# Urban Vulnerability Assessment of Sea Level Rise in Singapore through the World Avatar
## 2 Background
### The World Avatar  
The World Avatar (TWA) aims at creating a digital 'avatar' of the world using technologies from the Semantic Web. The world in this idea measures the space of all possible concepts and their realisations. [PERSON] et al. [15] provided a very comprehensive overview of the background of this project, as well as the future goals. Only the important aspects of the project are introduced here.  
TWA consists of a dynamic knowledge graph along with a network of agents that updates with the knowledge graph to simulate the behaviour of the world. The World Avatar makes use of the Resource Description Framework (RDF) and ontologies to represent data. The RDF data model makes statements about resources using triples, or in the form of _subject-predicate-object_. Each resource is represented with an internationalised resource identifier (IRI), ensuring data can be uniquely identified. Directed graphs can be formed with subjects and objects as nodes and predicates as edges, creating actionable knowledge graphs. SPARQL Protocol and RDF Query Language (SPARQL) [31] serves as the standard query language for querying RDF data. This query language is commonly applied for standardisation, accessibility, and automation in concepts [32]. Ontologies are used to define the classes for the domains of interest (e.g., sea level rise in this work) and the permissible relationships between the classes. They add semantic meaning to the underlying data, allowing inferences to be made.  
There are two primary ways to make these graphs queryable. The first method is through triple stores like Blazegraph [33] and RDF4J [34], where triples are explicitly stored in the database. The second method is by using virtual knowledge graphs such as Ontop [35], where the underlying data are stored in relational databases (i.e., in table form) and the triples are materialised upon query according to rules set by mapping files.  
TWA utilises Docker containers in its setup, providing modularity and scalability. Unlike conventional databases, TWA features a network of autonomous agents that operates on the knowledge graph. These agents vary from simple ones that periodically update the graph with new information, such as weather data, to sophisticated agents that perform complex simulations. These simulations include producing air quality estimates and optimising district heating networks [36], as well as conducting molecular engineering of metal-organic polyhedra [37]. By leveraging ontologies and Docker containers, the system is highly modular. Any agent can be replaced without affecting the overall operation, provided it adheres to the ontologies, ensuring seamless operation.  
In this research, TWA and autonomous agents are applied to connect various urban data (e.g., buildings, land plots, road network, cultural sites) onto the knowledge graphs. The computational agent then assesses and identifies the urban elements affected and connects the various sea level rise scenarios to the corresponding affected urban elements using knowledge graphs. The ability to perform SPARQL queries enables the answering of various competency questions that are relevant to sea level rise assessment. These questions are further detailed in Section 4.2.