Discovering the complex relationships between entities is one way of benefitting from the Semantic Web. This paper discusses new approaches to implementing rho-operators into RDF querying engines which will enable discovering such relationships viable. The cornerstone of such implementation is creating an index which describes the original RDF graoh. The index is created by recursive application of a transformation of graph to forest of trees and then to each tree its extended signature is created. The signatures are accompanied by the additional information about transformed problematic nodes breaking the tree structure. The components described by the signatures are assumed as a single node in the following step. The transitions between the signatures represent edges.

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.

In this paper a correlation between rho-operators and indirect relations in citation analysis is presented. The rho-operators were defined to explore complex relationships in graph structured data. Various direct and indirect relations identified in citation analysis are used to study the semantics within the citation network. The rho-index was used to implement the rho - path search in the citation network and gained results are evaluated and discussed.

Idea sémantického webu je široce diskutována mezi odbornou veřejností již mnoho let. Přestože je vyvinuta řada technologií, jazyků, prostředků a dokonce i softwarových nástrojú, málokdo někdy nějaký reálný sémantický web viděl. Za jeden z hlavních dùvodù tohoto stavu považujeme neexistenci potřebné infrastruktury pro provoz sémantického webu. V našem článku popisujeme návrh takové infrastruktury, která je založena na využití a rozšíření technologie datového stohu a nástrojích pro něj vyvinutých a jejich kombinaci s webovými vyhledávači a dalšími nástroji a prostředky.

Basic storage and querying of RDF data using a relational database can be done in a very simple manner. Such approach can run into trouble when used on large and complex data. This paper presents such data and several sample queries together with analysis of their performance. It also describes two possible ways of improving the performance based on this analysis.

The Semantic Web is not widespread as it has been expected by its founders. This is partially caused by lack of standard and working infrastructure for the Semantic Web. We have built a working, portable, stable, highperformance infrastructure for the Semantic Web. This paper is focused on tasks performed by the infrastructure.

The Semantic Web is not widespread as it has been expected by its founders. This is partially caused by lack of standard and working infrastructure for the Semantic Web. We have built a working, portable, stable, high-performance infrastructure for the Semantic Web. This enables various experiments with the Semantic Web in the real world.

We have developed and implemented [1,2] infrastructure and RDF storage for the Semantic Web. When we filled it with data the need for some tool that could explore the data became evident. Unfortunately, none of existing solutions fulfills requirements imposed by the data and users expectations. This paper presents our RDF visualizer that was designed specifically to handle large RDF data by means of incremental navigation. A detailed description of the algorithm is given as well as actual results produced by the visualizer.

Uncertainty querying of large data can be solved by providing top-k answers according to a user fuzzy ranking/scoring function. Usually different users have different fuzzy scoring function a user preference model. Main goal of this paper is to assign a user a preference model automatically. To achieve this we decompose user’s fuzzy ranking function to ordering of particular attributes and to a combination function. To solve the problem of automatic assignment of user model we design two algorithms, one for learning user preference on particular attribute and second for learning the combination function. Methods were integrated into a Fagin-like top-k querying system with some new heuristics and tested.

User preferences became recently a hot topic. The massive use of internet shops and social webs require the presence of a user modelling, which helps users to orient them selfs on a page. There are many different approaches to model user preferences. In this paper, we will overview the current state-of-the-art in the area of acquisition of user preferences and their induction. Main focus will be on the models of user preferences and on the induction of these models, but also the process of extracting preferences from the user behaviour will be studied. We will also present our contribution to the probabilistic user models.

We discuss models of user preferences in Web environment. We construct a model for user preference querying over a number of data sources and ordering of answers by a combination of particular attribute rankings. We generalize Fagin's algorithm in two directions - we develop some new heuristics for top-k search in the model without random access and propose a method of ordering lists of objects by user fuzzy function. To enable different user preferences our system does not require objects to be sorted - instead we use a B+- tree on each of the attribute domains. This leads to a more realistic model of Web services. We implement our methods and heuristics for search of top-k answers into Tokaf middleware framework prototype. We describe experiments with Tokaf and compare different performance measures with some other methods.

Web search heuristics based on Fagin’s threshold algorithm assume we have the user profile in the form of particular attribute ordering and a fuzzy aggregation function representing the user combining function. Having these, there are sufficient algorithms for searching top-k answers. Finding particular attribute ordering and aggregation for a user still remains a problem. In this short paper our main contribution is a proof of concept of a new iterative process of acquisition of user preferences and attribute ordering .

EGOTHOR is a search engine that indexes the Web and allows us to search the Web documents. Its hit list contains URL and title of the hits, and also some snippet which tries to shortly show a match. The snippet can be almost always assembled by an algorithm that has a full knowledge of the original document (mostly HTML page). It implies that the search engine is required to store the full text of the documents as a part of the index. Such a requirement leads us to pick up an appropriate compression algorithm which would reduce the space demand. One of the solutions could be some use of common compression methods, for instance gzip or bzip2, but it might be preferable to develop a new method which would take advantage of the document structure, or rather, the textual character of the documents. There already exist special compression text algorithms and methods for a compression of XML documents. The aim of this paper is an integration of the two approaches to achieve an optimal level of the compression ratio.

