Abstract

Data integration applications are ubiquitous in scientific disciplines. A state-of-the-art data integration system accepts both a set of data sources and a target ontology as input, and semi-automatically maps the data sources in terms of concepts and relationships in the target ontology. Mappings can be both complex and highly domain-specific. Once such a semantic model, expressing the mapping using community-wide standard, is acquired, the source data can be stored in a single repository or database using the semantics of the target ontology. However, acquiring the mapping is a labor-prone process, and state-of-the-art artificial intelligence systems are unable to fully automate the process using heuristics and algorithms alone. Instead, a more realistic goal is to develop adaptive tools that minimize user feedback (eg, by offering good mapping recommendations), while at the same time making it intuitive and …

Date

January 1, 1970

Authors

D Garijo, M Kejriwal, SA Pierce, PIQ Houser, SD Peckham, Z Stanko, D Hardesty Lewis, Y Gil, DD Pennington, C Knoblock

Journal

AGU Fall Meeting Abstracts

Volume

2017

Pages

IN13A-0053