Biodiversity data are complex and abundantly available and spread out over a multitude of repositories. These data can be classified as semi-structured and are organized differently, depending on the elicitor or the expert who generated the knowledge. This constitutes the problem of biodiversity data interoperability. To mitigate such problems and to improve knowledge acquisition, OntoBio was developed.

The methodology adopted for the development of OntoBio, uses explicit knowledge to define the ontological schema of the domain. Thus, the tacit knowledge of the domain is not considered during modeling and it is observed that much could be inferred and the scope of the modeled schema would be amplified if it were considered during the process of formalization. The incorporation of tacit knowledge to ontological schemas has the purpose of increasing the expressiveness of ontologies. This purpose has guided the development of a conceptual framework to incorporate semantics to formal ontologies through tacit knowledge.

The conceptual framework consists of the following steps: (1) knowledge elicitation; (2) knowledge formalization; (3) ontology matching; (4) recommendations for ontology evolution and; (5) analysis of the recommendations for ontology evolution. The application of the framework to OntoBio has produced two main outputs: (a) recommendations for evolution of the underlying ontology from the domain Expert Mental Model (EMM). In this research, EMMs refer to more specific fact situations, rather than more general phenomena. To each new elicited and formalized EMM, new recommendations for change are available. Ontology becomes a dynamic instrument of knowledge representation; and (b) to each EMM applied to the framework, a Progressive Formalization Schema (the knowledge of a domain may be presented at different levels of formalization, from text documents to explicit rules) is generated, allowing ontology engineers to revisit the elicited and formalized knowledge for further use. Also, it allows access to knowledge at different levels of granularity and minimizes the semantic losses that may occur at different levels of knowledge representation.

The steps (3) and (4) are under development and the tests carried out so far have covered the ichthyology domain. The next phase of the research, includes the design of an experiment to elicit scientific knowledge of strategic research groups at Instituto Nacional de Pesquisas da Amazônia (INPA), for example ornithology, and disseminate the EMMs. This implies that any new EMM should be mapped to OntoBio, resulting in improvements to the ontology.