Multi-agent systems typically utilize a non-blocking asynchronous communication in order to achieve required flexibility and adaptability. High performance computing techniques exploit the current hardware ability of overlapping asynchronous communication with computation to load the available computer resources efficiently. On the contrary, widely used parallel processes modeling methodologies do not often allow for an asynchronous communication description. At the same time those models do not allow their user to select the granularity level and provide only a fixed set of machine and algorithm description quantities. In this work we addressed this issue and designed a new parallel processes modeling methodology. Its main features include an open set of atomic operations that are calculated and predicted for the algorithm in question, and the computer aided semi-automatic measuring of operation counts and approximation of cost functions. This allows not only for tuning the model granularity as well as accuracy according to user needs, but also to reach a such description complexity that would be very difficult to obtain without any computer aid. We demonstrated that our approach gives good results on the parallel implementation of a selected generalized genetic algorithm. A model was constructed and its predictions compared with the reality on various computer architectures, including one parallel cluster machine. We also designed and implemented an open multi-agent system suitable for the above mentioned experiments and many others. This system synthesizes the areas of high performance computing, multi-agent systems and computational intelligence into an efficient and flexible means of running experiments.

We discuss two approaches to supervised learning, namely regularization networks and RBF networks, and demonstrate their performance on experiments. We claim that the performance of these two models is comparable, so the RBF networks can be used as a cheaper alternative to regularization networks.

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.

In this paper we study learning with Regularization Networks (RN). RN are feedforward neural networks with one hidden layer. Since they have a very good theoretical background, we study their practical aspects and applicability. On experiments we demonstrate the role of the regularization parameter, compare RN with different kernels and parameter settings on benchmark data sets. Then we apply RN to a problem of a flow rate prediction, real data from Czech river Sázava are used. All experiments were made using the system Bang.

The problem of supervised learning is a subject of great interest at present. It covers wide range of tasks, such as various classification, prediction, or forecasting problems, i.e. problems that also often arise in semantic web applications. We study one approach to this problem - regularization networks. We introduce composite types of kernel functions, sum kernels and product kernels. On experiments we demonstrate the role of the regularization parameter and kernel function in the regularization network learning, and compare networks with different types of kernel functions.

Syllable based text compression is a new approach to compression by symbols. In this concept syllables are used as the compression symbols instead of the more common characters or words. This new technique has proven itself worthy especially on short to middle-length text files. The effectiveness of the compression is greatly affected by the quality of dictionaries of syllables characteristic for the certain language. These dictionaries are usually created with a straight-forward analysis of text corpora. In this paper we would like to introduce an other way of obtaining these dictionaries using genetic algorithm. We believe, that dictionaries built this way, may help us lower the compress ratio. We will measure this effect on a set of Czech and English texts.

The Burrows-Wheeler Transform (BWT) is a compression method which reorders an input string into the form, which is preferable to another compression. Usually Move-To-Front transform and then Huffman coding is used to the permutated string. The original method [3] from 1994 was designed for an alphabet compression. In 2001, versions working with word and n-grams alphabet were presented. The newest version copes with the syllable alphabet [7]. The goal of this article is to compare the BWT compression working with alphabet of letters, syllables, words, 3-grams and 5-grams.

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.

World Wide Web obsahuje data, která jsou pro počítačové programy nesrozumitelná. Následkem toho je na něm obtížné některé věci zautomatizovat. Nedostatky současného webu by měl odstranit sémantický web, ve kterém data budou mít přesně popsaný význam. Zlepšení může přinést také v oblasti integrace, která je v případě dat pocházejících z webu velmi obtížná. Tento článek se zabývá integrací webových dat. Zaměřuje se na relační data ve formátu XML a navrhuje postupy základních integračních operací.

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.

A crucial point in automated data processing is the way in which the data are expressed. One possibility is to employ existing features of the Semantic Web - ontologies. Ontologies play an important role in a knowledge representation. The aim of the research presented in this paper is to provide more automated VirGIS system. VirGIS is an integration system that works with GIS (Geographical Information Systems) data. As a first step of our research, we describe its data using common Semantic Web techniques and build a VirGIS ontology.

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.

Integration has been an acknowledged problem for a long time. With the aim at combining data from different sources, data integration usually provides a unified global view over these data. A crucial part of the task is the establishment of the connection between the global view and the local sources. Two basic approaches have been proposed for this purpose: Global As View (GAV) and Local As View (LAV).With the Semantic Web and its data description means, there is also another possibility - to employ ontologies for the relationship description in an integration system.

Information Retrieval (IR) as a process of searching relevant information is a significant discipline of a data processing field. European Summer School in Information Retrieval ESSIR provides students, academic and industrial researchers and developers a grounding in the core objects of IR (models, architectures, algorithms), as well as covering some current topics, e.g. information retrieval from the Web. We have participated its 5th year that was held at Dublin City University in Dublin, Ireland.

Datová integrace je uznávaný problém v oblasti zpracování dat. Jejím cílem je obvykle poskytnout ucelený pohled na několik datových zdrojů. V případě nematerializovaného řešení je klíčové stanovení vazeb mezi poskytovaným virtuálním pohledem a daty uloženými ve zdrojích. článek se zabývá řešením stanovení těchto vazeb. Svůj přístup zakládá na ontologiích.

A key point in modern automated data processing is metadata semantics representation. Employing Semantic Web existing features - ontologies - is a promising option. Ontologies open a novel approach to knowledge representation. The paper presents a GIS (Geographic Information System) domain application illustrating ontological approach to data integration and data processing automation in the specific system. This VirGIS system is an integration system that works with spatio-temporal data. We start our study with developing the data representation based on common Semantic Web techniques and build a VirGIS ontology.

