One focus of data analysis in formal concept analysis is attribute-significance measure, and another is attribute reduction. From the perspective of information granules, we propose information entropy in formal contexts and conditional information entropy in formal decision contexts, and they are further used to measure attribute significance. Moreover, an approach is presented to measure the consistency of a formal decision context in preparation for calculating reducts. Finally, heuristic ideas are integrated with reduction technique to achieve the task of calculating reducts of an inconsistent data set.
Ontology is widely used in the computer domain to structure concepts that represent a view of world nowadays, which could formally specify semantic relationship among the terms. In this paper, we present coordination between agent crawlers based on ontology in Topic Specific Search Engines, and we try to measure understanding among them, relying on Formal Concept Analysis (FCA) instead of comparing the terms only. In literature, most papers on concept similarity in FCA are based on two different concepts in the same concept lattice, and whereas there is very little research related to different concept lattices or even different agents. We propose a novel method on concept similarity for computing the Concept-Concept similarity, the Concept-Ontology similarity and the Ontology-Ontology similarity, and at last we can deduce understanding among agent crawlers. Finally, we can guide the crawlers effectively in our Search Engine.