Wikipedia is often referred to as the best example of collective knowledge creation, folksonomies, and the wisdom of the crowds. UDC, on the other hand, is a classic example of a knowledge order designed and updated by defined expert groups. In this study, we have shown that a simple mapping between Wikipedia and UDC category structure is problematic, firstly due to the nature of the act of classification itself. Secondly, the differences in the structure and distribution of both systems add new problems to this process. To draw attention to the resulting ambiguity and problems of such translations, we have demonstrated that a simple approach based on domain knowledge and background created highly controversial ‘left-over’ categories. A keyword search in the UDC database clarified the position of some ambiguous categories but required manual adjustment. Even this adjustment was not enough to properly assign some categories, so we applied a second keyword analysis to find UDC main class keywords in Wikipedia categories. While this stage solved some of the problems, we freely admit that the results are far from perfect.

The pivotal piece of information that enables knowledge bases to perform in this way is a coherent knowledge graph of concepts and entity types. As I have discussed many times, the native category structure of Wikipedia (and all other commonly used KBs) leaves much to be desired. It is one of the major reasons we are reorganizing KB content using the UMBEL reference knowledge graph [1]. The ultimate requirement for the governing knowledge graph (ontology) is that it be logical, consistent and coherent. It is from this logical structure that we can provide the rich semsets [2] for semantic matches, make inferences, understand relatedness, and make disjointedness assertions. In the context of knowledge-based artificial intelligence (KBAI) applications [3], the disjointedness assertions are especially important to aiding the creation of negative training sets based on knowledge supervision.

An attempt to shine light on the interface between technology and knowledge through the case of Wikipedia. My basic claim is that Wikipedia can be understood not just as an online encyclopedia, but as a form. This form, I will claim, is geared at the formation of knowledge and its catalogization, and is found to be deeply linked to the technology it uses, thus revealing a rarely seen interface between the process of knowledge production and information technology. The crux of my argument is that Wikipedia can be understood as a logical structure - comprised of codes and procedures - built to represent knowledge and form the discourse of its creation. As a structure consciously geared at portraying knowledge in the form of both articles and a database, I will claim, Wikipedia visually gives shape to the attempt to link the database technology with the social process of encyclopedic production.

We propose a method for semi-automatic construction of an ontology of a given branch of science for measuring its evolution in time. The method relies on a collection of documents in the given thematic domain. We observe that the words of different levels of abstraction are located within different parts of a document: say, the title or abstract contains more general words than the body of the paper.

This paper presents an overview of the ways in which genres, or structural forms, develop in a community of practice, in this case, Wikipedia. Firstly, we collected data by performing a small search task in the Wikipedia search engine (powered by Lucene) to locate articles related to global car manufacturers, for example, British Leyland, Ferrari and General Motors. We also