The effort described in this paper introduces an indexing structure for path search in the graph structured data called rho-index. It is based on a graph segmentation S(G) that is meant to represent the indexed graph G in a simpler manor yet having similar properties as the graph G had. This is achieved using graph transformations and a special type of a matrix used to represent the transformed graph.

Kernel based learning methods are subject of great interest at present. We discuss two kernel based learning methods, namely the Regularization Networks (RN) and the Radial Basis Function Network (RBF networks). The RNs are derived from the regularization theory, had been studied thoroughly from a function approximation point of view, and therefore have very good theoretical background. The RBF networks represent a model of artificial neural networks with both neuro-physiological and mathematical motivation. In addition they may be treated as a generalised form of Regularization Networks, i.e. RN with increased number of kernel functions. We demonstrated the performance of both approaches on experiments, including both benchmark and real-life learning tasks. We claim that the performance of RN and RBF network is comparable in terms of generalisation error. The RN approach usually leads to solutions with higher model complexity (high number of base units). In this situations, the RBF networks can be used as a ’cheaper’ alternative.

Integration has been an acknowledged data processing problem for a long time. However, there is no universal tool for general data integration. Because various data descriptions, data heterogeneity, and machine unreadability, it is not easy way. Improvement in this situation could bring the Semantic Web. Its idea is based on machine understandable web data, which bring us an opportunity of better automated processing. The SemanticWeb is still a future vision, but there are already some features we can use. The paper describes how is integration solved in mediation integration system VirGIS and discusses use of nowadays Semantic Web features to improve it. According to the proposed changes, a new ontology that covers data used in VirGIS is presented.

Abstract Logic is the foundational discipline of many sciences. Part mathematics, part philosophy and part computing science, logic remains a core intellectual study and is increasingly relevant to practical concerns. It spreads into planning, into program synthesis, into circuit design and into discourse analysis. It underpins the entire science of artiŻcial intelligence. In order to increase knowledge from the field of logic, I participated in the Logic Summer School. This report covers some information.

Knowledge representation in geographic information systems (GIS) and associated data processing presents many challenges for researchers. To use ontologies as knowledge representation belongs to the most topical problems to solve. This involves ontology development as well as ontology re-usage. The goal of the research described in this paper is to develop a specific ontology for a given GIS area.

Ontologies play an important role in knowledge representation. Among various fields, where ontologies can be useful, is the GIS data area. We consider data in a specific GIS domain and develop a new ontology. The result is described in this paper.

Integration has been an acknowledged data processing problem for a long time. However, there is no universal tool for general data integration. Because various data descriptions, data heterogeneity, and machine unreadability, it is not easy way. Improvement in this situation could bring the Semantic Web. Its idea is based on machine understandable web data, which bring us an opportunity of better automated processing. The SemanticWeb is still a future vision, but there are already some features we can use. The paper describes how is integration solved in mediation integration system VirGIS and discusses use of nowadays Semantic Web features to improve it. According to the proposed changes, a new ontology that covers data used in VirGIS is presented.

Ontologies play an important role in a knowledge representation. It involves ontology development as well as ontology re-use. Among various fields, where ontologies can be useful, is the GIS (Geographical Information System) data area. The goal of the research described in this paper is to develop a specific ontology for a given GIS domain. At first, we describe a general methodology and main tools for ontology development. Then a new ontology that covers data used in a VirGIS integration system is presented. The paper describes the VirGIS specified ontology as well as a list of spatio-temporal data ontologies that are available and possible to use for a general data features description.

The paper deals with expressing preferences in the framework of the relational data model. Preferences have usually a form of a partial ordering. Therefore the question arises how to provide the relational data model with such an ordering.

We study and design multi-agent systems for computational intelligence modeling. Agents typically reside in a high-performance parallel environment, such as a cluster of computational nodes, and utilize a non-blocking asynchronous communication. The need of accurate predictions of run-time and other characterizations of complex parallel asynchronous processes bring us to design a new parallel model creation methodology. In this article our approach is briefly described and a test case is shown and discussed.

This paper presents BOLE — a new platform for bottomup generation and merging of bio-ontologies. In contrast to other ontology-learning systems that are currently available, BOLE can be characterized by the modular architecture enabling integrating and comparing various methods of the automatic acquisition of semantic relations. We introduce the architecture of the tool and discuss the methodology of the employed synthetic bottom-up approach. OLITE — the central component responsible for the automatic acquisition of semantic relations from texts is described in detail. The presented preliminary results prove the efficiency of the implemented framework. We also provide a brief comparative overview of other relevant approaches and outline the future work on representation of uncertain knowledge for bio-ontology merging.

This paper presents OLE — a new platform for bottom-up generation and merging of ontologies. In contrast to other ontology-learning systems that are currently available, OLE can be characterized by the modular architecture enabling integrating and comparing various methods of the automatic acquisition of semantic relations. We introduce the architecture of the tool and discuss the methodology of the employed synthetic bottom-up approach. OLITE — the central component responsible for the automatic acquisition of semantic relations from texts is described in detail. The presented preliminary results prove the efficiency of the implemented framework. We also provide a brief comparative overview of other relevant approaches and outline the future work on representation of uncertain knowledge for ontology merging.

The paper describes a method for relational data model estimation from input web data and usage of this method. It includes also its principal limitations and shows the model usage for a more effective storage into a repository. The repository is implemented as the universal relation. The properties of the model are described as well.

Metoda odhadování struktury dat spojuje vizi sémantického webu a dnešní webové datové zdroje, které převážně neobsahují žádnou doprovodnou sémantiku prezentovaných informací. Aby bylo možné tyto zdroje použít pokročilými nástroji sémantického webu, je potřeba sémantiku prezentovaných dat alespoň odhadnout. Příspěvek popisuje takovou metodu, ukazuje její použití pro úlohy induktivního logického programování a jmenuje výhody použití pravidlových systémů pro její implementaci.

Odhadování struktury dat je jednou z možností, jak automatizovaným způsobem interpretovat data. Ta mohou být popsána pomocí modelu funkčních závislostí, vytváření takového modelu lze srovnat s některými technikami strojového učení. Tento příspěvek shrnuje vybrané základní techniky induktivního logického programování a analyzuje je z pohledu metody odhadování struktury dat. Ukazuje se, že techniky induktivního logického programování lze v některých případech převést právě odhadování struktury dat.

The paper describes a possible treatment of sharing data in a large network of users. The mathematical model is based on weighted hypergraphs whose nodes and edges denote the users and their relations, respectively. Its flexibility guarantees to have basic relations between users robust under frequent changes in the network connections. Approach copes with the communication/computing issues from different point of view based on a structure evolution and its further optimization in sense of keeping the parallel space and time complexities low. Although the idea is aimed to the field of mobile computing, it can be generalized in straightforward way to other similar environment. An experimental application is also proposed and discussed in the paper.

The paper describes selected problems and possible solutions for the position management in mobile computing. A proposed scheme extends existing approaches. The main idea is to reduce amount of possible solutions given by a movement prediction algorithm by constrains ubiquitously found in the real-life. Existing solutions and possibilities for a future research are also described.