8 research outputs found
Building a Knowledge Graph for Food, Energy, and Water Systems
Title from PDF of title page viewed January 30, 2018Thesis advisor: Praveen R. RaoVitaIncludes bibliographical references (pages 41-44)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017A knowledge graph represents millions of facts and reliable information about people,
places, and things. Several companies like Microsoft, Amazon, and Google have developed
knowledge graphs to better customer experience. These knowledge graphs have proven their
reliability and their usage for providing better search results; answering ambiguous questions
regarding entities; and training semantic parsers to enhance the semantic relationships over the
Semantic Web. Motivated by these reasons, in this thesis, we develop an approach to build a
knowledge graph for the Food, Energy, and Water (FEW) systems given the vast amount of
data that is available from federal agencies like the United States Department of Agriculture
(USDA), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Geological
Survey (USGS), and the National Drought Mitigation Center (NDMC). Our goal is to facilitate
better analytics for FEW and enable domain experts to conduct data-driven research. To
construct the knowledge graph, we employ Semantic Web technologies, namely, the Resource
Description Framework (RDF), the Web Ontology Language (OWL), and SPARQL. Starting
with raw data (e.g., CSV files), we construct entities and relationships and extend them
semantically using a tool called Karma. We enhance this initial knowledge graph by adding
new relationships across entities by extracting information from ConceptNet via an efficient
similarity searching algorithm. We show initial performance results and discuss the quality of
the knowledge graph on several datasets from the USDA.Introduction -- Challenges -- Background and related work -- Approach -- Evaluation -- Conclusion and future wor