Review Article Open Access

STORE-AND-SEARCH: A MODEL FOR KNOWLEDGE DISCOVERY

Abstract

The combination of two powerful technologies - the Semantic Web and Data Mining - will probably bring the internet and even the intranet closer to human reasoning than we ever thought possible. The internet is simply viewed as one huge, distributed database just waiting to be made sense of. Preliminary work in transforming this huge corpus of text, images, sound and video is already available. There is still a long way to go until efficient algorithms for automatic conversion of traditional data into ontologies and concept hierarchies will be found. In this paper we present two approaches to semantic web mining, each concerning a different aspect –yet focusing on the same basic problem: making sense of already-existing data designed originally only for human readers. The first one is an approach to recurring pattern mining and the second is a store-and-search model for knowledge discovery. We present in this paper only a small subset of work undergone in this exciting field of Semantic Web Mining, but we hope that it will provide a glimpse into the realm of possibilities that it opens.

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