Data integration usually provides a unified global view over several data sources. A crucial part of the task is the establishment of the connection between the global view and the local sources. For this purpose, two basic mapping approaches have been proposed: GAV (Global As View) and LAV (Local As View). On the Semantic Web, there can be considered also an ontological approach. In this paper, data integration is solved using ontologies of the sources. To express relationships between the global view and local source schemas, an ontology for the integration system is built. Thus, a schema integration task is transformed to an ontology merging task.

V tomto příspěvku je popsán přístup k virtuální integraci dat využívající současných principů, metod a nástrojů sémantického webu. Přístup pracuje s daty ve formátu RDF a předpokládá dostupnost ontologií, které je popisují. Ontologie jsou základem pro všechny kroky prezentovaného integračního procesu. Jsou využity jak k určení vztahů mezi daty a poskytovaným integrovaným pohledem, tak i k zápisu nalezených korespondencí. Ty jsou dále použity při zpracování dotazů kladených na integrovaná data.

The paper deals with one step of non-materialized data integration - schema matching task. It works with data sources on the Semantic Web; the crucial assumption for the considered task is available ontologies describing data to integrate. Source ontologies are used to find correspondences between source schemas elements. For this, also techniques known from ontology alignment and ontology merging field are used.

Článek se zabývá úlohami, které je třeba řešit při nematerializované integraci dat. Zaměřuje se na hledání korespondencí mezi schématy a mapování schémat. Návrh přístupu řešení těchto úloh na Sémantickém webu těží z dostupných ontologiích popisujících integrované zdroje. Ontologie jsou využity jak k hledání mapování, tak i při jejich popisu.

Článek se zabývá přístupem, jak se pokusit zautomatizovat mnohdy netriviální úlohu nalezení pravidel pro integraci dat. Předkládaný přístup automaticky generuje kandidáty pravidel včetně jejich ohodnocení pomocí nepřímé míry definující jejich prioritu. Priorita může následně být použita buďto návrhářem (člověkem) jako pomocný prvek pro přípravu návrhu, nebo při automatickém návrhu integračního procesu zahrnující pravidla s maximální prioritou. Studie v příspěvku se detailně věnuje dvěma základním typům pravidel, ekvivalenci a hierarchii, přičemž ohodnocení kandidátů je založeno na (strukturální) analýze aktivních domén atributů. V neposlední řadě příspěvek ukazuje možnost decentralizovaného přístupu k integraci dat, jenž je inspirován webovými technologiemi.

As XML has undoubtedly become a standard for data representation, it is inevitable to propose and implement techniques for efficient managing of XML data. A natural alternative is to exploit features and functions of (object-)relational database systems, i.e. to rely on their long theoretical and practical history. The main concern of such techniques is the choice of an appropriate XML-to-relational mapping strategy. In this paper we focus on enhancing of user-driven techniques which leave the mapping decisions in hands of users. We propose an algorithm which exploits the user-given annotations more deeply searching the user-specified "hints" in the rest of the schema and applies an adaptive method on the remaining schema fragments. We describe the proposed algorithm, the similarity measure designed for this purpose, sample implementation of key features of the proposal called UserMap, and results of experimental testing on real XML data.

Since XML becomes a crucial format for representing information, it is necessary to establish techniques for managing XML documents. A possible solution can be found in storing XML data in (object-)relational databases. For this purpose most of the existing techniques often exploit an XML schema of the stored XML data, usually expressed in DTD. But the more complex today’s applications are, the more insufficient the DTD becomes and the necessity to use XML Schema language becomes more essential. The paper proposes an algorithm for mapping XML Schema structures to an object-relational database schema (defined by the SQL:1999 standard) using a (modified) DOM interface and an algorithm for storing the valid XML data into relations of the resulting schema. The main aim is to exploit object-oriented features XML Schema has and the advantages of object-relational databases and to preserve the structure as well as semantic constraints of the source schema in the target schema.

Recently XML has achieved the leading role among languages for data representation and thus we can witness a massive boom of corresponding techniques for managing XML data. Most of the processing techniques however suffer from various bottlenecks worsening their time and/or space efficiency.We assume that the main reason is they consider XML collections too globally, involving all their possible features, although real data are often much simpler. Even though some techniques do restrict the input data, the restrictions are often unnatural. In this paper we analyze existing XML data, their structure and real complexity in particular.We have gathered more than 20GB of real XML collections and implemented a robust automatic analyzer. The analysis considers existing papers on similar topics, trying to confirm or confute their observations as well as to bring new findings. It focuses on frequent but often ignored XML items (such as mixed content or recursion) and relationship between schemes and their instances.

As the XML has become a standard for data representation, it is inevitable to propose and implement techniques for efficient managing of XML data. A natural alternative is to exploit features of (object-)relational database systems, i.e. to rely on their long theoretical and practical history. The main concern of such techniques is the choice of an appropriate XML-to-relational mapping strategy. In this paper we focus on enhancing of user-driven techniques which leave the mapping decisions in hands of users who specify their requirements using schema annotations.We describe our prototype implementation called UserMap which is able to exploit the annotations more deeply searching the user-specified “hints” in the rest of the schema and applies an adaptive method on the remaining schema fragments. Using a sample set of supported fixed mapping methods we discuss problems related to query evaluation for storage strategies generated by the system, in particular correction of the candidate set of annotations and related query translation. And finally, we describe the architecture of the whole system.

