Getting Graphical with Knowledge Graphs : A proof-of-concept for extending and modifying knowledge graphs

Abstract: Knowledge Graph (KG) is an emerging topic of research. The promise of KGs is to be able to turn data into knowledge by supplying the data with context at the source. This could in turn allow machines to make sense of data by inference; looking at the context of the data and being able to derive knowledge from its context and relations, thus allowing for new ways of finding value in the sea of data that the world produces today. Working with KGs today involves many steps that are open to simplification and improvement, especially in regards to usability. In this thesis, we've aimed to design and produce an application that can be used to modify, extend and build KGs. The work includes the front-end library VueJS, the Scalable Vector Graphics (SVG) library D3 and the graph database Stardog. The project has made use of Scrum methodology to distribute and plan the work that took place over a span of six months, with two developers working halftime (20 hours/week). The result of the project is a working application that can be used by developers within the KG domain who want to be able to test and modify their graphs in a visual manner.