Data validation in cancer registration is a critical operation but is resource-intensive and has traditionally depended on proprietary software. Ontology-based AI is a novel approach utilising machine reasoning based on axioms formally described in description logic. This is a different approach from deep learning AI techniques but not exclusive of them. The advantage of the ontology approach lies in its ability to address a number of challenges concurrently. The disadvantages relate to computational costs, which increase with language expressivity and the size of data sets, and class containment restrictions imposed by description logics. Both these aspects would benefit from the availability of design patterns, which is the motivation behind this study. We modelled the European cancer registry data validation rules in description logic using a number of design patterns and showed the viability of the approach. Reasoning speeds are a limiting factor for large cancer registry data sets comprising many hundreds of thousands of records, but these can be offset to a certain extent by developing the ontology in a modular way. Data validation is also a highly parallelisable process. Important potential future work in this domain would be to identify and optimise reusable design patterns, paying particular attention to avoiding any unintended reasoning efficiency hotspots.
癌症登记中的数据验证是一项关键操作，但资源密集且传统上依赖于专有软件。基于本体的AI是一种新颖方法，它利用基于描述逻辑中形式化描述的公理进行机器推理。这种方法与深度学习AI技术不同，但并不排斥它们。本体方法的优势在于能够同时应对多项挑战，其劣势则涉及计算成本——该成本随语言表达能力和数据集规模的增加而增长，以及描述逻辑所施加的类别包含限制。这两个方面都将受益于设计模式的可用性，这正是本研究的动机所在。我们使用多种设计模式在描述逻辑中对欧洲癌症登记数据验证规则进行了建模，并展示了该方法的可行性。对于包含数十万条记录的大型癌症登记数据集，推理速度是一个限制因素，但通过以模块化方式开发本体，可以在一定程度上抵消这一问题。数据验证也是一个高度可并行化的过程。该领域未来重要的潜在工作将是识别和优化可复用的设计模式，并特别注意避免任何非预期的推理效率热点。
Ontology-Based AI Design Patterns and Constraints in Cancer Registry Data Validation
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