As the popularity of XML as a format for data representation grows the need for storing XML data in an effective way grows as well. Recent research has provide us with effeective solutions based on storing XML data into relational databases and with new technologies based on storing XML data in the native form. However, design of XML databases has not been studied su±ciently yet. In this paper, we suppose a set of XML schemes that describe XML representation of our data in several types of XML documents. We show that we can not usually store the data directly in this representation because it can contain redundancies. To design an optimal database schema we therefore need to locate these redundancies and eliminate them.We describe two types of redundancies in XML data in this paper and show how to utilize a conceptual schema of the XML schemes to locate such redundancies. We also show how to normalize the XML schemes to eliminate these redundancies.

Recently XML is the standard format used for the exchange of data between information systems and is also frequently applied as a logical database model. If we use XML as a logical database model we need a conceptual model for the description of its semantics. However, XML as a logical database model has some special characteristics which makes existing conceptual models as E-R or UML unsuitable. In this paper, the current approaches to the conceptual modeling of XML data are described in an uniform style. A list of requirements for XML conceptual models is presented and described approaches are compared on the base of the requirements.

Recently XML is the standard format used for the exchange of data between information systems and is also frequently applied as a logical database model. If we use XML as a logical database model we need a conceptual model for the description of its semantics. However, XML as a logical database model has some special characteristics which makes existing conceptual models as E-R or UML unsuitable. In this paper, the current approaches to the conceptual modeling of XML data are described in an uniform style. A list of requirements for XML conceptual models is presented and described approaches are compared on the base of the requirements.

Recently XML is the standard format used for the exchange of data between information systems and is also frequently applied as a logical database model. If we use XML as a logical database model we need a conceptual model for the description of its semantics. In this paper, we describe our work on a new conceptual model for XML called XSEM created as a combination of several approaches applied in the area of conceptual modeling for XML.

In this paper we briefly describe a new conceptual model for XML data called XSEM. The model is a combination of several approaches in the area of conceptual modeling of XML data. The model divides the process of conceptual modeling of XML data to two levels. On the first level, a designer designs an overall non-hierarchical conceptual schema of a domain. On the second level, he or she derives different hierarchical representations of parts of the overall conceptual schema using transformation operators. These hierarchical representations describe how the data is organized in an XML form.

We propose a new conceptual model for XML data called XSEM as a combination of several approaches in the area of the conceptual modeling for XML. The model divides the conceptual modeling process of XML data to two levels. On the first level, a designer designs an overall non-hierarchical conceptual schema of a domain. On the second level, he or she derives different hierarchical representations of parts of the overall conceptual schema using transformation op- erators. These hierarchical representations describe how the data is organized in an XML form.

In this paper we briefly describe a new conceptual model for XML data called XSEM and how to use it for modeling XML interfaces of services in service oriented architecture (SOA). The model is a combination of several approaches in the area of conceptual modeling of XML data. It divides the process of conceptual modeling of XML data to two levels. The first level consists of designing an overall non-hierarchical conceptual schema of the domain. The second level consists of deriving different hierarchical representations of parts of the overall conceptual schema using transformation operators. Each hierarchical representation models an XML schema describing the structure of the data exchanged between a service interface and external services.

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.

This paper describes an option to express our preferences in the framework of relational databases. Preferences have usually a form of a partial ordering. Therefore the question is how to deliver the semantics of ordering to a database system. The answer is quite straightforward.

The aim of the paper is to present a novel, general approach to preference modelling in the framework of the relational data model. To allow nonmonotonic operations, the preferences are defined between sets of relational instances. The aim is the generalization of the relational algebra that is as minimal as possible, in the sense that the formal fundamentlas of the relational data model are preserved. At the same time, the extended model should be formal enough to provide a sound basis for the investigation of other new preference constructors and operations and for new possible applications.

The aim of the paper is to present a novel, general approach to preference modelling in the framework of the relational data model. The preferences are defined between sets of relational instances, which is a nontrivial generalization of the approach aiming at incorporating ordered attribute domains into the relational data model. The main goals are as follows: an effective representation of information representable by a partial order, an intuitive preference construction and its processing throughout the query execution plan, and a suitable data structure to support it all.

The notion of preference poses a new prospect of personalization of database queries. In addition, it can be exploited to optimize query execution. Indeed, a novel optimization technique involving preference is developed, and its algorithm presented.

The paper resumes recent advances in the field of logic of preference and presents their application in the field of database queries. Namely, non-monotonic reasoning mechanisms including various kinds of preferences are reviewed, and a way of suiting them to practical database applications is shown: reasoning including sixteen strict and non-strict kinds of preferences, inclusive of ceteris paribus preferences, is feasible. However, to make the mechanisms useful for practical applications, the assumption of preference specification consistency has to be relinquished. This is achieved in two steps: firstly, all the kinds of preferences are de- fined so that some uncertainty is inherent, and secondly, not a notion of a total pre-order but a partial pre-order is used in the semantics, which enables to indicate some kind of conflict among preferences. Most importantly, the semantics of a set of preferences is related to that of a disjunctive logic program.

