In the contemporary world of AI and data-driven applications, supervised
machines often derive their understanding, which they mimic and reproduce,
through annotations--typically conveyed in the form of words or labels.
However, such annotations are often divorced from or lack contextual
information, and as such hold the potential to inadvertently introduce biases
when subsequently used for training. This paper introduces SituAnnotate, a
novel ontology explicitly crafted for 'situated grounding,' aiming to anchor
the ground truth data employed in training AI systems within the contextual and
culturally-bound situations from which those ground truths emerge. SituAnnotate
offers an ontology-based approach to structured and context-aware data
annotation, addressing potential bias issues associated with isolated
annotations. Its representational power encompasses situational context,
including annotator details, timing, location, remuneration schemes, annotation
roles, and more, ensuring semantic richness. Aligned with the foundational
Dolce Ultralight ontology, it provides a robust and consistent framework for
knowledge representation. As a method to create, query, and compare label-based
datasets, SituAnnotate empowers downstream AI systems to undergo training with
explicit consideration of context and cultural bias, laying the groundwork for
enhanced system interpretability and adaptability, and enabling AI models to
align with a multitude of cultural contexts and viewpoints.

引言中介绍了 SituAnnotate，这是一个专门为'situated grounding' 而设计的新本体论，旨在将用于训练 AI 系统的基础真实数据与从中产生这些基础真实数据的情境和文化背景联系起来，通过结构化和上下文感知的数据注释的本体论方法，解决由于孤立注释引起的潜在偏见问题，确保语义丰富性，为知识表示提供稳健一致的基础。SituAnnotate 还作为创建、查询和比较基于标签的数据集的方法，使下游的 AI 系统能够在考虑上下文和文化偏见的情况下进行训练，为增强系统的可解释性和适应性奠定了基础，使得 AI 模型能够与多种文化背景和观点相契合。