Formal Concept Analysis (FCA) is a concept clustering approach that has been widely applied in ontology learning. In our work, we present an innovative approach to generating information context from a tentative domain specified scientific corpus and mapping a concept lattice to a formal ontology. The application of the proposed approach to Semantic Web search demonstrates this automatically co...

Computer modeling of activities such as concept-learning, conceptual blending, discovery, analogy and creativity has greatly expanded our understanding of possible mechanisms of conceptualization. Unfortunately, it has also meant that theoretical assessment of conceptual possibilities has become problematic. Determining what concepts can be constructed by what mechanisms in what contexts is far...

The impact of the existence and nature of multi-authorship on the visibility of research results is a relevant issue in the framework of the monitoring and evaluation of scientific performance. Multi-authorship involving researchers from different institutions is a growing trend typical of today’s social, economic and political development and an expression of the so-called “internationalizatio...

The organizational structure is usually defined using the best experience and there is a minimum of formal approach involved. This paper shows the possibilities of the theory of concept analysis that can help to understand organizational structure based on solid mathematical foundations. This theory is extended by the concept of knowledge sharing and diversity that enables to evaluate the organ...

ion: FC1 Φ FC2, which represents a more generic concept whose intension is the intersection CNTX1 ∩ CNTX2, and the extension the union W1 ∪W2. The clustering method we will describe in the following section makes use of these two operations. The resulting concepts generated by such operations give rise to a concept lattice. The more balanced concepts in that lattice will be the startpoint (i.e....

The fields of cluster analysis and concept analysis are both used to identify patterns in data. Concept analysis identifies similarities between sets of objects based on their attributes. Cluster analysis groups objects with related characteristics based on some notion of distance. In this paper, we investigate connections between these two approaches. In particular, for each binary relation de...