Knowledge graphs session kicks off BEMA, ASU Workforce Edition

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Knowledge graphs session kicks off BEMA, ASU Workforce Edition

28 Jun 2024

Mari Rydings

Mari Rydings is a versatile content creator, editor and marketer with experience developing dynamic, results-driven materials for B2B and B2C audiences. As the editorial director for Avant Food Media, she leads the day-to-day editorial operations for the company’s print and digital magazines, newsletters and social media platforms. Outside of work, she loves reading, hiking, putting together jigsaw puzzles and spending time with her husband and their twin daughters.

PALOS VERDES, CA — As part of BEMA’s annual convention, taking place June 28-July 2 in Rancho Palos Verdes, CA, the association partnered with Arizona State University (ASU) to host three virtual workforce-related sessions as part of its Workforce Edition programming.

The first session, Knowledge Graphs: An Enabler of Supplier Discoverability and Enabling Digital Supply Chains, focused on the basics of knowledge graphs and how they can help create a cohesive digital supply chain and enhance food traceability efforts.

Farhad Ameri, associate professor at ASU’s School of Manufacturing Systems and Networks, led the discussion.

“A knowledge graph combines characteristics of several data management paradigms and can be understood as a database, graph and knowledge base,” Ameri explained. “It’s a database because the data can be queried via structured queries; it’s a graph because it can be analyzed; and it’s a knowledge base because the data contained in it bears formal semantics, which can be used to interpret the data and infer new facts.”

According to Ameri, knowledge graphs offer an effective approach to integrating data from multiple sources. They can also handle complex relationships; promote data standardization; provide high-quality, AI-ready data; and enable reasoning, inference and data visualization.

Created within a generalized, semantic ontology framework that gives definition and meaning to the data it receives, a knowledge graph also organizes and integrates that data according to the ontology.

In the commercial baking industry, knowledge graphs can alleviate ever-present supply chain challenges related to data integrity and interoperability, visibility and traceability, and manufacturer capability representation.

“Knowledge graphs have several advantages over traditional databases. They offer extensibility and flexibility, interoperability and standardization, and data integration and harmonization.” — Farhad Ameri | associate professor | Arizona State University

“With data integrity and interoperability, for example, there are multiple partners — manufacturers, logistics partner and retailers — each with different systems and data,” Ameri said “These systems don’t talk to each other because they have their own logical model. Ontologies can provide a formal semantic framework, facilitating data integration and ensuring interoperability among different systems.”

Other benefits of connecting supply chain information with a knowledge graph include enhanced transparency and visibility; real-time intelligence; enhanced collaboration, communication and data exchange; and flexibility, agility and resilience.

Ameri added that knowledge graphs can also help address traceability challenges in food supply chains, a hot topic as the January 2026 compliance date for the FSMA Rule 204 draws near.

“There is a wide range of diverse participants in agri-food supply chains, and end-to-end traceability requires complete and consistent information,” Ameri said. “There are data issues with varying syntax and semantics; inconsistent naming and identification conventions; and incomplete, low-quality and proprietary data. Knowledge graphs can help solve some of these problems.”

Ameri cited a current ASU use case that is applying knowledge graphs and ontology to grain manufacturing, from seed to planting to harvesting to the final transfer point.

“Each of these stages generate different types of data, and the questions becomes, ‘How can we connect all these disparate data sources and create one coherent body of data that we can traverse and query to answer traceability questions?’” he said. “These are some pretty complex queries, and we need a rich data set to answer those questions, which a knowledge graph can provide, allowing us to do traceability in a very detailed manner.”

As the session wrapped, Ameri reminded participants that a knowledge graph is much more than a database. It represents a network of real-world entities and illustrates the relationships between them.

“Knowledge graphs have several advantages over traditional databases,” he said. “They offer extensibility and flexibility, interoperability and standardization, and data integration and harmonization.”

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