Abstract-Due to the increased amount of available biodiversity data, many biodiversity research institutions are now making their databases openly available on the web. Researchers in the field use this databases to extract new knowledge and also share their own discoveries. However, when these researchers need to find relevant information in the data, they still rely on the traditional search approach, based on text matching, that is not appropriate to be used in these large amounts of heterogeneous biodiversity's data, leading to search results with low precision and recall.We present a new architecture that tackle this problem using a semantic search system for biodiversity data. Semantic search aims to improve search accuracy by using ontologies to understand user objectives and the contextual meaning of terms used in the search to generate more relevant results. Biodiversity data is mapped to terms from relevant ontologies, such as Darwin Core, DBpedia, Ontobio and Catalogue of Life, stored using semantic web formats and queried using semantic web tools (such as triple stores). A prototype semantic search tool was successfully implemented and evaluated by users from the National Research Institute for the Amazon (INPA). Our results show that the semantic search approach has a better precision (28% improvement) and recall (25% improvement) when compared to keyword based search, when used in a big set of representative biodiversity data (206,000 records) from INPA and the Emilio Gueldi Museum in Pará (MPEG). We also show that, because the biodiversity data is now in semantic web format and mapped to ontology terms, it is easy to enhance it with information from other sources, an example using deforestation data (from the National Institute of Space Research -INPE) to enrich collection data is shown.

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The consistent evolution of ontologies is a major challenge for systems using semantically enriched data, for example, for annotating, indexing, or reasoning. The biomedical domain is a typical example where ontologies, expressed with different formalisms, have been used for a long time and whose dynamic nature requires the regular revision of underlying systems. However, the automatic identification of outdated concepts and proposition of revision actions to update them are still open research questions. Solutions to these problems are of great interest to organizations that manage huge and dynamic ontologies. In this paper, we present an approach for (i) identifying the concepts of an ontology that require revision and (ii) suggesting the type of revision. Our analysis is based on three aspects: structural information encoded in the ontology, relational information gained from external source of knowledge (i.e., PubMed and UMLS) and temporal information derived from the history of the ontology. Our approach aims to evaluate different methods and parameters used by supervised learning classifiers to identify both the set of concepts that need revision, and the type of revision. We applied our approach to four well-known biomedical ontologies/terminologies (ICD-9-CM, MeSH, NCIt and SNOMED CT) and compared our results to similar approaches. Our model shows accuracy ranging from 68% (for SNOMED CT) to 91% (for MeSH), and an average of 71% when considering all datasets together.

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Abstract-Biodiversity studies all life forms that we find in nature. The maintenance of biological diversity is important because it is essential to life on Earth. The lack of accurate spatial geographic information in species occurrence data, especially from diversity rich regions (like the Amazon Forest), leads to problems in many conservation activities, such as systematic planning for the protection of endangered species. In this paper, we present a gazetteer (a geographical directory that associate name places to geographic coordinates) for biodiversity data that is available as an Linked Open Data resource (using a GeoSPARQL Endpoint) and show how it can be used to improve inaccurate geographic collection data. We compared the efficiency of our Gazetteer with three openly available resources, Geonames, WikiMapia and Wikipedia, and got a 10% better recall rate than these endpoints. We also used the Gazetteer to correct geographic data from a big record sample (327,000 occurrence records) from SpeciesLink and GBIF (two big open access repositories of biodiversity occurrence data). In this data set, we were able to add geographic coordinates to around 14% of records that did not have them before.

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