A Locality Sensitive Hashing Approach for Conceptual Classification

The increasing volume of semantic content available in the Web, generally classified by concept hierarchies or simple ontologies, turns the searching and reasoning upon these data a great challenge. Generally, a search in Semantic Web may not be addressed to a specific document, but to a group of data classified in the same concept. Several structures used to distribute data, e.g. P2P networks, use hash values to identify these data, without maintaining the semantic values of the stored data. This paper contributes by proposing the creation of hash values that keep similar data stored near to each other in a P2P network, reducing the effort to retrieve similar data. The proposed hash values are derived from the data classification based on ontologies, using locality sensitive hashing (LSH) functions.