Αναθεώρηση Γνώσης σε Περιβάλλοντα Σημασιολογικού Ιστού

Towards the realization of the vision of the Semantic Web, one of the most significant tasks to be performed is the transformation of current human-oriented Web information into machine-processable Web information. In this direction, standards have been adopted in order to structure the data (XML) and to describe the semantics of the data (meta-data expressed in RDF). RDF is a data model and along with the RDF Schema, which defines the vocabulary of this model, they form a mechanism which provides a formal, machine processable representation of knowledge. However the nature of world is dynamic and as the world changes, the knowledge itself, or our view of it, is subject to changes. Consequently, modeling a dynamic world means encapsulating a mechanism for updating knowledge. The algorithms dealing with the incorporation of new knowledge in an ontology (ontology evolution) often share a rather standard process of dealing with changes. We acknowledge that this process consists of the specification of the language, the determination of the allowed update operations, the identification of the invalidities that could be caused by each such operation, the determination of the various alternatives to deal with each such invalidity, and, finally, some (manual or automatic) selection mechanism that allows singling out the “best” of these alternatives. Unfortunately, most ontology evolution algorithms implement these steps using a case-based, ad-hoc methodology, which is cumbersome and error-prone. Knowledge updating is a problem which has been thoroughly examined in the field of Artificial Intelligence under the term belief change. One key idea in the belief change field is that an update operation should produce an updated belief which is as close as possible to the original belief. This approach is often described as minimal change approach. Trying to define “minimal” a lot of propositions have been made, among which is the definition of an ordering of the possible update results. This work presents a framework for updating knowledge, where both the initial knowledge and the update are expressed in a special subset of First Order Logic. Updating is based on a well-formed set of Integrity Constraints on this logic and a predefined ordering between the possible update results. Using this framework we apply this updating mechanism to a specific application: the RDF/S language. We define a model to express RDF language in terms of First Order Logic; an ordering between possible update results and build optimizations of the framework’s updating techniques based on RDF’s particular set of integrity constraints. Through the application of our framework’s techniques on RDF/S we express how the peculiarities of a specific language (which can be expressed with First Order Logic) could be used to optimize the proposed framework for the specific case. On the practical side, we speedup our general-algorithm by developing several, special per-operation, versions of it, which are also formally equivalent to it. We also discuss a number of issues raised during the implementation of the algorithm in a real-world environment