Inferring Metadata for a Semantic Web Peer-to-Peer Environment.

This article shows how additional inference rules allows us to derive additional metadata from existing ones and using these rules as integrity constraints helps to define the constraints on LOM elements, thus taking an important step toward a complete axiomatization of LOM metadata.

Abstract

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.