarify the transition steps we discuss their correspondence to the five levels of knowledge representation established by R. Brachman and to the steps of empirically grounded theory building proposed by A. Strauss and J. Corbin.

Elterliche Geschlechtsstereotype und deren Einfluss auf das mathematische Selbstkonzept von Grundschulkindern

Ausgangspunkt dieser Dissertation ist die Überlegung, warum Mädchen und Frauen in mathematisch-naturwissenschaftlichen Fächern und Berufen unterrepräsentiert sind. Irrtümlicherweise werden als Erklärung hierfür häufig Geschlechterdifferenzen in der Mathematikleistung herangezogen. Diese bieten jedoch aufgrund nicht einheitlicher Forschungsbefunde keinen zufriedenstellenden Erklärungsansatz. Naheliegender ist es, das mangelnde Selbstvertrauen von Mädchen in Mathematik (als mathematisches Selbstkonzept bezeichnet) als Ursache heranzuziehen, denn verschiedene Studien kamen zu dem Ergebnis, dass Mädchen, auch bei vergleichbarer Leistung, ein geringeres mathematisches Selbstkonzept aufweisen als Jungen (Dickhäuser & Stiensmeier-Pelster, 2003; Frome & Eccles, 1998; Rustemeyer & Jubel, 1996; Skaalvik & Skaalvik, 2004). Die Rolle der Eltern als primäre Sozialisationsinstanz wird als bedeutsamer Einflussfaktor auf das mathematische Selbstkonzept von Kindern beschrieben. Besonders für den Bereich Mathematik besteht die Gefahr, dass Eltern durch geschlechtsstereotype Einstellungen und Erwartungen ihre Tochter ungünstig beeinflussen (Jacobs, 1991; Tiedemann, 2000). In dieser Arbeit wird untersucht, inwiefern Eltern Geschlechtsstereotype zuungunsten der Mädchen in Mathematik äußern und inwiefern sich diese – schon zur Grundschulzeit – in den elterlichen Einschätzungen (elterliche Leistungs- und Fähigkeitseinschätzungen sowie elterliche Ursachenerklärungen) des eigenen Kindes widerspiegeln. Es wird angenommen, dass Mädchen entsprechend dem klassischen Geschlechtsstereotyp weniger talentiert und weniger leistungsstark in Mathematik eingeschätzt werden als Jungen. Für die Einschätzungen des eigenen Kindes wird erwartet, dass diese geschlechtsspezifische Verzerrungen zuungunsten der Mädchen aufweisen. Anhand von Pfadmodellen wird in dieser Arbeit der Einfluss elterlicher Geschlechtsstereotype und Einschätzungen, unter Kontrolle der vorangegangenen Mathematikleistung und des vorangegangenen mathematischen Selbstkonzeptes des Kindes, auf das aktuelle mathematische Selbstkonzept des Kindes am Ende des dritten Schuljahres analysiert. Als Grundlage dienen Daten von circa 900 Schülern und 400 Eltern aus dem Projekt Persönlichkeits- und Lernentwicklung von Grundschulkindern (PERLE). Die Befunde der vorliegenden Arbeit können bisherige Forschungsbefunde aus dem Sekundarbereich für den Grundschulbereich replizieren und weitere erstmalige Befunde ergänzen. Zusammenfassend kann festgehalten werden, dass knapp zwei Drittel der Eltern Geschlechtsstereotype zuungunsten der Mädchen in Mathematik äußern. Die Pfadanalysen ergeben, dass nicht das Geschlecht des Kindes, sondern Wechselwirkungen zwischen Geschlecht und elterlichen Geschlechtsstereotypen die elterlichen Einschätzungen des eigenen Kindes beeinflussen. Wenn Eltern Geschlechtsstereotype vertreten, schätzen sie eine Tochter ungünstiger ein als einen Sohn (unabhängig von der tatsächlichen Mathematikleistung des Kindes). Die elterlichen Einschätzungen haben wiederum einen signifikanten Einfluss auf das mathematische Selbstkonzept des Kindes. Die Ergebnisse dieser Arbeit werden abschließend diskutiert und Ansätze für Interventionen aufgezeigt.