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Related Experiment Video

Updated: Nov 14, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Published on: October 13, 2023

FoodKG: A Tool to Enrich Knowledge Graphs Using Machine Learning Techniques.

Mohamed Gharibi¹, Arun Zachariah², Praveen Rao^2,3

¹Department of Computer Science and Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO, United States.

Frontiers in Big Data

|March 11, 2021

Summary

This summary is machine-generated.

FoodKG is a new tool that enhances Food, Energy, and Water (FEW) knowledge graphs using machine learning. It improves decision-making, knowledge discovery, and search for FEW data scientists.

Keywords:

AGROVOC graph embeddings knowledge graphs machine learning semantic similarity

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Area of Science:

Data Science
Artificial Intelligence
Knowledge Representation

Background:

Numerous Food, Energy, and Water (FEW) datasets exist online.
Lack of tools hinders knowledge graph development and decision-making applications in FEW domains.

Purpose of the Study:

Introduce FoodKG, a novel software tool to enrich FEW knowledge graphs.
Improve decision-making, knowledge discovery, and search results for FEW data scientists.

Main Methods:

FoodKG enriches input knowledge graphs with semantically related triples, relations, and images.
Utilizes a graph embedding technique trained on AGROVOC, a controlled vocabulary from the Food and Agriculture Organization.
Enhances knowledge graphs with semantic similarity scores, relations, and entity classification.

Main Results:

The FoodKG model demonstrated superior performance compared to state-of-the-art embedding models.
Achieved higher scores based on the Spearman Correlation Coefficient during evaluation.

Conclusions:

FoodKG effectively enriches FEW knowledge graphs, improving data utility.
Enables FEW experts to use scientific terms for concept description, facilitating better knowledge discovery.