BibSonomy publications for /user/grahl/conceptFri Nov 17 08:46:17 CET 2006Hong KongProc. 6th ICDM conferenceDecember(to appear)TRIAS - An Algorithm for Mining Iceberg Tri-Lattices2006nepomuk algorithm formal triadic folksonomy concept iceberg analysis lattices tagging trias fca Fri Nov 17 08:46:11 CET 2006HeidelbergFormal Concept Analysis: Foundations and Applicationshttp://www.springerlink.com/content/31aa2gtve59x3jh1/180-195LNAIEfficient Mining of Association Rules Based on Formal Concept Analysis36262005book formal discovery knowledge rule closed itemset concept analysis condensed kdd data-mining representation association fca 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.Fri Nov 17 08:46:11 CET 2006Journal Intelligent Information Systems (JIIS)129-60Generating a Condensed Representation for Association Rules242005kdd rule discovery condensed itemset association formal sets concept fca data-mining analysis knowledge closed representation Fri Nov 17 08:45:59 CET 2006HeidelbergConceptual Structures for Knowledge Creation and Communication.131-145LNAICreation and Merging of Ontology Top-Levels27462003merging formal analysis fca concept ontology We provide a new 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.Fri Nov 17 08:45:59 CET 2006Intl. J. Human-Comuter Studies (IJHCS)September3287-325Off to New Shores -- Conceptual Knowledge Discovery and Processing592003processing analysis discovery knowledge formal conceptual fca concept In the last years, the main orientation of Formal Concept Analysis (FCA) has turned from mathematics towards computer science. This article provides a review of this new orientation and analyzes why and how FCA and computer science attracted each other. It discusses FCA as a knowledge representation formalism using five knowledge representation principles provided by Davis, Shrobe, and Szolovits (1993). It then studies how and why mathematics-based researchers got attracted by computer science. We will argue for continuing this trend by integrating the two research areas FCA and Ontology Engineering. The second part of the article discusses three lines of research which witness the new orientation of Formal Concept Analysis: FCA as a conceptual clustering technique and its application for supporting the merging of ontologies; the efficient computation of association rules and the structuring of the results; and the visualization and management of conceptual hierarchies and ontologies including its application in an email management system.Fri Nov 17 08:45:59 CET 2006Text Clustering Based on Background KnowledgeTechnical Report 4252003clustering semantic concept text ontology knowledge formal analysis fca web background Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. Standard partitional or agglomerative clustering methods efficiently compute results to this end. However, the bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning has been achieved, because it is only specified extensionally. In order to deal with the two problems, we integrate background knowledge into the process of clustering text documents. First, we preprocess the texts, enriching their representations by background knowledge provided in a core ontology — in our application Wordnet. Then, we cluster the documents by a partitional algorithm. Our experimental evaluation on Reuters newsfeeds compares clustering results with pre-categorizations of news. In the experiments, improvements of results by background knowledge compared to the baseline can be shown for many interesting tasks. Second, the clustering partitions the large number of documents to a relatively small number of clusters, which may then be analyzed by conceptual clustering. In our approach, we applied Formal Concept Analysis. Conceptual clustering techniques are known to be too slow for directly clustering several hundreds of documents, but they give an intensional account of cluster results. They allow for a concise description of commonalities and distinctions of different clusters. With background knowledge they even find abstractions like “food” (vs. specializations like “beef” or “corn”). Thus, in our approach, partitional clustering reduces first the size of the problem such that it becomes tractable for conceptual clustering, which then facilitates the understanding of the results.Fri Nov 17 08:45:59 CET 2006Journal of Applied Artificial Intelligence (AAI)3281-301Conceptual Knowledge Discovery - a Human-Centered Approach172003analysis kdd knowledge formal discovery concept conceptual human fca In this paper we discuss Conceptual Knowledge Discovery in Databases (CKDD) as it is developing in the field of Conceptual Knowledge Processing. Conceptual Knowledge Processing is based on the mathematical theory of Formal Concept Analysis which has become a successful theory for data analysis during the last two decades. CKDD aims to support a human-centered process of