RDF Graph Visualization by Interpreting Linked Data as Knowledge

Abstract

Abstract. It is known that Semantic Web and Linked Open Data (LOD) are powerful technologies for knowledge management, and explicit knowledge is expected to be presented by RDF format (Resource Description Framework), but normal users are far from RDF due to technical skills required. As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be familiar with Semantic technology. However, an RDF graph generated from the whole query result is not suitable for reading, because it is highly connected like a hairball and less organized. To make a graph presenting knowledge be more proper to read, this research introduces an approach to sparsify a graph using the combination of three main functions: graph simplification, triple ranking, and property selection. These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users. A prototype is implemented to demonstrate the suitability and feasibility of the approach. It shows that the simple and flexible graph visualization is easy to read, and it creates the impression of users. In addition, the attractive tool helps to inspire users to realize the advantageous role of linked data in knowledge management.

LDViz: A Tool to Assist the Multidimensional Exploration of SPARQL Endpoints

“…In such tools, the RDF graph is represented via a node-link diagram and the user can incrementally reveal or hide neighboring resources via selection operations to explore and visualize relevant data from very large RDF graphs [11], while discovering linked RDF graphs in the Web [19], and inspecting information and internal relations of data subsets [10]. To assist the user in interpreting all nodes and links of an RDF graph as knowledge structures by keeping only interesting triples, Chawuthai and Takeda [8] an RDF graph, which remove redundant triples to present a sparse graph to the user, while ranking triples according to topics of interest.…”

Linked Data Interfaces: A Survey

“…This tool provides a fast and compact overview of SPARQL endpoint content, facilitating efficient exploration and comprehension. RDF4U [48] (Figure 23) is another tool that offers graph visualization over summarized graphs. It combines graph simplification, triple ranking, and property selection to present relevant information effectively.…”

Terminology for a FAIR Framework for the Virus Outbreak Data Network-Africa

“…As relational descriptions in RDF are primarily used for machine interoperability, and through linkages compatible with JSON data produced by non-relational health databases, they have no spatial structure. The visualisation of these graphs in complex relational schemas is non-trivial [14], but an RDF-based knowledge representation provides a very powerful machine interpretable data structure that can be readily used for relational knowledge discovery [15], which is one of the core aims of the knowledge base developed within VODAN.…”