Document Type

Conference Proceeding

Publication Date

2023

Abstract

Knowledge graphs (KG) are an established method for heterogeneous data integration and have begun powering complex software agents. However, it is important to understand where the data in the knowledge graph originates, especially within the context of synthetic research agents and other trustworthy AI systems. In this paper, we propose an ontology design pattern for tracking the provenance and context of computational observations, as well as a proposing a supporting, simplified conceptual framework for modeling abstract and concrete versions of the same underlying notion.

Comments

This work is licensed under CC BY 4.0 Creative Commons Attribution 4.0 License

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