A Computational Framework for Formalizing Rules and Managing Changes in Normative Systems

Legal texts are typically written in a natural language. However, a legal text that is written in a formal language has the advantage of being subject to automation, at least partially. Such a translation is not easy, and the matter is even more complex because the law changes with time, so if we formalized a legal text that was originally written in natural language, there is a need to keep track of the change. This thesis proposes original developments on these subjects. In order to formalize a legal document, we provide a pipeline for the translation of a legal text from natural to formal language and we apply it to the case of natural resources contracts. In general, adjectives play an important role in a text and they allow to characterize it: for this reason we developed a logical system aimed at reasoning with gradable adjectives. Regarding norm change, we provide an ontology to represent change in a normative system, some basic mechanisms by which an agent may acquire new norms, and a study on the problem of revising a defeasible theory by only changing its facts. Another contribution of this thesis is a general framework for revision that includes the previous points as specific cases

Automated code compliance checking in the construction domain using semantic natural language processing and logic-based reasoning

Construction projects must comply with various regulations. The manual process of checking the compliance with regulations is costly, time consuming, and error prone. With the advancement in computing technology, there have been many research efforts in automating the compliance checking process, and many software development efforts led by industry bodies/associations, software companies, and/or government organizations to develop automated compliance checking (ACC) systems. However, two main gaps in the existing ACC efforts are: (1) manual effort is needed for extracting requirements from regulatory documents and encoding these requirements in a computer-processable rule format; and (2) there is a lack of a semantic representation for supporting automated compliance reasoning that is non-proprietary, non-hidden, and user-understandable and testable. To address these gaps, this thesis proposes a new ACC method that: (1) utilizes semantic natural language processing (NLP) techniques to automatically extract regulatory information from building codes and design information from building information models (BIMs); and (2) utilizes a semantic logic-based representation to represent and reason about the extracted regulatory information and design information for compliance checking. The proposed method is composed of four main methods/algorithms that are combined in one computational framework: (1) a semantic, rule-based method and algorithm that leverage NLP techniques to automatically extract regulatory information from building codes and represent the extracted information into semantic tuples, (2) a semantic, rule-based method and algorithm that leverage NLP techniques to automatically transform the extracted regulatory information into logic rules to prepare for automated reasoning, (3) a semantic, rule-based information extraction and information transformation method and algorithm to automatically extract design information from BIMs and transform the extracted information into logic facts to prepare for automated reasoning, and (4) a logic-based information representation and compliance reasoning schema to represent regulatory and design information for enabling the automated compliance reasoning process. To test the proposed method, a building information model test case was developed based on the Duplex Apartment Project from buildingSMARTalliance of the National Institute of Building Sciences. The test case was checked for compliance with a randomly selected chapter, Chapter 19, of the International Building Code 2009. Comparing to a manually developed gold standard, 87.6% precision and 98.7% recall in noncompliance detection were achieved, on the testing data