A platform-based Natural Language processing-driven strategy for digitalising regulatory compliance processes for the built environment

The digitalisation of the regulatory compliance process has been an active area of research for several decades. However, more recently the level of activities in this area has increased considerably. In the UK, the tragic incident of Grenfell fire in 2017 has been a major catalyst for this as a result of the Hackitt report’s recommendations pointing a lot of the blame on the broken regulatory regime in the country. The Hackitt report emphasises the need to overhaul the building regulations, but the approach to do so remains an open research question. Existing work in this space tends to overlook the processing of actual regulatory documents, or limits their scope to solving a relatively small subtask. This paper presents a new comprehensive platform approach to the digitalisation of the regulatory compliance processing. We present i-ReC (intelligent Regulatory Compliance), a platform approach to digitalisation of regulatory compliance that takes into consideration the enormous diversity of all the stakeholders’ activities. A historical perspective on research in this area is first presented to put things in perspective which identifies the challenges in such an endeavour and identifies the gaps in state-of-the-art. After enumerating all the challenges in implementing a platform-based approach to digitalising the regulatory compliance process, the implementation of some parts of the platform is described. Our research demonstrates that the identification and extraction of all relevant requirements from the corpus of several hundred regulatory documents is a key part of the whole process which underlies the entire process from authoring to eventually compliance checking of designs. Some of the issues that need addressing in this endeavour include ambiguous language, inconsistent use of terms, contradicting requirements and handling multi-word expressions. The implementation of these tools is driven by NLP, ML and Semantic Web technologies. A semantic search engine was developed and validated against other popular and comparable engines with a corpus of 420 (out of about 800) documents used in the UK for compliance checking of building designs. In every search scenario, our search engine performed better on all objective criteria. Limitations of the approach are discussed which includes the challenges around licensing for all the documents in the corpus. Further work includes improving the performance of SPaR.txt (the tool created to identify multi-word expressions) as well as the information retrieval engine by increasing the dataset and providing the model with examples from more diverse formats of regulations. There is also a need to develop and align strategies to collect a comprehensive set of domain vocabularies to be combined in a Knowledge Graph

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

Knowledge Extraction and Visualization from Textual Sources Intended for Construction Project Management

Током животног циклуса инвестиционог пројекта ствара се велики корпус неструктуираних и полуструктуираних докумената. Традиционални приступи у складиштењу и организовању информација из неструктуираних податка су оријентисани на рад са документима, што их чини неподесним за анализу и издвајање знања. У неструктуираним документима је отежано прикупљање, анализа и поновно коришћење релевантних информација у интегралном облику, што може изазвати проблеме на пројекту услед неблаговремених или неодговарајућих одлука. У овој дисертацији је приказана репрезентација информација издвојених из неструктуираних текстуалних докумената у облику графа значајних фраза, који корисницима треба да омогући визуелизацију и анализу значајних чињеница на пројекту са минималном количином уложеног труда. Са циљем да се конструише доменски независна репрезентација са минималним трудом експерта за претходно конфигурисање, значајне фразе су детектоване у вишејезичном окружењу применом статистичких мера за одређивање корелисаности пара речи. Граф садржи аутоматски издвојене значајне фразе које су повезане на основу сличности семантичких контекста. Репрезентација је имплементирана у графовској бази података што корисницима омогућава да детектују и визуелизују различите скривене обрасце у подацима. Неинформативне фразе су филтриране кроз поступке одређивања ентропије скупа контекста и динамичности суседства фразе кроз више графова који представљају тренутке у времену. Приказана је хеуристика за издвајање комплексних концепата, заснована на итеративној процедури за детекцију блиских фраза које припадају истом семантичком подграфу. Могућности примене предложене репрезентације су демонстриране на графу конструисаном за постојећи корпус докумената са међународног инвестиционог пројекта.During a construction project lifecycle, an extensive corpus of unstructured or semi-structured text documents is generated. Traditional approaches for information storing and organizing are document-oriented, which is highly inconvenient for data analysis and knowledge extraction. The nature of unstructured sources impedes users’ acquisition, analysis, and reuse of relevant information, leading to possible negative effects in the project management process. This dissertation suggests a procedure for automatic extraction of relevant project concepts from unstructured text documents. Concepts are organized in the form of a key-phrase network, intended to provide users with the possibility to visualize and analyze valuable project facts with less effort. With the objective of constructing a domain-independent and language-independent key-phrase network, with minimal expert involvement for configuration, an approach to detect key phrases was examined by using measures of correlation for word pairs. A network contains key phrases automatically extracted from various types of unstructured documents, with relations based on the similarity of semantic contexts. The representation was implemented as a graph database, enabling project participants to extract and visualize various patterns in data. The problem of noisy key phrases was reduced by introducing the entropy score for a set of co-occurring contexts and the measure of phrase neighborhood dynamics throughout construction project lifecycle. A heuristic for extraction of complex concepts is presented, based on the iterative procedure for detection of adjacent key phrases belonging to a same semantic subnetwork. Possible applications, such as concept tracking through time or determination of communication patterns between project participants, is demonstrated using a key-phrase network generated for the existing document corpus from an international construction project