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.

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.

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.

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.

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.

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%.

With the increasing number of applications that base searching on similarity rather than on exact matching, novel index structures are needed to speedup execution of similarity queries. An important stream of research in this direction uses the metric space as a model of similarity. We explain the principles and survey the most important representatives of index structures. We put most emphasis on distributed similarity search architectures which try to solve the difficult problem of scalability of similarity searching. The actual achievements are demonstrated by practical experiments. Future research directions are outlined in the conclusions.