Demonstrating Spindra: A Geographic Knowledge Graph Management System

Abstract

Knowledge Graphs are widely used to store facts about real-world entities and events. With the ubiquity of spatial data, vertexes or edges in knowledge graphs can possess spatial location attributes side by side with other non-spatial attributes. For instance, as of June 2018 the Wikidata knowledge graph contains 48, 547, 142 data items (i.e., vertexes) to date and ≈13% of them have spatial location attributes. The co-existence of graph and spatial data in the same geographic knowledge graph allows users to search the graph with local intent. Many locationbased services such as UberEats, GrubHub, and Yelp already employ similar knowledge graphs to enhance the location search experience for their end-users. In this paper, we demonstrate a system, namely Spindra, that provides efficient management of geographic knowledge graphs. We demonstrate the system using an interactive map-based web interface that allows users to issue location-aware search queries over the WikiData knowledge graph. The Front-end will then visualize the returned geographic knowledge to the user using OpenStreetMap.

Publication
In IEEE International Conference on Data Engineering, ICDE
Jia Yu
Jia Yu
Assistant Professor (from Fall 2020)

Jia Yu obtained his PhD from Arizona State University in Summer 2020. His research interests include database systems, distributed data systems and geospatial data management.

Mohamed Sarwat
Mohamed Sarwat
Assistant Professor

Mohamed Sarwat is an assistant professor of computer science at Arizona State University. His general research interest lies in developing robust and scalable data systems for spatial and spatiotemporal applications.

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