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Abstract

This chapter presents ontologies and their role in the creation of the Semantic Web. Ontologies hold special interest, because they are very closely related to the way we understand the world. They provide common understanding, the very first step to successful communication. In following sections, we will present ontologies, how they are created and used. We will describe available tools for specifying and working with ontologies.

Date

31. 7.2010 16:58:22

Theme

Semantic Web

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Abstract

Libraries are the tools we use to learn and to answer our questions. The quality of our work depends, among others, on the quality of the tools we use. Recent research in digital libraries is focused, on one hand on improving the infrastructure of the digital library management systems (DLMS), and on the other on improving the metadata models used to annotate collections of objects maintained by DLMS. The latter includes, among others, the semantic web and social networking technologies. Recently, the semantic web and social networking technologies are being introduced to the digital libraries domain. The expected outcome is that the overall quality of information discovery in digital libraries can be improved by employing social and semantic technologies. In this chapter we present the results of an evaluation of social and semantic end-user information discovery services for the digital libraries.

Date

1. 8.2010 12:35:22

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Abstract

One of the main features of classic libraries is metadata, which also is the key aspect of the Semantic Web. Librarians in the process of resources annotation use different kinds of Knowledge Organization Systems; KOS range from controlled vocabularies to classifications and categories (e.g., taxonomies) and to relationship lists (e.g., thesauri). The diversity of controlled vocabularies, used by various libraries and organizations, became a bottleneck for efficient information exchange between different entities. Even though a simple one-to-one mapping could be established, based on the similarities between names of concepts, we cannot derive information about the hierarchy between concepts from two different KOS. One of the solutions to this problem is to create an algorithm based on data delivered by large community of users using many classification schemata at once. The rationale behind it is that similar resources can be described by equivalent concepts taken from different taxonomies. The more annotations are collected, the more precise the result of this crosswalk will be.