Towards A Unified Knowledge Graph Data Management System

Abstract

Knowledge graph currently has two main data models: RDF graph and property graph. The query language on RDF graph is SPARQL, while the query language on property graph is mainly Cypher. Diferent data models and query languages hinder the wider application of knowledge graphs. In the paper, we propose a unified interoperable knowledge graph database system, which can efectively manage both RDF and property graphs.

INTRODUCTION

With the proliferation of Knowledge Graphs (KG), the applications of KGs have a rapid growth in recent years. RDF (Resource Description Framework) graph and property graph are the two mainstream data models of KGs. On one hand, RDF has become the World Wide Web Consortium recommendation to represent KGs, and is widely used by triple stores, such as gStore [ 1 ]. On the other hand, property graphs are widely applied to graph databases, such as Neo4j [ 2 ]. It has been widely recognized that it is necessary to unify the data models and query languages for KG database management. To this end, we propose a unified KG data management system, which consists of three components, i.e., storage manager, query processing coordinator, and Web interface, making multiple KGs managable in a unified database management system. The queries will be translated into unified semantics denoted by relational algebra using the query processing coordinator. In storage manager, RDF graphs and property graphs will be shred into relations with the specific approaches.

APPROACH AND NOVELTY

As shown in Fig. 1, to the best of our knowledge, the system proposed in this paper is the first KG database system that realizes a unified storage scheme, facilitates the interoperability of SPARQL and Cypher, and meanwhile provides a Web interface to visualize the query results and explanations. (1) Based on the relational model, a unified storage scheme is utilized to eficiently store RDF graphs and property graphs, and support the query requirements of knowledge graphs. (2) Using the characteristic-set-based method, Copyright © 2021 for the individual papers by the papers’ authors. Copyright © 2021 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0). Published in the Proceedings of the 2nd Workshop on Search, Exploration, and Analysis in Heterogeneous Datastores, co-located with VLDB 2021 (August 16-20, 2021, Copenhagen, Denmark) on CEUR-WS.org.

Web Interface SPARQL Cypher

Lexical Parser

Syntax Parser

Visualization

Knowledge Graph

Vertex Table 1 id property Vertex Table 2 id property Edge Table 1 id source target property Vertex Table 3 id property Edge Table 2 id source target property

Unified Knowledge Graph Storage

Query

Graph Analysis √ √ √ RPQ √ × × 3

FUTURE WORKS

In order to meet the storage and query requirements of large-scale KG data, we will focus on the distributed KG data management systems in the future. Moreover, more query features will be supported in the unified KG management system.

ACKNOWLEDGMENTS

This work is supported by National Key Research and Development Program of China (2019YFE0198600); the National Natural Science Foundation of China (61972275, 61972402); and CCF-Huawei Database Innovation Research Plan (CCF-Huawei DBIR2019004B).

References

[1] Lei Zou , M Tamer Özsu , Lei Chen, Xuchuan Shen, Ruizhe Huang , and Dongyan Zhao. gstore: a graph-based sparql query engine . The VLDB journal , 23 ( 4 ): 565 - 590 , 2014 .

[2] Justin J Miller . Graph database applications and concepts with neo4j . In Proceedings of the Southern Association for Information Systems Conference , Atlanta, GA , USA, volume 2324 , 2013 .