Invited Talk : USDA's Semantic System
- Long title
- Cultivating Excellence: USDA's Semantic System
- Starts at
- Wed, Oct 23, 2024, 12:00 EDT
- Finishes at
- Wed, Oct 23, 2024, 13:00 EDT
- Venue
- Room A
Cultivating Excellence: USDA's Semantic System
The National Agricultural Library, in partnership with the USDA Agricultural Research Service’s Partnerships for Data Innovations (PDI) initiative, is developing a unified system for USDA semantic standards related to vocabulary, semantic modeling, and data validation and integration for knowledge management. This USDA Semantic System, aims to provide a user-friendly way for agricultural researchers to access and apply these standards to their tabular data, without needing to be experts in the underlying technology. The goal is to enhance USDA data interoperability, improve the quality of agricultural information search, discovery, aggregation, and normalization, and accommodate the diversity of domains within agricultural research. The System also focuses on optimizing computational and metadata curation while supporting innovations in the National Agricultural Library Thesaurus (NALT), which is transformed to a concept space and based on the the Simple Knowledge Organization System (SKOS) W3C standard. NALT Concept Space allows for multiple vocabularies, and associated properties, mappings to other standards, and curated SKOS collections. The result is a valuable resource for USDA standard data shapes or schemas to represent standard experimental designs, and to support both text and tabular data. The USDA Semantic System simplifies the process of gathering semantic data from domain experts, and enhances the depth of knowledge capture in a sustainable and cost-effective manner.
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Jennifer Woodward-Greene
National Agricultural Library, USDA
Jennifer is serving as the Acting Associate Director, and as the Indexing and Informatics Branch Chief for the Data Production Division at the National Agricultural Library (NAL), part of the USDA Agricultural Research Service. She specializes in applying artificial intelligence for automated subject indexing for NAL's PubAg, and other USDA content. She is responsible for the publication of the NAL Thesaurus Concept Space (NALT), and is leading the development of the USDA Semantic System in support of USDA's data transparency and interoperability initiatives. Jennifer earned her doctorate in bioinformatics and computational biology from George Mason University, a Master of Science in animal nutrition, and a Bachelor of Animal Science with honors from the University of Maryland. Her work included developing software for rapid and accurate digital image labeling and annotation, and a livestock image collection protocol to extract body measures and body weight from the digital images collected. Jennifer will discuss the development of the USDA Semantic System, emphasizing the critical need for maximal user participation to ensure the success of semantic interoperability efforts. She will share how the System's workflow was designed to address USDA users' challenges in semanticization, while enhancing the traditional NALT vocabulary to support interoperability of USDA's tabular data resources.
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