Year: 2024
Type: conference-paper
Abstract: Knowledge graphs have soared in popularity by supporting different types of applications and domains. In this context, the property graph data model has become an emerging standard in industry and academia. With its widespread use, there is also an increasing interest in investigating constraints for property graph data and their applications in data profiling. Graph Generating Dependencies (GGDs) are a class of property graph data dependencies that can express constraints on topology and properties of nodes and edges of the graph, making them a suitable candidate to expose an overview of the property graph to the user (profile graph data). However, GGDs can be difficult to set manually. To solve this issue, we propose a framework for discovering GGDs automatically from the property graph to profile graph data. Our framework has three main steps: (1) pre-processing, (2) candidate generation, and, (3) GGD extraction. Our results show that the discovered set of GGDs can give an overview of the input graph, including schema-level information between the graph patterns and attributes.
Institutions: University of Stuttgart, Eindhoven University of Technology
Language: English
Topic: Graph Theory and Algorithms
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403578186