IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Semantic Annotation of Objects

Semantic Annotation of Objects
View Sample PDF
Author(s): Petr Kremen (Czech Technical University in Prague, Czech Republic), Miroslav Blaško (Czech Technical University in Prague, Czech Republic)and Zdenek Kouba (Czech Technical University in Prague, Czech Republic)
Copyright: 2009
Pages: 16
Source title: Handbook of Research on Social Dimensions of Semantic Technologies and Web Services
Source Author(s)/Editor(s): Maria Manuela Cruz-Cunha (Polytechnic Institute of Cavado and Ave, Portugal), Eva F. Oliveira (Polytechnic Institute of Cavado and Ave, Portugal), Antonio J. Tavares (Polytechnic Institute of Cavado and Ave, Portugal)and Luis G. Ferreira (Polytechnic Institute of Cavado and Ave, Portugal)
DOI: 10.4018/978-1-60566-650-1.ch011

Purchase

View Semantic Annotation of Objects on the publisher's website for pricing and purchasing information.

Abstract

Compared to traditional ways of annotating multimedia resources (textual documents, photographs, audio/video clips etc.) by keywords in form of text fragments, semantic annotations are based on tagging such multimedia resources with meaning of objects (like cultural/historical artifacts) the resource is dealing with. The search for multimedia resources stored in a repository enriched with semantic annotations makes use of an appropriate reasoning algorithm. Knowledge management and Semantic Web communities have developed a number of relevant formalisms and methods. This chapter is motivated by practical experience with authoring of semantic annotations of cultural heritage related resources/objects. Keeping this experience in mind, the chapter compares various knowledge representation techniques, like frame-based formalisms, RDF(S), and description logics based formalisms from the viewpoint of their appropriateness for resource annotations and their ability to automatically support the semantic annotation process through advanced inference services, like error explanations and expressive construct modeling, namely n-ary relations.

Related Content

R. Sundar, P. Balaji Srikaanth, Darshana A. Naik, V. P. Murugan, Madhavi Karumudi, Sampath Boopathi. © 2024. 26 pages.
Kamalendu Pal. © 2024. 26 pages.
Hayder Luis Endo Pérez, Amed Abel Leiva Mederos, José Antonio Senso-Ruíz, Ghislain Auguste Atemezing, Daniel Gálvez Lio, Jose Luis Sánchez-Chávez, Alfredo Simón Cueva. © 2024. 13 pages.
Graveth Uzoma Ejekwu, Samson Ajodo, O. Mashood Lawal, Oluwafemi S. Balogun. © 2024. 20 pages.
Marwa Ben Arab, Mouna Rekik, Lotfi Krichen. © 2024. 18 pages.
J. Vimala Devi, Rajesh Vyankatesh Argiddi, P. Renuka, K. Janagi, B. S. Hari, S. Boopathi. © 2024. 24 pages.
Marius Iulian Mihailescu, Stefania Loredana Nita. © 2024. 45 pages.
Body Bottom