Abstract

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 paper, we introduce a novel software tool, called FoodKG, that enriches FEW knowledge graphs using advanced machine learning techniques. Our overarching goal is to improve decision-making and knowledge discovery as well as to 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.

Highlights

  • Food, energy, and water are the critical resources for sustaining human life on Earth

  • We evaluated two recent graph embedding models, namely DeepWalk and GEMSEC, trained on AGROVOC data, to analyze their performance on the FEW domains

  • The results show that AGROVEC, based on GEMSEC trained and fine-tuned on AGROVOC, outperforms all other models by a significant margin when predicting FEW domain similarity scores

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Summary

Introduction

Energy, and water are the critical resources for sustaining human life on Earth. FEW data exists on the Internet in different formats with different file extensions, such as CSV, XML, and JSON, and this makes it a challenge for users to join, query, and perform other tasks (Knoblock and Szekely, 2015). Such data types are not consumable in the world of Linked Open Data (LOD), and neither are they ready to be processed by different deep learning networks (Meester, 2018). In September 2018, Google announced its “Google Dataset Search”, which is a search engine that includes graphs and Linked Data.

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