The paper resumes a logical framework for formulating preferences and proposes their embedding into relational algebra through a single preference operator parameterized by a set of user preferences of sixteen various kinds, inclusive of ceteris paribus preferences, and returning only the most preferred subsets of its argument relation. Most importantly, conflicting set of preferences is permitted and preferences between sets of elements can be expressed. Formal foundation for algebraic optimization, applying heuristics like push preference, also is provided: abstract properties of the preference operator and a variety of algebraic laws describing its interaction with other relational algebra operators are presented.

Multi-agent systems typically utilize a non-blocking asynchronous communication in order to achieve required flexibility and adaptability. High performance computing techniques exploit the current hardware ability of overlapping asynchronous communication with computation to load the available computer resources efficiently. On the contrary, widely used parallel processes modeling methodologies do not often allow for an asynchronous communication description. At the same time those models do not allow their user to select the granularity level and provide only a fixed set of machine and algorithm description quantities. In this work we addressed this issue and designed a new parallel processes modeling methodology. Its main features include an open set of atomic operations that are calculated and predicted for the algorithm in question, and the computer aided semi-automatic measuring of operation counts and approximation of cost functions. This allows not only for tuning the model granularity as well as accuracy according to user needs, but also to reach a such description complexity that would be very difficult to obtain without any computer aid.

A multi-agent system targeted toward the area of computational intelligence modeling is presented. The purpose of the system is to allow both experiments and high-performance distributed computations employing hybrid computational models. The focus of the system is the interchangeability of computational components, their autonomous behavior, and emergence of new models.

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.

Ontologies are commonly considered as one of the essential parts of the Semantic Web vision, providing a theoretical basis and implementation framework for conceptual integration and information sharing among various domains. In this paper, we present the main principles of a new ontology acquisition framework applied for semi-automatic generation of scientific portals. Extracted ontological relations play a crucial role in the structuring of the information at the portal pages, automatic classification of the presented documents as well as for personalisation at the presentation level.

Recently, the significance of uncertain information representation has become obvious in the Semantic Web community. This paper presents an ongoing research of uncertainty handling in automatically created ontologies. Proposal of a specific framework is provided. The research is related to OLE (Ontology LEarning), a project aimed at bottom-up generation a nd merging of domain specific ontologies. Formal systems that underlie the uncertai nty representation are briefly introduced. We will discuss a universal internal form at of uncertain conceptual structures in OLE then. The proposed format serves as a basis for inference tasks performed among an ontology. These topics are outlined as motivations of our future work.

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 significance of uncertainty representation has become obvious in the Semantic Web community recently. This paper presents our research on uncertainty handling in automatically created ontologies. A new framework for uncertain information processing is proposed. The research is related to OLE (Ontology LEarning) - a project aimed at bottom-up generation and merging of domain-specific ontologies. Formal systems that underlie the uncertainty representation are briefly introduced. We discuss the universal internal format of uncertain conceptual structures in OLE then and offer a utilisation example then. The proposed format serves as a basis for empirical improvement of initial knowledge acquisition methods as well as for general explicit inference tasks.

Current Semantic Web implementation efforts pose a number of challenges. One of the big ones among them is development and evolution of specific resources—the ontologies—as a base for representation of the meaning of the web. This paper deals with the automatic acquisition of semantic relations from the text of scientific publications (journal articles, conference papers, project descriptions, etc.). We also describe the process of building of corresponding ontological resources and their application for semi–automatic generation of scientific portals. Extracted relations and ontologies are crucial for the structuring of the information at the portal pages, automatic classification of the presented documents as well as for personalisation at the presentation level. Besides a general description of the portal generating system, we give also a detailed overview of extraction of semantic relations in the form of a domain–specific ontology. The overview consists of presentation of an architecture of the ontology extraction system, description of methods used for mining of semantic relations and analysis of selected results and examples.

Ontology learning is one of the essential topics in the scope of an important area of current computer science and artificial intelligence - the upcoming Semantic Web. As the Semantic Web idea comprises semantically annotated descendant of the current world wide web and related tools and resources, the need of vast and reliable knowledge repositories is obvious. Ontologies present well defined, straightforward and standardised form of these repositories. There are many possible utilisations of ontologies - from automatic annotation of web resources to domain representation and reasoning tasks. However, the ontology creation process is very expensive, time-consuming and unobjective when performed manually. So a framework for automatic acquisition of ontologies would be very advantageous. In this work we present such a framework called OLE (an acronym for Ontology LEarning) and current results of its application. The main relevant topics, state of the art methods and techniques related to ontology acquisition are discussed as a part of theoretical background for the presentation of the OLE framework and respective results. Moreover, we describe also preliminary results of progressive research in the area of uncertain fuzzy ontology representation that will provide us with natural and reasonable instruments for dealing with inconsistencies in empiric data as well as for reasoning. Main future milestones of the ongoing research are debated as well.

The significance of uncertainty representation has become obvious in the Semantic Web community recently. This paper presents new results of our research on uncertainty handling in ontologies created automatically by means of Human Language Technologies. The research is related to OLE (Ontology LEarning) a project aimed at bottom-up generation and merging of domain-specific ontologies. It utilises a proposal of expressive fuzzy knowledge representation framework called ANUIC. We discuss current achievements in taxonomy acquisition and outline some interesting applications of the framework regarding non-traditional reasoning perspectives.

