||Evaluating LegalDocML and LegalRuleML as a Standard for Sharing Normative Information in the AEC/FM Domain
||Johannes Dimyadi, Guido Governatori and Robert Amor
||Legal text is typically conveyed in natural language and thus not readily suitable for computer processing. Numerous work-around approaches have been proposed by researchers in the Architecture, Engineering, Construction and Facilities Management (AEC/FM) domain over the last four decades to create computable representations of normative data that can be used to automate some of the processes in the domain. The transition from human-readable text to a structured representation can occur in many possible ways, e.g. through Natural Language Processing (NLP) techniques, manual annotations, or through direct coding. In all cases, however, the human-readable document at the source remains the sole point of reference. Ideally, however, one digital structured representation should also be available and recognised as the single digital point of reference.Research in the AEC/FM domain has shown that automated compliant building design processes would benefit from a single standardised and manageable digital representation of normative data. Recent efforts in the legal domain have shown promising developments in legal mark-up languages such as LegalDocML and LegalRuleML as emerging open standards for legal knowledge interchange. In this article, we explore the potential of adapting these emerging standards to accommodate specific requirements of the AEC/FM domain.
|Year of publication:
||Legal Knowledge Model, Normative Information, Automated Compliance Audit, LegalDocML, LegalRuleML
Johannes Dimyadi, Guido Governatori and Robert Amor (2017).
Evaluating LegalDocML and LegalRuleML as a Standard for Sharing Normative Information in the AEC/FM Domain. Lean and Computing in Construction Congress (LC3): Volume I Ð Proceedings of the Joint Conference on Computing in Construction (JC3), July 4-7, 2017, Heraklion, Greece, pp. 637-644,