Ontology-based Semantic Search Tool for Atmospheric Science

Current search tools typically use text string comparisons to find resources matching a user's needs. For example, searches on science data catalogs are typically based on a controlled vocabulary, or a specialized set of search keywords. Smarter search tools can be developed that use ontologies to provide semantic capabilities. An ontology encodes concepts and the relationships among them. From a Machine Learning or Artificial Intelligence perspective, it is viewed as a formal, explicit specification of a shared conceptualization. It captures consensual domain knowledge of concepts and constraints of their use in a machine understandable fashion. As part of the Linked Environments for Atmospheric Discovery (LEAD) project, we have developed an Ontology-based Semantic Search Tool for Atmospheric Science. The ontology used by this tool focuses on Mesoscale Meteorology. The tool supports both ontology browsing and ontology-based semantic search capabilities. While browsing, the users are able to navigate and traverse the Mesoscale Meteorology ontology from different points of access. Thus, a user can start at a general topic and navigate to specific topics by selecting the related concepts of interest. The ontology-based semantic search provides several inference capabilities such as equivalence, inversion, generalization and specialization. These capabilities will support an expanded search vocabulary besides the list of controlled keywords. The tool uses these inference capabilities to serve as a one-stop shop to collate resources such as web pages, data sets, publications etc., relevant to Mesoscale Meteorology. When a user enters a search term, our tool first uses the ontology to find synonyms and other related concepts. The tool then uses the results from this inference step to query other resources such as Google, data catalogs or journal databases to find all the relevant resources.