Inferential Ontology Learning

The purpose of this document is to give an overview of author's prospective doctoral thesis in terms of goals, plans, adopted methodology and current achievements. The thesis' general focus is the Semantic Web, AI, automatic ontology acquisition and reasoning. When considering ontologies as general knowledge repositories, ideally reflecting substantial amount of information present on the web, it is obvious that developing them purely manually is infeasible task not only due to the extensive size of data, but also due to the highly dynamic nature of the environment. Therefore the need for automated methods of ontology creation and maintenance is well acknowledged in the community. However, there has been no explicit support for automatically learned ontologies in the main branches of research concerning inference in the Semantic Web. We believe that efforts leading to bridging these two rather disparate lines of research are more than worthwhile and will prove beneficial for both automated onto-logy development and reasoning, considering the noisy, context-dependent and inconsistent character of mainly unstructured web data we have to deal with when making the Semantic Web real. The nature of this knowledge is hard to be captured by traditional (logical) reasoning paradigms that usually require quite extensively (and expensively) specified descriptions in order to allow any usable reasoning. We plan to develop an alternative formal semantics of the Semantic Web data and implement respective reasoning tool prototype that would be able to deal with this situation better in the context of ontology learning. This is reflected in the tentative thesis' title A Non-traditional Inference Paradigm for Learned Ontologies. Within implementation of the thesis topic prototype, we adhere to these required features: – the ability to refine the learned knowledge on the fly by incorporation of a specific reasoning paradigm and respective tools; – a query-processing mechanism able to infer valuable and useful knowledge from learned ontologies by tools basing on the same reasoning paradigm;