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.

Due to the increasing complexity of current digital data, similarity search has become a fundamental computational task in many applications. Unfortunately, its costs are still high and the linear scalability of single server implementations prevents from efficient searching in large data volumes. In this paper, we shortly describe four recent scalable distributed similarity search techniques and study their performance of executing queries on three different datasets. Though all the methods employ parallelism to speed up query execution, different advantages for different objectives have been identified by experiments. The reported results can be exploited for choosing the best implementations for specific applications. They can also be used for designing new and better indexing structures in the future.

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.

This paper presents a method for dynamization which may be used for fast and effective inverted index maintenance. Experimental results show that the dynamization process is possible and that it guarantees the response time for the query operation and index actualization.

This paper reports a new kind of lexicon currently being developed as a resource for natural language processing, language teaching, and other applications. This is a "Pattern Dictionary of English", based on detailed and extensive corpus analysis of each sense of each verb in the language. A pattern consists of a verb with its valencies, plus semantic values for each valency and other relevant clues, and is associated with an implicature that associates the meaning with the context rather than with the word in isolation. For each verb, all normal patterns are recorded. The semantic types in each argument slot are linked to actual words via a large ontology. The dictionary is aimed primarily at the NLP community, but it also has relevance for language teaching. For NLP purposes, matching actual uses of verbs in previously unseen text to patterns in the pattern dictionary offers some hope of solving the “Word Sense Disambiguation (WSD) problem”. The paper discusses the relationship between A) words as they are actually used and B) semantic types and functions in a theoretical lexicon. An attempt will be made in the full paper to relate empirically observable, corpus-based facts about ordinary word use to the theoretical abstractions of Generative Lexicon Theory of James Pustejovsky. Lexicography and linguistic theory are often uneasy bedfellows, but I shall suggest that in at least these two cases, there is a possibility of a harmonious and productive relationship.

In this paper, we present the new wordnet development tool called DEBVisDic. It is built on the recently developed platform for clientserver XML databases, called DEBii. This platform is able to cover many possible applications, from which we concentrate on the new, complete reimplementation of one of the mostspread wordnet editor and browser – VisDic. We argue for the benefits the new DEBii platform brings to wordnet editing and to XML databases in general. In the paper, we describe the state of the implementation, the insides and interfaces of the DEBVisDic tool. We also discuss its functionality and some distinctions in comparison with other dictionary writing systems.

This paper introduces a novel method for semantic annotation of web pages. Semantic annotation is performed with regard to unwritten and empirically proven agreement between users and web designers using Web patterns. This method is based on extraction of patterns which are characteristic for concrete domain. Patterns provide formalization of the agreement and allows assignment of semantics to parts of web pages. Experimental results verify the e®ectives of the proposed method.

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.

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 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.

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.

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.

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.

In this paper, a multi agent platform targeted toward the area of computational intelligence modeling is presented. We show the design of various computational agents creating multi agent systems, as well as the infrastructure capabilities. The focus of the system is the cooperation of agents, their interchangeability autonomous behavior, and emergence of new models. Two main areas of cooperation are presented: automated creation of a multi-agent system satisfying given constraints, and decision support for agent partner selection. It is demonstrated that such a system is able to assist in building hybrid artificial intelligence models based on data in a distributed environment.

In this paper we present an approach where a hybrid computational model is represented as a set of communicating agents composing a multi-agent system. A general concept of representation of connected groups of agents is introduced and utilized for automatic building of schemes to solve a given computational task. We propose a combination of an evolutionary algorithm and a formal logic resolution system which is able to generate and verify new schemes. Furthermore, the adaptive cooperation support of individual computational agents is described, which improves their efficiency in time. These features are implemented within a software system and demonstrated on several examples.

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.

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.

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.

The need for a retrieval based not on the attribute values but on the very data content has recently led to rise of the metric-based similarity search. The computational complexity of such a retrieval and large volumes of processed data call for distributed processing which allows to achieve scalability. In this paper, we propose M-Chord, a distributed data structure for metric-based similarity search. The structure takes advantage of the idea of a vector index method iDistance in order to transform the issue of similarity searching into the problem of interval search in one dimension. The proposed peer-to-peer organization, based on the Chord protocol, distributes the storage space and parallelizes the execution of similarity queries. Promising features of the structure are validated by experiments on the prototype implementation and two real-life datasets.

