An LRMoo-Based, Component-Level, Event-Centric Approach to Legal Knowledge Graphs

View the PDF file for the paper entitled “Modeling” for the dark development of legal standards: an approach at a component level, at a component level, focusing on the event, by Hudson de Martim

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a summary:The representation of legal standards for automatic treatment is a decisive challenge, especially in tracking the timetable of its hierarchical components. While constitutive conceptual frameworks such as IFLA LRMOO provide a general bibliographic data set, and coding standards such as Akoma Ntoso provides building a strong sentence for legal documents, a model pattern is still needed for granular version, at the component level. This restriction hinders the rebuilding of the inevitable points of legal texts, which is an essential capacity for reliable legal technology applications and artificial intelligence applications. This paper suggests an organized pattern of temporal modeling on the basis of LRMOO to meet this need. Our approach models are models of development of the legal rule as a series of F2 expressions. We offer a major discrimination between the angry linguistic time version (TV)-a semantic shot of the base of the base and its monochrome achievements, and linguistic versions (LV). Both are similar to F2 expressions associated with Canonical R76 derived for property. This model is frequently applied to the internal structure of the legal text, and represented as a hierarchical chain parallel to the works of abstract components (F1) and the expressions of ingredients with version (F2). Moreover, we make the official character to the legislative amendment process using the F28 expression creation event, allowing the changes to change from the amendment law to its exact impact on the amended rule. Using the Brazilian Federal Constitution as a case study, we explain how this structure that focuses on the event allows careful and inevitable recovery and rebuilding any part of the legal text when it is on a specific date. The model provides a strong basis for building a verification that can be verified and advanced AI tools, and overcoming the restrictions of current obstetric models.