h Semantic Web aims to represent the contents of Web resources in formalisms that both programs and humans can understand. It relies on rich metadata, called semantic annotations, offering explicit semantic descriptions of Web resources. These annotations are built on ontologies, representing domains through their concepts and the semantic relations between them. Ontologies are the foundations of the Semantic Web and the keystone of the Web's automated tasks-searching, merging, sharing, maintaining, customizing, and monitoring. Our work focuses on searching as needed in Web applications such as digital libraries, Web intelligence, and corporate intranets for knowledge management. Publishing languages such as HTML let us retrieve documents on the basis of their presentation and textual contents. Structuring languages such as XML or SGML let us access Web resources on the basis of their data structure. Semantic annotations improve Web searches by letting us access Web resources on the basis of their semantic descriptions. Here, we address the problem of a dedicated ontology-based query language. Ontologies ensure an efficient retrieval of Web resources by enabling inferences based on domain knowledge. However, the vision of the Semantic Web implicitly relies on the assumption that an ontology designed to describe a domain can both annotate and retrieve Web resources. In reality, this isn't always the case, because domain specialists usually build the ontologies, and users don't always share or understand their viewpoints. Users might not use the right conceptsfrom an ontologist's viewpoint-when writing a query, leading to missed answers. For example, a user might use commerce instead of business. Or, perhaps a user asking for a person working on a subject might also appreciate the retrieval of a research group working on that subject. Consequently, approximate-query processing is of prime importance for efficiently searching the Semantic Web. Our Corese ontology-based search engine handles RDF Schema, OWL Lite, and RDF metadata, and its query language enables both ontological and structural approximations. Several realworld projects using Corese illustrate its potential. Ontologies let us take into account, during query processing, some background knowledge implicit in the annotations. This comprises subsumption links between concept types or relation types, signatures of relations, axioms or rules enabling deductions, and so forth. This knowledge supports inferences that improve the matching process's efficiency. The following logical model expresses the use of ontological knowledge in Web search approaches.
First, Corby et al. REF introduced the conceptual graph-based Corese search engine that defines an RDF query language enabling both ontological and structural approximations.
Searching the semantic Web: approximate query processing based on ontologies
IEEE Intelligent Systems
IEEE Intelligent Systems
Computer Science