Concept Relation Discovery and Innovation Enabling Technology (CORDIET)
This is an incremental tool for researchers or analysts needing to discover concept relations from text data.
The paper tackles the problem of extracting new knowledge from unstructured text data by introducing CORDIET, a toolbox that uses C-K theory and methods like Formal Concept Analysis and Emergent Self Organizing Maps, enabling users to define attributes and cluster objects for analysis.
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.