Short Papers
Biodiversity, Ontology, Data Integration, Semantic Search
Biological diversity is of essential value to life sustainability on Earth and motivates many efforts to collect data about species, giving rise to a large amount of information. Biodiversity data, in most cases, is stored in relational databases. Researchers use this data to extract knowledge and share their new discoveries about living things. However, nowadays the traditional search approach, based on keywords, is not appropriate to be used in large amounts of heterogeneous biodiversity data. In addition, the search by keyword has low precision and recall in this kind of data. In this paper, we present a novel architecture, for ontology based semantic search systems, and test results of a prototype system, implemented using state of the art free semantic web tools, using a set of representative data about biodiversity from INPA (consisting of specimens of fish and insects). This test results show that the prototype had better recall and precision than keyword based methods (for the same dataset). In the semantic web, ontologies allow knowledge to be organised into conceptual spaces in accordance to its meaning. For that reason, for semantic search to work, a key point is to create mappings between the data, stored in relational databases, and the ontologies describing this data. This work also developed such a mapping.
Flor Mamani Amanqui, Kleberson Serique, Franco Lamping, José Campos dos Santos, Andréa Albuquerque, Dilvan de Abreu Moreira