BibTeX
@MISC{Fernández_unordereddocuments,
author = {Miriam Fernández and David Vallet and Pablo Castells},
title = {Unordered Documents},
year = {}
}
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Abstract
Semantic search has been one of the major envisioned benefits of the Semantic Web since its emergence in the late 90’s [1]. Our demo shows a proposal towards this goal. One way to view a semantic search engine is as a tool that gets formal queries (e.g. in RDQL, RQL, SPARQL, or the like) from a client, executes them against an ontologybased knowledge base, and returns tuples of ontology values (resources) that satisfy the query [2]. While this conception of semantic search brings enormous advantages already, our work aims at taking a step beyond this. In our view of Information Retrieval in the Semantic Web, a search engine returns documents, rather than (or in addition to) exact values, in response to user queries. The engine should rank the documents, according to conceptbased relevance criteria. The overall retrieval process is illustrated in Figure 1 (see [3] for more details of our research). In our demo we present an environment where these ideas are put to work. The main ideas behind our prototype, and their realization in the demo, are briefly explained next. 1. Knowledge representation We propose a model that considers the distinction of three types of ontologies:
Keyphrases
semantic search semantic web ontologybased knowledge base formal query major envisioned benefit enormous advantage search engine return document semantic search engine exact value knowledge representation main idea ontology value relevance criterion overall retrieval process return tuples information retrieval