Zatímco dílčí ontologie pokrývají jeden pohled na úzce vymezenou oblast, mnohé aplikace vyžadují obecnější přístup k popisovaným datům. Z tohoto důvodu se přistupuje ke srovnávání ontologií (Ontology Matching), které, pokud je to možné, transformuje několik různých ontologických popisů do jediného. Příspěvek popisuje případovou studii takového procesu za využití různých metod, srovnává jejich úspěšnost a diskutuje možnost využití dílčích výsledků k definici výsledné ontologie. Pro experiment byly nezávisle vytvořeny dvě triviální ontologie, které byly různými nástroji a metodami integrovány do jedné.

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.

Příspěvek popisuje provedenou studii proveditelnosti databázově orientované části systému zajišťujícím automatickou extrakci dat z webových zdrojů (formáty XHTML, XML, CSV). Úkolem této části je transformace dat do automaticky vygenerovaného relačního modelu, který může být následně užit pro realizaci myšlenek sémantického webu. V úvodní části je uvedena motivace pro implementaci takového nástroje. Součástí příspěvku je i částečné ohlédnutí za již implementovanými metodami, které autor v současné době zpracovává. V poslední části je nastíněna fuzzyfikace problematiky.

Příspěvek popisuje provedenou studii proveditelnosti databázově orientované části systému zajišťujícím automatickou extrakci dat z webových zdrojů (formáty XHTML, XML, CSV). Úkolem této části je transformace dat do automaticky vygenerovaného relačního modelu, který může být následně užit pro realizaci myšlenek sémantického webu. V úvodní části je uvedena motivace pro implementaci takového nástroje. Součástí příspěvku je i částečné ohlédnutí za již implementovanými metodami, které autor v současné době zpracovává. V poslední části je nastíněna fuzzyfikace problematiky.

The paper proposes a data structure modelling method, which aim is to estimate a structure model from a given input data set. The model can be seen as an estimate of data semantics ֠the obtained relations can be transformed into an RDF or OWL semantic web format documents to be included into the semantic web portfolio. The proposed method makes a connection between current web sources and the semantic web vision to be realized. Finally, the method usage and conversion rules are illustrated on an example.

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.

Odhad struktury dat získaných například z webových zdrojů lze využít jednak pro uložení dat, tak pro netriviální dotazování nad těmito daty. Článek rozšiřuje metodu odhadu struktury dat získávající odpovídající schéma relačního modelu ze vstupních dat a popisuje metodu uložení dat pomocí jednoduchého asociativního úložiště dat právě na základě odhadnutého modelu. Článek diskutuje dvě možné implementace úložiště: první uchovávající data jako instance funkčních závislostí, druhou uchovávající pouze instance funkčních závislosti mezi jednoduchými atributy rozšířenou o podporu komplexních atributů pomocí metainformace.

Použití asociativního úložiště dat je jednou z možností, jak efektivním způsobem reprezentovat data. Článek se zabývá z převážné části metodou učení takového úložiště, přičemž využívá myšlenek vize sémantického webu. Dále ukazuje souvislosti této metody s teoriemi organizace paměti živých organismů včetně člověka a její učení bez snahy tyto procesy zpětně modelovat. Jelikož se nabízí možnost využít současných webových stránek jako vstupních dat, je učící algoritmus navržen inkrementálnì a výhody použití takového adaptivního přístupu jsou detailně popsány. Výsledkem algoritmu je asociativní úložiště navržené na základě všech dostupných (meta)informací, na které je možné pohlížet jako na extensionální úrovni odhadnutou sémantiku dat.

Mechanisms for accessing and training the data repository using a binary matrix formalism are presented. The repository is designed for a data storage through corresponding instances of simple attribute functional dependencies, which can be seen as similar to the binary predicate formalism being used by the RDF semantic web format. Two mechanisms for querying a repository, the generalisation and the specialisation, are given. Furthermore, the incremental repository training mechanism with no extra requirements on the input data form is described: The extensional functional dependency system is used as a generalised view on the stored data; the algorithm is inspired by the functional dependency discovery approach.

The paper compares features of learning and querying process in the situation, when values in the input data set are annotated by attributes or this information is not available. The attribute annotation enables to consider global relationships, which are useful to express the data semantics in a explicit way. It will be shown data can be accessed with no semantic interpretation and then, after the evaluation process, the result can be interpreted.

Machine learning methods can be seen as an optimalisation task reducing differences between an expected and returned result on a given data set. A corresponding knowledge base can be expressed in many ways, for example, by a binary predicate formalism. The talk deals with a minimalisation of predicate ammount in such a repository, which is enabled by a transitivity. The transitive reduction algorithm will be detaily given for an incremental (attribute annotated data driven) building of a knowledge base; a base model with higher expressiveness will be prefered. Finally, an effect of the selected model to estimated explicit semantic definitions of symbols (internal base interpretation) will be mentioned as well.

Vize sémantického webu byla představena před skoro již 10 lety, avšak žádná z její aplikací prozatím nedokázala oslovit takové množství lidí, jaké dnes používá web v současné podobě. Příspěvek se věnuje možnostem sémantického webu a přínosům, které může přinést pro koncové uživatele. Nejprve podává přehled o současných technologiích i jejich použití a následně diskutuje možnosti plynoucí z použití odkazů v prostředí sémantického webu tak, jak je známe z webu současného, tedy rozšiřující, zpřesňující či udávající kontext prezentované informace.

