Michal Zawadzki

The cartographer algorithm for processing and querying description logics ontologies

One of the most popular formalisms of describing knowledge is Description Logics. It has become even more popular when OWL standard has emerged. The paper presents a new reasoning algorithm – the Cartographer Algorithm – that allows for inferring implicit knowledge from a terminology (TBox) and assertions (ABox) of an ontology. The paper describes the way of processing ontologies in terms of binary signatures and an efficient way of querying ontologies containing numerous individuals. In addition, results of experiments comparing the Cartographer Algorithm with other reasoners are presented.

The knowledge cartography – a new approach to reasoning over description logics ontologies

The paper presents a new method for representation and processing ontological knowledge – Knowledge Cartography. This method allows for inferring implicit knowledge from both: terminological part (TBox) and assertional part (ABox) of a Description Logic ontology. The paper describes basics of the method and gives some theoretical background of the method. Knowledge Cartography stores and processes ontologies in terms of binary signatures, which gives efficient way of querying ontologies containing numerous individuals. Knowledge Cartography has been applied in KaSeA – a knowledge management system that is being developed in course of a European integrated research project called PIPS. Results of efficiency experiments and ideas of further development of the system are presented and discussed.

Managing Data from Heterogeneous Data Sources Using Knowledge Layer

In the process of data integration using ontologies it is important to manage data from external data sources in the same way as data stored in the Knowledge Base. In previous papers [1], [2] the way of inference from data stored in the Knowledge Base, using Knowledge Cartography idea has been presented. However, this solution requires loading all data to the Knowledge Base. The solution presented in this paper shows how the Knowledge Cartography can be used to infer from data stored in external data sources, without loading them to the Knowledge Base. The presented solution is to enrich each data source with an additional layer that allows managing data using signatures. The paper additionally describes the results of tests comparing times of inference when data are loaded to the Knowledge Base and when data are fetched on demand.

Processing and Querying Description Logic Ontologies Using Cartographic Approach

Description Logic (DL) is a formalism for knowledge representation that recently has gained widespread recognition among knowledge engineers. After a brief introduction to DL, the paper presents a DL reasoner developed at Gdansk University of Technology (GUT). The reasoner, called KaSeA, is based on an original idea called Knowledge Cartography. The paper presents basics of Knowledge Cartography, its potentials and limitations, and compares the solution with other DL reasoners.

Defining trustworthiness in Semantic Web by ontological assertions

The paper introduces the model of knowledge management system that handles some aspects of trustworthiness. A method of defining trustworthiness in semantic Web and reasoning over knowledge enriched with trust information is presented. The method provides a way of building trustworthiness information into the assertional part of an ontology. Defining trustworthiness is meant as assigning a set of features to unary and binary assertions. The model defines a ldquotrust ontologyrdquo - an ontology that allows for assigning some features to specified assertions. There is also explained how Semantic Web agents could express their requirements for trust while querying a knowledge management system. The paper concludes with some implementation aspects of a prototype system that incorporates the presented model.