Basics of the DataPile structure for data handling systems have been theoretically designed and published. During implementation of such system, numerous problems which were not addressed during the theoretical design phase arose. In a real production environment, the applications connected to the DataPile core need special treatment and set important requirements on the data synchronization process. This article concerns with generating of differential data being distributed from the central DataPile storage to individual applications. It is shown that the synchronization part of DataPile-structured system can be implemented and run efficiently despite of the restrictions or limitations these individual applications impose.

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.

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 retrieval of objects from a multimedia database employs a measure which defines a similarity score for every pair of objects. The measure should effectively follow the nature of similarity, hence, it should not be limited by the triangular inequality, regarded as a restriction in similarity modeling. On the other hand, the retrieval should be as efficient (or fast) as possible. The measure is thus often restricted to a metric, because then the search can be handled by metric access methods (MAMs). In this paper we propose a general method of non-metric search by MAMs. We show the triangular inequality can be enforced for any semimetric (reflexive, non-negative and symmetric measure), resulting in a metric that preserves the original similarity orderings (retrieval effectiveness). We propose the TriGen algorithm for turning any blackbox semimetric into (approximated) metric, just by use of distance distribution in a fraction of the database. The algorithm finds such a metric for which the retrieval efficiency is maximized, considering any MAM.

This paper describes a prototype of the OCR math engine built in the DML-CZ project. Solution stands on the combination of FineReader and InftyReader programmes. The achieved error rate (counting not only character errors, but also errors in the recognition of structure of mathematics notation) decreased from an initial 12% to under 1%.

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 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.

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.

We present an overview of our results on model theoretic and fixpoint semantics for a relational algebra using a model of many valued Datalog with similarity. Using our previous results on equivalence of our model and certain variant of generalized annotated programs, we base our querying on fuzzy aggregation operators (also called annotation terms, combining functions, utility functions). Using of fuzzy aggregation operators (distinct from database aggregations) enables us to reduce tuning of various linguistic variables. In practice we can learn fuzzy aggregator operators by an ILP procedure for every user profile. Our approach enables also integration of data from different sources via aggregation and similarity. Extending domains we discuss difference between fuzzy elements and fuzzy subsets. We also discuss an alternative, when all extensional data are stored crisp and fuzziness is in rules interpreting data, context and in user query.

In this position paper we share ideas on modeling querying web resources by (imperfect) combination of particular user preferences based on description logic. Our basic assumption is, that web resources are modeled crisp. Imperfection (uncertainty, vagueness,...) comes from user context and preferences. We offer a model based on connection between three EL-description logic systems: classical, annotated(fuzzy) and a new variant of Bayesian description logic. The Bayesian part enables learning each single user`s combination function and concepts.

Moderní teorie kognitivních systémů pohlíží na tyto systémy jako na autonomní vtělené výpočetní systémy, které se situují v okolí prostřednictvím svých senzomotorických jednotek. Přesto zejména v kruzích počítačových teoretiků je opakovaně slyšet názory, že na kognici lze pořád možné pohlížet i „klasicky“, jako na problém specifického zpracování dat a že tudíž vtělení není nezbytné pro zachycení podstaty kognice. Ukážeme, že takto zjednodušený pohled opomíjí podstatnou vlastnost kognitivních systémů − a sice jejich aktivní vliv na výběr či dokonce vznik vstupních dat. Bez této zpětné vazby si systém nemůže vytvořit svůj vnitřní model světa poznaný prostřednictvím svých akcí. Pro vysvětlení povahy zmíněného problému použijeme výpočetní model kognitivních systémů zavedený autorem v předchozích pracích. Tento model umožní na principielní úrovni přemýšlet o fungování algoritmických mechanizmů imitace, komunikace, vzniku řeči, myšlení a vědomí a tím přispět i k jejich pochopení v živých systémech.

Existujú v súčasnej informatike výzvy zrovnateľné s veľkými problémami, s ktorými je konfrontované ľudstvo v oblasti fyziky, biológie, či iných vedných a inžinierskych oblastí? Má súčasná informatika výskumné zámery svojim významom zrovnateľné s odhalením podstaty gravitácie? S cestou na Mars? S rozlúštením genetického kódu? V prednáške sa zamyslíme, ktoré problémy v informatike majú podobný charakter, prečo je dobré sústrediť sa na ich riešenie a aké riešenia sa rysujú.