Mining and Utilizing Dataset Relevancy from Oceanographic Dataset (MUDROD) Metadata, Usage Metrics, and User Feedback to Improve Data Discovery and Access

Big oceanographic data have been produced, archived and made available online, but finding the right data for scientific research and application development is still a significant challenge. A long-standing problem in data discovery is how to find the interrelationships between keywords and data, as well as the intrarelationships of the two individually. Most previous research attempted to solve this problem by building domain-specific ontology either manually or through automatic machine learning techniques. The former is costly, labor intensive and hard to keep up-to-date, while the latter is prone to noise and may be difficult for human to understand. Large-scale user behavior data modelling represents a largely untapped, unique, and valuable source for discovering semantic relationships among domain-specific vocabulary. In this article, we propose a search engine framework for mining and utilizing dataset relevancy from oceanographic dataset metadata, user behaviors, and existing ontology. The objective is to improve discovery accuracy of oceanographic data and reduce time for scientist to discover, download and reformat data for their projects. Experiments and a search example show that the proposed search engine helps both scientists and general users search with better ranking results, recommendation, and ontology navigation.