discovering knowledge from data by visualizing and analyzing the conceptual structure of the data. We dicuss how the management system TOSCANA for conceptual information systems supports CKDD, and illustrate it by two applications in database marketing and flight movement analysis. Finally, we present a new tool for conceptual deviation discovery, Chianti.Fri Nov 17 08:45:59 CET 2006Journal of Applied Artificial Intelligence (AAI)3257-280Document Retrieval for Email Search and Discovery using Formal Concept Analysis172003concept email information retrieval nepomuk formal analysis fca search This paper discusses an document discovery tool based on conceptual clustering by formal concept analysis. The program allows users to navigate email using a visual lattice metaphor rather than a tree. It implements a virtual file structure over email where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in email discovery. The system described provides more flexibility in retrieving stored emails than what is normally available in email clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems and aid knowledge discovery in document collections.Fri Nov 17 08:45:59 CET 2006HeidelbergKnowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases217-228LNAIExplaining Text Clustering Results using Semantic Structures28382003text clustering fca semantic ontology concept analysis formal Common text clustering techniques offer rather poor capabilities for explaining to their users why a particular result has been achieved. They have the disadvantage that they do not relate semantically nearby terms and that they cannot explain how resulting clusters are related to each other. In this paper, we discuss a way of integrating a large thesaurus and the computation of lattices of resulting clusters into common text clustering in order to overcome these two problems. As its major result, our approach achieves an explanation using an appropriate level of granularity at the concept level as well as an appropriate size and complexity of the explaining lattice of resulting clusters.Fri Nov 17 08:45:54 CET 2006Lyon, FranceJuly 23,Advances in Formal Concept Analysis for Knowledge Discovery in Databases. Proc. Workshop FCAKDD of the 15th European Conference on Artificial Intelligence (ECAI 2002)2002ecai formal proceedings concept workshop analysis fca Fri Nov 17 08:45:54 CET 2006{J.} Knowledge and Data Engineering (KDE)2189-222Computing Iceberg Concept Lattices with {Titanic}422002itemset iceberg lattices formal titanic closed analysis fca concept Fri Nov 17 08:45:54 CET 2006HeidelbergWissensmanagement mit Referenzmodellen -- Konzepte für die Anwendungssystem- und Organisationsgestaltung163-174Using Ontologies and Formal Concept Analysis for Organizing Business Knowledge2002knowledge fca semantic formal concept wissensmanagement ontology analysis business management web Fri Nov 17 08:45:54 CET 2006Technique et Science Informatiques (TSI)165-95Pascal: un alogorithme d'extraction des motifs fréquents212002pascal association lattices rule condensed analysis iceberg fca representation closed titanic itemset concept formal Fri Nov 17 08:45:54 CET 2006HeidelbergConceptual Structures: Integration and Interfaces Invited Talk, summary of \cite{stumme03offtonew}2-19LNAIFormal Concept Analysis on its Way from Mathematics to Computer Science23932002formal fca concept informatics science analysis mathematics computer Fri Nov 17 08:45:54 CET 2006Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002)37-45Conceptual Clustering of Text Clusters2002concept clustering formal conceptual fca analysis text Fri Nov 17 08:45:47 CET 2006HeidelbergLNAIConceptual Structures -- Broadening the Base. Proc. 9th International Conference on Conceptual Structures21202001structure analysis fca cgs conceptual formal cg graph iccs concept Fri Nov 17 08:45:47 CET 2006HeidelbergConceptual Structures: Broadening the Base. 202-215LNAIReverse Pivoting in Conceptual Information Systems.21202001information reverse analysis concept formal conceptual pivoting toscana systems fca Fri Nov 17 08:45:47 CET 2006Seattle, WA, USAProc. 17th Intl. Conf. on Artificial Intelligence (IJCAI '01)225-230{FCA}-{M}erge: Bottom-Up Merging of Ontologies.2001ontology semantic concept web fca merging formal analysis Fri Nov 17 08:45:47 CET 2006HeidelbergKI 2001: Advances in Artificial Intelligence. KI 2001335-350LNAIIntelligent Structuring and Reducing of Association Rules and with Formal Concept Analysis21742001association formal concept kdd analysis representation closed data-mining knowledge discovery rule itemset condensed base fca Fri Nov 17 08:45:47 CET 2006Universität Dortmund 763Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)October{P}art of \cite{stumme02computing}Conceptual Clustering with Iceberg Concept Lattices2001clustering knowledge iceberg analysis conceptual itemset concept discovery lattices kdd fca closed formal