| Abstract |
Smart city systems are increasingly built with data analytics and machine learning techniques, basing on massive data sets. They have a heavy impact on human behavior and quality of life, and thus need to deliver a controllable and sufficiently transparent experience for the users. The aim of my work is to make smart city systems more interoperable and explainable, involving data visualization and communication techniques, sensor data processing, as well as the associated intelligent data value chain production and consumption. Here, the data and the information are shared employing Knowledge Graphs, that are becoming a key enabler for large-scale processing of massive collections of interrelated facts. Examples include the Google Knowledge Graph with dozens of billion facts, dataCommons, DBPedia, YAGO, and Knowledge Vault, a very large scale probabilistic knowledge graph created with information extraction methods for unstructured or semi-structured information. Specifically, Knowledge Graphs provide the means of development of the newest methods for data management, data fusion, data merging, and graph and network optimization and modeling, serving as a source of high quality data and a base for information integration. In particular, Knowledge Graphs help to infer new relationships out of existing facts, giving context and meaning to the content, and can be used in applications. For example, the data generated by a computer vision system could be semantically represented and shared across numerous systems, taking into account the needs and requirements of these systems, as well as the context, provenance, licensing and consent aspects of the generated data. I demonstrate Knowledge Graphs-based methods in advanced smart city applications from the domains such as automation and construction of buildings, energy efficiency, tourism, transport. |