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Designing and refining ontologies becomes a tedious task, once the boundary to real-world-size knowledge bases has been crossed. Hence semi-automatic methods supporting those tasks will determine the future success of ontologies in practice. In this paper we describe a way for ontology creation and refinement by combining techniques from natural language processing (NLP) and formal concept analysis (FCA). We point out how synergy between those two fields can be established thereby overcoming each other’s shortcomings.

It is known that implications in powerset-based closure systems correspond to Horn approximations in propositional logic frameworks. Here we focus on the problem of implications between injective partial orders. We set up the definitions that allow one to apply standard constructions of implications, and formally characterize the propositional theory obtained. We describe also some experimental applications of our development.

Browsing images using Formal Concept Analysis (FCA) for conceptual representation, navigation and clustering was shown in the ImageSleuth projects [1,2,3]. To showcase the ideas and knowledge gained through ImageSleuth, the same techniques were applied to an information space built from a dynamic collection sourced from the Amazon.com on-line store. Using the Amazon.com catalog, conceptually similar DVDs are able to be discovered and viewed, and then used to explore the information space of their conceptual neighbourhood. A case study of the project – called DVDSleuth – is presented in this paper focusing on the history, results and difficulties encountered by the project. The shortcomings of our approach are analysed and reported as a guide for future projects using FCA techniques for information exploration using on-line Web catalogs.

An important part of knowledge-based systems for helpdesk is a database of incidents that were solved in the past. A major part of the reported incidents is related to relatively small sets of repeated problems. It is important that these repeated problems are correctly recognized and their solutions are stored. In order to solve the new incident we can use the experience of solving a similar previously reported problem. Such a process is known as Case Based Reasoning (CBR). In practice however, it is not always immediately clear what the problem is about. It is due to the fact that different customer may describe the same problem in a different way. In this paper we describe an application of the helpdesk system which is based on Formal Concept Analysis (FCA), and enables to find analogous incidents by using navigation within the concept lattice.

The concept of Chu correspondences between formal contexts is introduced. The construction of formal concepts induces a functor from the category of Chu correspondences to the category of sup-preserving maps between complete lattices. It turns out that the category has a *-autonomous category structure and the functor is shown to preserve the *-autonomous category structure. Details are given in [4].

New technologies based on meta-models and ontology engineering allow the formalization and conceptualization of the components that take part in the process of collaboration and interaction analysis in CSCL (Computer-Supported Collaborative Learning) environments. In this article, a proposal to characterize the process of analysis of solutions in CSCL environments is made by means of an ontology. These solutions are built by the learners through a process of collaboration following a problem solving approach.