On Succinct Representation of Knowledge Community Taxonomies With Formal Concept Analysis

Abstract

We present an application of formal concept analysis aimed at representing a meaningful structure of knowledge communities in the form of a lattice-based taxonomy. The taxonomy groups together agents (community members) who develop a set of notions. If no constraints are imposed on how it is built, a knowledge community taxonomy may become extremely complex and difficult to analyze. We consider two approaches to building a concise representation respecting the underlying structural relationships, while hiding uninteresting and/or superfluous information: a pruning strategy based on the notion of concept stability and a representational improvement based on nested line diagrams and "zooming". We illustrate the methods on two examples: a community of embryologists and a community of researchers in complex systems.

FCA for Common Interest Communities discovering

“…In fact, only very few researches have actually focused on this difficulty [17]. The authors in [7] used the iceberg method as well as the stability method as a Galois lattice reduction methods. Authors in [18] identify concepts with frequent intents above a set threshold.…”

“…the semantic information. Recently, several works have been proposed for this purpose, they have used the formal concept analysis(FCA) as conceptual clustering method [7]. This approaches attempt to extract communities preserving knowledge shared in each community.…”

Data Weeding Techniques Applied to Roget’s Thesaurus

“…Roth et al (2008) modify this notion of "zoom" by applying local criteria to the calculation of inner scales instead of a global algorithm. In Stumme's (1996) approach to "local scaling" some concepts are expanded with inner scales, while others are not.…”

“…All concepts that are less stable than a user-defined threshold are removed. Roth et al (2008) use a slight variant of Kuznetsov's definition of stability for their pruning, in combination with their notions of nesting and zooming. As mentioned above, Roth et al's notion of "zooming" is slightly different from Vogt & Wille's (1995).…”

Triadic Formal Concept Analysis and triclustering: searching for optimal patterns

“…Yet these different frameworks considerably overlap in applications. Among those: finding co-regulated genes over gene expression data (Madeira and Oliveira 2004;Besson et al 2005;Barkow et al 2006;Tarca et al 2007;Hanczar and Nadif 2010;Kaytoue et al 2011;Eren et al 2013), prediction of biological activity of chemical compounds (Blinova et al 2003;Kuznetsov and Samokhin 2005;DiMaggio et al 2010;Asses et al 2012), summarization and classification of texts (Dhillon 2001;Cimiano et al 2005;Banerjee et al 2007;Ignatov and Kuznetsov 2009;Carpineto et al 2009), structuring websearch results and browsing navigation in Information Retrieval (Carpineto and Romano 2005;Koester 2006;Eklund et al 2012;Poelmans et al 2012), finding communities in two-mode networks in Social Network Analysis (Duquenne 1996;Freeman 1996;Latapy et al 2008;Roth et al 2008;Gnatyshak et al 2012) and Recommender Systems (Boucher-Ryan and Bridge 2006;Symeonidis et al 2008;Ignatov and Kuznetsov 2008;Nanopoulos et al 2010;Ignatov et al 2014).…”