.png){kind=icon}
.png){kind=icon}
Return
Visualizing large knowledge graphs: A performance analysis
.png){kind=icon}
OA
AI
DOI：10.1016/j.future.2018.06.015.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Knowledge graphs are an increasingly important source of data and context information in Data Science. A first step in data analysis is data exploration, in which visualization plays a key role. Currently, Semantic Web technologies are prevalent for modeling and querying knowledge graphs; however, most visualization approaches in this area tend to be overly simplified and targeted to small-sized representations. In this work, we describe and evaluate the performance of a Big Data architecture applied to large-scale knowledge graph visualization. To do so, we have implemented a graph processing pipeline in the Apache Spark framework and carried out several experiments with real-world and synthetic graphs. We show that distributed implementations of the graph building, metric calculation and layout stages can efficiently manage very large graphs, even without applying partitioning or incremental processing strategies. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Graphs
Visualization
Big data
Linked data
Performance analysis
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
F
IF:
6.1
Papers:
6.8K
Citations:
2.3W