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

Enriching Knowledge Graphs Using Machine Learning Techniques

Title from PDF of title page viewed March 11, 2021Dissertation advisors: Praveen Rao and Sejun SonVitaIncludes bibliographical references (page 101-116)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2020A knowledge graph represents millions of facts and reliable information about people, places, and things. 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. However, while there exist a plethora of datasets on the Internet related to Food, Energy, and Water (FEW), there is a real lack of reliable methods and tools that can consume these resources. This hinders the development of novel decision-making applications utilizing knowledge graphs. In this dissertation, we introduce a novel tool, called FoodKG, that enriches FEW knowledge graphs using advanced machine learning techniques. Our overarching goal is to improve decision-making, knowledge discovery, and provide improved search results for data scientists in the FEW domains. Given an input knowledge graph (constructed on raw FEW datasets), FoodKG enriches it with semantically related triples, relations, and images based on the original dataset terms and classes. FoodKG employs an existing graph embedding technique trained on a controlled vocabulary called AGROVOC, which is published by the Food and Agriculture Organization of the United Nations. AGROVOC includes terms and classes in the agriculture and food domains. As a result, FoodKG can enhance knowledge graphs with semantic similarity scores and relations between different classes, classify the existing entities, and allow FEW experts and researchers to use scientific terms for describing FEW concepts. The resulting model obtained after training on AGROVOC was evaluated against the state-of-the-art word embedding and knowledge graph embedding models that were trained on the same dataset. We observed that this model outperformed its competitors based on the Spearman Correlation Coefficient score. We introduced Federated Learning (FL) techniques to further extend our work and include private datasets by training smaller version of the models at each dataset site without accessing the data and then aggregating all the models at the server-side. We propose an algorithm that we called RefinedFed to further extend the current FL work by filtering the models at each dataset site before the aggregation phase. Our algorithm improves the current FL model accuracy from 84% to 91% on MNIST dateset.Introduction -- Background -- Related work -- Approach Implementation -- Evaluation -- Conclusion and Future Wor