Formal Concept Analysis on Its Way from Mathematics to Computer Science

Stumme, Gerd

doi:10.1007/3-540-45483-7_2

Cited by 31 publications

(24 citation statements)

References 49 publications

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“…Formal Concept Analysis (FCA) arose twenty-five years ago as a mathematical theory (Stumme, 2002). FCA was for the first time used as an exploratory data analysis and knowledge enrichment technique for analysing domestic violence cases in the Netherlands (Poelmans, 2008).…”

Section: Exploratory Analysis With Fcamentioning

confidence: 99%

“…For our data exploration we chose to use a combination of two visually appealing discovery techniques, known as Formal Concept Analysis (FCA) (Stumme, 2002) and Emergent Self Organizing Maps (ESOM) (Ultsch, 2005). In this paper we aim at making the reader familiar with how we used these tools for browsing through the data in search of new knowledge for classifying new cases.…”

Section: Introductionmentioning

confidence: 99%

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DETECTING DOMESTIC VIOLENCE - Showcasing a Knowledge Browser based on Formal Concept Analysis and Emergent Self Organizing Maps

Elzinga

Poelmans

Viaene

et al. 2009

Proceedings of the 11th International Conference on Enterprise Information

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Abstract:Over 90% of the case data from police inquiries is stored as unstructured text in police databases. We use the combination of Formal Concept Analysis and Emergent Self Organizing Maps for exploring a dataset of unstructured police reports out of the Amsterdam-Amstelland police region in the Netherlands. In this paper, we specifically aim at making the reader familiar with how we used these two tools for browsing the dataset and how we discovered useful patterns for labelling cases as domestic or as non-domestic violence.

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Section: Exploratory Analysis With Fcamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DETECTING DOMESTIC VIOLENCE - Showcasing a Knowledge Browser based on Formal Concept Analysis and Emergent Self Organizing Maps

Elzinga

Poelmans

Viaene

et al. 2009

Proceedings of the 11th International Conference on Enterprise Information

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“…FCA represents a context as a triple (E, A, F ) where E and A are sets of examples (extents) and attributes (intents) and F : E → A is a mapping function. All attributes thought with a concept is called its intension and all instances for which the concept can be predicated is called its extension [Stumme, 2002]…”

Section: Resolving Misunderstandings At the Sign Levelmentioning

confidence: 99%

Consensus ontologies in socially interacting MultiAgent systems

Biçici

2008

MGS

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This paper presents approaches for building, managing, and evaluating consensus ontologies from the individual ontologies of a network of socially interacting agents. Each agent has its own conceptualization of the world within the multiagent system framework. The interactions between agents are modeled by sending queries and receiving responses and later assessing each other's performance based on the results. This model enables us to measure the quality of the societal beliefs in the resources which we represent as the expertise in each domain. The dynamic nature of our system allows us to model the emergence of consensus that mimics the evolution of language. We present an algorithm for generating the consensus ontologies which makes use of the authoritative agent's conceptualization in a given domain. As the expertise of agents changes after a number of interactions, the consensus ontology that we build based on the agents' individual views evolves. The resulting approach is concordant with the principles of emergent semantics. We provide formal definitions for the problem of finding a consensus ontology in a step by step manner. We evaluate the consensus ontologies by using different heuristic measures of similarity based on the component ontologies. Conceptual processing methods for generating, manipulating, and evaluating consensus ontologies are given and experimental results are presented. The presented approach looks promising and opens new directions for further research.

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“…The theory of formal concept analysis (FCA), originally proposed by Wille 1 in 1982, is oriented towards the discovery of formal concepts and their hierarchical structure at its early stage of development, but nowadays it has shown a trend of multidisciplinary intersection and fusion 2,3,4,5,6,7 . This theory is based on mathematical order theory, in particular the theory of complete lattice, and it has been considered to be an effective tool for conceptual data analysis and knowledge processing 8 .…”

Section: Introductionmentioning

confidence: 99%

On inference rules in decision formal contexts

Mei

Wang

et al. 2014

International Journal of Computational Intelligence Systems

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Rule acquisition is one of the main purposes in the analysis of decision formal contexts. In general, the number of implications in a decision formal context is an exponential increase to the scale of the database. So, it is important to introduce effective inference rules between implications for eliminating as many superfluous implications as possible. This study puts forward a criterion called 'strongness' to assess the effectiveness of inference rules in terms of eliminating superfluous implications. We define a new inference rule in decision formal contexts and prove that the proposed inference rule is stronger than the existing one. Furthermore, we figure out the exact number of the superfluous implications that we can additionally remove by using the proposed inference rule compared with the existing one.

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Formal Concept Analysis on Its Way from Mathematics to Computer Science

Cited by 31 publications

References 49 publications

DETECTING DOMESTIC VIOLENCE - Showcasing a Knowledge Browser based on Formal Concept Analysis and Emergent Self Organizing Maps

DETECTING DOMESTIC VIOLENCE - Showcasing a Knowledge Browser based on Formal Concept Analysis and Emergent Self Organizing Maps

Consensus ontologies in socially interacting MultiAgent systems

On inference rules in decision formal contexts

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