Fig 2 - uploaded by Peter W. Eklund
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Diagram generated from the Phones database with mp3player:yes , games:yes and chat:yes as Filter elements.
Source publication
This paper introduces a framework for relational schema navigation via a Web-based browser application that uses Formal Con- cept Analysis as the metaphor for analysis and interaction. Formal Con- cept Analysis is a rich framework for data analysis based on applied lat- tice and order theory. The application we develop, D-SIFT, is intended to provi...
Context in source publication
Context 1
... these relationships it is easiest to see relations such as mutual exclusivity and implication. Figure 2 shows a simple lattice diagram. The user can see that mp3player:yes and chat:yes are mutually exclusive (there are no phones with both an MP3 player and a chat function) because the point where the concepts join (reading the diagram downwards) has an extent size of 0. Also, it can be seen that chat:yes implies games:yes (every phone with a chat function also has games). ...
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Citations
... B. Safar and H. Kefi harnessed domain ontology and formal concept analysis for implementing an interactive querying system on a topical resources repository [16]. Jon D. et al. developed D-SIFT to provide untrained users with practical and intuitive access to the core functionality of formal concept analysis to explore relational database schema [17]. Sergei O. et al compared several concept lattice construction algorithms for generating concept lattices [18]. ...
Relational Concept Analysis (RCA) is an extension
to the Formal Concept Analysis (FCA) which is an unsupervised classification method producing concept lattices. In addition RCA considers relations between objects from different contexts and builds a set of connected lattices. This feature makes it more intuitive to extract knowledge from relational data and gives richer results. However, data with many relations imply scalability problems and numerous results that are difficult to exploit. We propose in this article a possible adaptation of RCA to explore relations in a guided way in order to increase the performance and the pertinence of the results. We also present an application of exploratory RCA to environmental data for extracting knowledge on water quality of watercourses.
This is the second part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003 and 2011 mentioning terms related to Formal Concept Analysis in the title, abstract and keywords. We developed a knowledge browsing environment to support our literature analysis process. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. In this second part, we zoom in on and give an extensive overview of the papers published between 2003 and 2011 which applied FCA-based methods for knowledge discovery and ontology engineering in various application domains. These domains include software mining, web analytics, medicine, biology and chemistry data.
This is the second part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003-2011 mentioning terms related to Formal Concept Analysis in the title, abstract and keywords. We developed a knowledge browsing environment to support our literature analysis process. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. In this second part, we zoom in on and give an extensive overview of the papers published between 2003 and 2011 which applied FCA-based methods for knowledge discovery and ontology engineering in various application domains. These domains include software mining, web analytics, medicine, biology and chemistry data.
Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.
