Zezula Pavel, Amato Giuseppe, Dohnal Vlastislav, and Batko Michal
Similarity Search - The Metric Space Approach. In: The Semantic Web: Research and Applications (Lecture notes in Computer Science 4011/2006 - Proceedings of
ESWC'06 - 3rd European Semantic Web Conference), 11.-14.6. 2006, Budva,
Montenegro, Berlin: Springer-Verlag, 2006, pp. 65-79. (ISBN 3-540-34544-2)
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.
In: Proceedings of
LREC 2006 - 5th International Conference on Language Resources and Evaluation, 24.-26.5. 2006, Genoa,
Italy, Paris: ELRA, 2006, pp. 1338-1343. (ISBN 2-9517408-2-4)
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.
In: Proceedings of
SOFSEM 2006: Theory and Practice of Computer Science, 21.1.-27.1.2006, Měřín,
Czech Republic, LNCS 3831, Springer, Berlin, 2006, pp. 493-500. (ISBN: 3-540-31198-X)
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.
In: Proceedings of
SOFSEM 2006: Theory and Practice of Computer Science, 21.1.-27.1.2006, Měřín,
Czech Republic, Volume II, ICS AS CR, Prague, 2006, pp. 145-154. (ISBN 80-903298-4-5)
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.
Diploma Thesis, Brno: Faculty of Informatics, Masaryk University, 2006. 65 p.
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.
