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. Different 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 effectively manage both RDF and property graphs.
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.
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 efficiently store RDF graphs and property graphs, and support the query requirements of knowledge graphs. (2) Using the characteristic-set-based method,
Visualization Due to the unified storage scheme and query processing method that we utilized, it is easier to manage multiple KGs in one database. Users no longer need to switch among different database systems to obtain storage and query support for different data models.
To verify the effectiveness and efficiency of the proposed system, extensive experiments were conducted on several data sets. The experimental results show that our system outperforms gStore [1] and Neo4j [2], which are two state-of-the-art KG database systems. The comparison of the features supported by the systems is shown in Table . 1.
Table 1: System Comparison.
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).
Storage Query RDF Property Graph
Graph Analysis
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.