Abstract

Industrial standards provide guidelines for data modeling to ensure interoperability between stakeholders of an industry branch (e.g., robotics). Most frequently, such guidelines are provided in an unstructured format (e.g., pdf documents) which hampers the automated validations of information objects (e.g., data models) that rely on such standards in terms of their compliance with the modeling constraints prescribed by the guidelines. This raises the risk of costly interoperability errors induced by the incorrect use of the standards. There is, therefore, an increased interest in automatic semantic validation of information objects based on industrial standards. In this paper we focus on an approach to semantic validation by formally representing the modeling constraints from unstructured documents as explicit, machine-actionable rules (to be then used for semantic validation) and (semi-)automatically extracting such rules from pdf documents. While our approach aims to be generically applicable, we exemplify an adaptation of the approach in the concrete context of the OPC UA industrial standard, given its large-scale adoption among important industrial stakeholders and the OPC UA internal efforts towards semantic validation. We conclude that (i) it is feasible to represent modeling constraints from the standard specifications as rules, which can be organized in a taxonomy and represented using Semantic Web technologies such as OWL and SPARQL; (ii) we could automatically identify modeling constraints in the specification documents by inspecting the tables (P=87%) and text of these documents (F1 up to 94%); (iii) the translation of the modeling constraints into formal rules could be fully automated when constraints were extracted from tables and required a Human-in-the-loop approach for constraints extracted from text.

中文翻译：

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从工业标准导出语义验证规则：OPC UA 研究

摘要

工业标准提供了数据建模指南，以确保行业分支（例如机器人）的利益相关者之间的互操作性。最常见的是，这样的指南以非结构化格式（例如，pdf文档）提供，这妨碍了对依赖于这样的标准的信息对象（例如，数据模型）的自动验证，因为它们符合指南规定的建模约束。这增加了因标准使用不当而导致代价高昂的互操作性错误的风险。因此，人们对基于工业标准的信息对象的自动语义验证越来越感兴趣。在本文中，我们重点关注一种语义验证方法，将非结构化文档的建模约束正式表示为显式的、机器可操作的规则（然后用于语义验证），并（半）自动从 pdf 文档中提取此类规则。虽然我们的方法旨在普遍适用，但鉴于该方法在重要工业利益相关者中的大规模采用以及 OPC UA 内部在语义验证方面的努力，我们在 OPC UA 工业标准的具体背景下举例说明了该方法的适应性。我们的结论是：(i) 将标准规范中的建模约束表示为规则是可行的，这些规则可以按分类法进行组织并使用语义 Web 技术（例如 OWL 和 SPARQL）进行表示； (ii) 我们可以通过检查表格来自动识别规范文档中的建模约束（磷=87%）以及这些文件的文本（F1 高达 94%）； (iii) 当从表格中提取约束并且需要对从文本中提取的约束采用人机循环方法时，建模约束到正式规则的转换可以完全自动化。