Concept Relation Discovery and Innovation Enabling Technology (CORDIET)

Concept Relation Discovery and Innovation Enabling Technology (CORDIET), is a toolbox for gaining new knowledge from unstructured text data. At the core of CORDIET is the C-K theory which captures the essential elements of innovation. The tool uses Formal Concept Analysis (FCA), Emergent Self Organizing Maps (ESOM) and Hidden Markov Models (HMM) as main artifacts in the analysis process. The user can define temporal, text mining and compound attributes. The text mining attributes are used to analyze the unstructured text in documents, the temporal attributes use these document's timestamps for analysis. The compound attributes are XML rules based on text mining and temporal attributes. The user can cluster objects with object-cluster rules and can chop the data in pieces with segmentation rules. The artifacts are optimized for efficient data analysis; object labels in the FCA lattice and ESOM map contain an URL on which the user can click to open the selected document

Advancing FCA Workflow in FCART System for Knowledge Discovery in Quantitative Data

AbstractWe describe new features in FCART software system, an integrated environment for knowledge and data engineers with a set of research tools based on Formal Concept Analysis. The system is intended for knowledge discovery from various data sources, including structured quantitative data and text collections. Final version of data transformation from external data source into concept lattice is considered. We introduce new version of local data storage, query language for conceptual scaling of data snapshots as multi-valued contexts, and new tools for working with formal concepts

Proceedings of the ECAI Workshop on Formal Concept Analysis for Artificial Intelligence (FCA4AI)

International audienceFormal Concept Analysis (FCA) is aimed at data analysis and classification. FCA proposes various efficient tools for concept lattice design and visualization, and is related to many research fields and application domains, including several fields of Artificial Intelligence (AI), e.g. knowledge discovery, knowledge representation and reasoning. In recent years, a series of work emerged for extending the possibilities of FCA w.r.t. knowledge processing, e.g. pattern structures and relational context analysis. Such extensions should allow FCA to deal with complex data from the knowledge discovery and the knowledge representation points of view. Moreover, these extensions of the capabilities of FCA offer new possibilities for AI activities in the framework of FCA. Accordingly, this workshop will be interested in two main issues: (i) how can FCA support AI activities and especially knowledge processing and (ii) how can FCA be extended for solving new and complex problems in AI

FCAIR 2012 Formal Concept Analysis Meets Information Retrieval Workshop co-located with the 35th European Conference on Information Retrieval (ECIR 2013) March 24, 2013, Moscow, Russia

International audienceFormal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classifiation. The area came into being in the early 1980s and has since then spawned over 10000 scientific publications and a variety of practically deployed tools. FCA allows one to build from a data table with objects in rows and attributes in columns a taxonomic data structure called concept lattice, which can be used for many purposes, especially for Knowledge Discovery and Information Retrieval. The Formal Concept Analysis Meets Information Retrieval (FCAIR) workshop collocated with the 35th European Conference on Information Retrieval (ECIR 2013) was intended, on the one hand, to attract researchers from FCA community to a broad discussion of FCA-based research on information retrieval, and, on the other hand, to promote ideas, models, and methods of FCA in the community of Information Retrieval

Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2014)

International audienceThis is the third edition of the FCA4AI workshop, whose first edition was organized at ECAI 2012 Conference (Montpellier, August 2012) and second edition was organized at IJCAI 2013 Conference (Beijing, August 2013, see http://www.fca4ai.hse.ru/). Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification that can be used for many purposes, especially for Artificial Intelligence (AI) needs. The objective of the workshop is to investigate two main main issues: how can FCA support various AI activities (knowledge discovery, knowledge representation and reasoning, learning, data mining, NLP, information retrieval), and how can FCA be extended in order to help AI researchers to solve new and complex problems in their domain