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ERIC Number: EJ852023
Record Type: Journal
Publication Date: 2004
Pages: 7
Abstractor: As Provided
Reference Count: 6
ISSN: ISSN-1436-4522
Inferring Metadata for a Semantic Web Peer-to-Peer Environment
Brase, Jan; Painter, Mark
Educational Technology & Society, v7 n2 p61-67 2004
Learning Objects Metadata (LOM) aims at describing educational resources in order to allow better reusability and retrieval. In this article we show how additional inference rules allows us to derive additional metadata from existing ones. Additionally, using these rules as integrity constraints helps us to define the constraints on LOM elements, thus taking an important step toward a complete axiomatization of LOM metadata (with the goal of transforming the LOM definitions from a simple syntactical description into a complete ontology). We will use RDF metadata descriptions and Prolog as an inference language. We show how these rules can be applied for the extensions of course metadata using an existing test bed with several courses. Based on the Edutella peer-to-peer architecture we can easily make RDF metadata accessible to a whole community using Edutella peers that manage RDF metadata. By processing inference rules we can achieve better search results. (Contains 1 tables and 3 figures.)
International Forum of Educational Technology & Society. Athabasca University, School of Computing & Information Systems, 1 University Drive, Athabasca, AB T9S 3A3, Canada. Tel: 780-675-6812; Fax: 780-675-6973; Web site:
Publication Type: Journal Articles; Reports - Descriptive
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Germany