Přıspěvek se zabývá aspekty optimalizace paměťových nároků binárního úložiště atributově anotovaných dat na základě transitivní redukce zobecněného systému funkčních závislostí. Tento systém buď může být předem daný modelem, v tomto případě se ukazuje, že je možné optimalizaci použít jednorázově; a nebo tento model je inkremetálním způsobem odhadován a pak se ukazuje vhodným pouze již jednou naoptimalizované úložiště pouze upravovat opět inkrementálním způsobem. V poslední sekci se příspěvek zaobírá rozborem nejednoznačnosti výsledku včetně detailního rozboru vlastností základních konfigurací částí modelu způsobující tuto nejednoznačnost. V neposlední řadě je analyzována složitost dílčích operací v úložišti.

Cílem příspěvku je předložit vizi nových přístupů pro sdílení a vyhledávání dat na internetu. Opírá se o prověřené technologie pracující nad textovými webovými dokumenty a propojuje je se sémantickým webem, moderním prostředkem pro výměnu dat a aktuálními trendy ve vývoji internetu jako celku.

The vision of the semantic web as a distributed and trusted environment for data sharing together with related issues are presented. The paper brings a basic binary matrix formalism for the internal representation of sources and shows the clasical issues as a data inconsistency and a data integration. Aspects of these issues lead to the binary formalism to be generalised into the <0,1> interval one to enable the consideration of uncertainty at various level. Finally, the need of a source trust definition is presented and discussed with respect to a semantic web.

This paper presents OASIS – a new tool that enables (semi)automatic conversion of existing knowledge bases, semantic networks, terminological databases and various other resources to complex ontologies into OWL. The tool is implemented as a client of DEB (Dictionary Editor and Browser) which is able to store, index and efficiently retrieve lexical data. The architecture is based on XML and related W3C standards (XSLT, XML Schema, XPath, DOM). The main feature which brings the efficiency of the transformation is the extension of a standard XSLT processor with the ability to obtain additional data from the server through the mechanism of nested queries. This technique allows formulation of complex constraints needed in the conversion to OWL

Advances in cellular mobile technology have engendered a new paradigm of computing, called mobile computing. New challenges have arisen and solutions are proposed based on various approaches. One of the most important challenges is security and now a day has been found ubiquitous in computing as whole. The paper is intended as a quick survey emphasizing security paradigm and also ad hoc networks are kept in mind and briefly discussed.

In the paper, we present a quality of service and energy aware communication protocol, called RollingBall. We do believe that QoS and energy awareness are two of the most important parameters in wireless sensor networks. The protocol is completely distributed with no centralized control. The key idea is to introduce a resistance calculation for every connection in the network. The resistance reflects the distance to the sink together with energy capabilities of particular sensor. While the resistance is continually re-calculated, packets are sent to the sink via an appropriate path. Such a scheme allows to spend minimum messages on network management, whereby sensor network lifetime is extended and throughput remains high.

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.

The thesis deals with security hazards in distributed environments where traditional centralized approaches are only of limited serviceability. One of the very successful model for treating security and access management in distributed systems are so called reputation systems. The main goal of the rep- utation systems is to provide entities in the environment with mechanisms for inferring and building trust consequently used for access control. If the trust between two entities is high enough, transactions are likely to be allowed. The thesis proposes a new security model with trust management system for dynamic and distributed environments with huge number of entities. In dynamic systems new entities or relationships are likely to emerge or existing entities or relationships may often disappear. Such dynamics pose severe problems even for traditional reputation systems. Therefore our approach differs from the traditional ones in the way adopted for establishment and management of trust between entities in our point of view trust is not assigned to particular relationships but the trust is common for a group of entities. In this way, our proposal significantly enhances ability to infer trust between entities with no previous personal experiences with each other or in environments with huge number of entities. For the proposal differs in understanding of trust, it uses a hypergraph model for representation of system of entities. The security model proposed in the thesis contains two algorithms for transformation of a general input graph structure into hypergraph model, an algorithm treating dynamics of the distributed environment and a security subsystem. Our experimental implementation SecGrid utilizes proposed algorithms and it is used for experimental verification of the security models. The experiments investigate ability of the transformation algorithms; in details the dynamic part of our proposal together with the security subsystem proposed specially for the hypergraph model. Experiments show that our model overcame the traditional graph model in many ways especially in dynamic environments with huge amount of entities.

The paper presents a new approach for treating security issues in various environments with special emphasis on Mobile databases, Semantic web and Grids. A brief overview on possible security models and a discussion on their advantages and disadvantages is given. Our model based on virtual organization and is build up on mathematical background based on hypergraphs. We show that hypergraphs are the way how to reduce space complexity of the model. The complexity is important with respect to target environments where number of users might be huge. To verify our model an experimental implementation was programed and some graphical outputs are mentioned.

This paper gives a short overview on security issues widely found in the Semantic Web environment. It goes through each level of the proposed Semantic Web layers and discusses security, privacy and trust for each. Then, a list of possible solutions is given. In particular XML security, RDF security, secure information integration and trust on the Semantic Web are mentioned and short discussion is given. Finally, an approach for treating security and trust based on Virtual organization is described and its advantages are provided.

The paper describes a new approach for treatment security issues in reconfigurable groups of users (Virtual Organizations-VO). The proposed strategy combines a convenient mathematical model, efficient combinatorial algorithms which are robust with respect to changes in the VO structure, and an efficient implementation. The mathematical model uses properties of weighted hypergraphs. Model flexibility enables description of basic security relations between the nodes such that these relations are preserved under frequent changes in connections of the hypergraph nodes. The proposed implementation makes use of the techniques developed for time and space-critical applications in numerical linear algebra. The ideas can be generalized to other concepts describable by weighted hypergraphs. The consistency of the proposed ideas for security management in the changing VO was verified in a couple of tests with our pilot implementation SECGRID.

