Improving data discovery in metadata repositories through semantic search

The amount of ecological data available electronically is increasing at a rapid rate, e.g., over 15,000 data sets are available today in the Knowledge Network for Biocomplexity (KNB) alone. Using the existing search capabilities of these online data repositories, however, scientists struggle to quickly locate data that are relevant to their needs or that will integrate with their current data sets. Semantic technologies aim at addressing many of these problems and hold the promise of enabling more powerful "smart" searches of online data archives. We describe new semantic search features within the Metacat metadata system, which is used by many ecological research sites around the world for archiving their data using a standardized metadata format. Our semantic search system adds to Metacat the ability to store OWL-DL ontologies in addition to semantic annotations that link data set attributes to ontology terms. Our approach also extends Metacat to improve metadata search in multiple ways: (i) by expanding standard keyword searches with ontology term hierarchies; (ii) by allowing keyword searches to be applied to annotations in addition to traditional metadata; and (iii) by allowing more structured searches over annotations via ontology terms. We describe our implementation of these extensions, and compare and contrast these different types of search for a corpus of annotated documents. As data repositories continue to grow, these tools will be instrumental in helping scientists precisely locate and then interpret data for their research needs.