Using Semantic Search and Knowledge Reasoning to Improve the Discovery of Earth Science Records: An Example with the ESIP Semantic Testbed

Web resources exploration is increasingly driven by semantic web technologies with automated processing. Earth science communities generate large amounts of datasets described in hundreds of millions of metadata records. It is critical to discover the accurate data from the millions of data records based on the end user’s searching intent. However, the big challenge is how to ensure that catalogs and Spatial Web Portals can understand end user’s intents. To enable portals effectively ‘understand’ the meaning of user’s queries and to provide a better searching experience for end users, we collaborated with Earth Science Information Partners (ESIP) to develop such a capability through a semantic Testbed. We implemented a reasoning engine using similarity calculations to facilitate the meaningful discovery of Earth science data and to improve the accuracy of searching results. Kai Liu George Mason University, USA Chaowei Yang George Mason University, USA Wenwen Li Arizona State University, USA Zhipeng Gui George Mason University, USA Chen Xu George Mason University, USA Jizhe Xia George Mason University, USA