The technical report presents a brief overview on web search engines with deeper insight into their linear algebra background. The linear algebra plays very important role in modern web search algorithms (e.g. Google). The report presents two algorithms, particularly HITS and PageRank. The algorithms are discussed on their convergence problems and also some improvements to their personalization abilities. The computation complexity is also mentioned and briefly sketched.

The paper describes a new approach for treating trust in reconfigurable groups of users with special accent on trust in the next generations of the Internet. The proposed model uses properties of weighted hypergraphs. Model flexibility enables description of relations between nodes such that these relations are preserved under frequent changes. The ideas can be straightforwardly generalized to other concepts describable by weighted hypergraphs. The consistency of the proposal was verified in a couple of experiments with our pilot implementation SecGRID.

The paper presents the BlueGame project an ad-hoc multiplayer mobile game based on the Dungeons&Dragons board game. The main idea lies in the adoption of Bluetooth Piconet configuration and direct face to face contact of players in real environments.

Despite the tremendous research activity in the field of searching engines for the Internet, current searching engines still face some severe limitations. The paper presents an idea of a distributed data source environment to be build on the current state of the art technologies available on the Internet. The paper combines recent advances in the fields of a data inconsistency, a data integration and reputations of sources for further refinements of data searching and sharing processes. The paper generalizes the data binary formalism narrowly connected with the ideas of the semantic web into the <0,1> interval to enable the consideration of uncertainty at various levels.

At present the eXtensible Markup Language (XML) is used almost in all spheres of human activities. Its popularity results especially from the fact that it is a self-descriptive metaformat that allows to define the structure of XML data using other powerful tools such as DTD or XML Schema. Consequently, we can witness a massive boom of techniques for managing, querying, updating, exchanging, or compressing XML data. On the other hand, for majority of the XML processing techniques we can find various spots which cause worsening of their time or space efficiency. Probably the main reason is that most of them consider XML data too globally, involving all their possible features, though the real data are often much simpler. If they do restrict the input data, the restrictions are often unnatural. In this contribution we discuss the level of complexity of real XML collections and their schemes, which turns out to be surprisingly low. We involve and compare results and findings of existing papers on similar topics as well as our own analysis and we try to ¯nd the reasons for these tendencies and their consequences.

At present the eXtensible Markup Language (XML) is used almost in all spheres of human activities. We can witness a massive boom of techniques for managing, querying, updating, exchanging, or compressing XML data. On the other hand, for majority of the XML processing techniques we can find various spots which cause worsening of their time or space efficiency. Probably the main reason is that most of them consider XML data too globally, involving all their possible features, though the real data are often much simpler. If they do restrict the input data, the restrictions are often unnatural. We discuss the level of complexity of real XML collections and their schemes, which turns out to be surprisingly low. We involve and compare results and findings of existing papers on similar topics as well as our own analysis and we try to ¯nd the reasons for these tendencies and their consequences.

While partial ontologies cover view at one-track area, many applications require much more general approach to describe their data. On this account it approaches to ontology matching as a headstone of further operations, that can transform several ontological descriptions into one. This paper describe case study of such process with using different methods, confront their fruitfulness and discuss a possibility of using particular results to definition of final ontology. Two trivial ontologies were created (independently of any tool) and they were matched using various selected tools.

Internet je ohromným zdrojem provázaných, ale většinou neuspořádaných dat. Sémantický web, jako rozšíření webu současného, se snaží tuto neuspořádanost řešit a to nejen bezprostředně pro lidského uživatele, ale zejména z hlediska možnosti strojového zpracování informací. Cílem je doplnit data o metadata, která mají být srozumitelná jak pro člověka, tak pro počítač. Tato metadata jsou nejčastěji vyjádřena pomocí ontologií, které jsou jedním ze základních stavebních prvků sémantického webu. V příspěvku se snažím nastínit některé z možností integrace (slučování) ontologií za účelem sdílení informací.

Amorphous computing differs from the classical ideas about computations almost in every aspect. The architecture of amorphous computers is random, since they consist of a plethora of identical computational units spread randomly over a given area. Within a limited radius the units can communicate wirelessly with their neighbors via a single-channel radio. We consider a model whose assumptions on the underlying computing and communication abilities are among the weakest possible: all computational units are finite state probabilistic automata working asynchronously, there is no broadcasting collision detection mechanism and no network addresses. We show that under reasonable probabilistic assumptions such amorphous computing systems can possess universal computing power with a high probability. The underlying theory makes use of properties of random graphs and that of probabilistic analysis of algorithms. To the best of our knowledge this is the first result showing the universality of such computing systems.

A computational model of molecularly communicating mobile nanomachines is de¯ned. Nanomachines are modelled by timed probabilistic automata augmented by a severely restricted communication mechanism. We show that for molecular communication among such machines an asynchronous stochastic protocol originally designed for wireless communication in so-called amorphous computers with static computational units can also be used. We design an algorithm that using randomness and timing delays selects with a high probability a leader from among sets of anonymous candidates. This enables a simulation of counter automata proving that networks of mobile nanomachines possess universal computing power.