As the quantities and complexity of data increase, methods for structuring and effectively processing it are more vital than they have ever been in the past. In terms of the development and integration of modern applications and their data, incorporating practices from the Semantic Web means that researchers now have more intelligent techniques to achieve interoperability, accessibility, and reliability between resources. Moreover, professionals continue to demonstrate the need for an increasing amount of interdisciplinary data to properly answer questions. However, the existence of the Semantic Web and the availability of Linked Data techniques is not sufficient; integration methods are needed to align data sets and applications, preferably in an automated manner, so as to enhance the capabilities of decision support methods and other artificial intelligence goals.Modern industry questions and decisions require multi-disciplinary information, most of which is not currently usable in a simultaneous manner, if it is reliable at all. For example, the best Decision Engine in the world is still limited by the data it has to work with to arrive at a set of decisions; semantics allow further data to be aligned, found, acquired, and formatted such that knowledge can continue to be generated. Industry goals for intelligence often return to issues with the types and structure -or lack of structure and context - of the available data resources themselves. This is a resounding set of issues resolved by the research and contributions of this dissertation.While these research solutions are designed to be applicable to many areas of industry and the data challenges therein, these concepts and automation techniques are applied specifically within the industry domains of architecture, civil engineering, natural disaster resilience, life cycle analysis, and manufacturing. Goals for the work involved overcoming certain limitations of the typically proprietary and siloed nature of architectural and engineering applications which use numerous geometry and spatial schemas along with localized data sets. Linked Data principles with RESTful, semantic endpoints are effective for establishing solutions within these domains, and are presented in more detail from thorough explanations found in several adjoining publications. Additionally, these methods are utilized within a cloud-based framework called a Linked Data Platform which is able to host components such as use-case data stores and simulation model information relevant to the noted industry fields. These approaches lend themselves well to answering modern questions because they enable distributed processing and robust decision support techniques that are becoming essential for handling Big Data and processing it at several locations, often simultaneously.The work presented in this thesis encompasses a variety of semantic technologies, including semantic graphs, ontology design patterns, semantic rules engines, literature reviews of the technological landscape as it exists today, linked data platform designs, and interoperability automation for better approaches to decision support and for handling data describing the built environment. The main contributions of the work can be summarized as follows:Computer Science Contributions: Established several components of an intelligent LDP infrastructure required to geographically distribute processing for the needs of modern applications as well as conceptual work for the expansion of the platform after my dissertation (this includes triple stores, endpoints, graph databases with GeoSPARQL enabled, Semantic Graph building parsers, etc.). Provided a LDP architecture that is modular, reproducible, and scalable such that it is able to handle the interoperability issues and knowledge representations of Spatial-Temporal data from various sources and formatting types.  Developed and implemented several Ontology Design Patterns including the Material Transformation Pattern, Relative Relationship Pattern, Extension to Hazardous Situation Pattern, and Spatial Graph Adapter Pattern, with others in process.  Used architectural model vocabulary development - or relational and descriptive spatial analysis - strategies to provide linked data methodologies for remote model usage, regardless of platform or schema style; this is needed, for example, when the natural language of a domain needs to be captured in a computer-readable style. Provided several use case scenarios for user queries by applying data patterns and platform component processing.  Created a Linked Data View methodology for automating data extraction using established semantic mappings; this approach enables information from several data schemas to be simultaneously translated into pattern-based RDF making interoperable spatial data.   Established technique for automating queries used to construct semantic graphs; this process involves using several platform components with a data extraction parser to produce in-memory graphs which help to automate and extend existing knowledge bases. Resilience, Energy, LCA, and Built Environment Contributions: LD Views were used to automate semantic graph population with spatiotemporal data; these are implemented and in use currently for BIM, CityGML, OGC, IFCXML, and GBXML, but the method is extensible to other spatial data schema. Established several pieces of the infrastructure needed for civil engineering and architectural model processing and aided in designing resilience work flow.  Created a building methodology semantic mapping technique to design complex decision support systems based on ontologies and knowledge graphs for modern web applications using open linked data principles and demonstrated this for the built environment. Used architectural and civil engineering model vocabulary development - or relational and descriptive spatial analysis - strategies to provide linked data methodologies for remote model usage specifically in the building industries, environmental, and resilience domains. Demonstrated how several crucial elements of a distributed platform would function to support more well-informed analysis of decisions made for the built environment, risk analysis, or disaster response and performed large amounts of Data Engineering for these domains.  Semantic mappings between Building Information Modeling, Energy, and Life Cycle Analysis data is a complex and involved process. However, the benefits of aligning and using these various resources is completely worth the effort when considering the positive impacts these methods could have for cities and the environment. Automatically translatable data means that tools and applications gain access to even more knowledge to use in simulations and also previously impossible compatibility with information such as geography data schemas or other neighborhood descriptors. Furthermore, the numerous types of data relevant for modern questions are both too large in size and too complex in nature to be centrally located; this means that cloud-based solutions are key for accessing and using the ``Big Data'' required for increasing the accuracy and reliability of choices resulting from computational simulation output. As a result of this research, semantic graphs and ontology data patterns utilized in platform architectures are able to resolve various levels of data translations necessary for fully-informed data-driven decisions and knowledge interconnectivity.