KGDB:统一模型和语言的知识图谱数据库管理系统

知识图谱是人工智能的重要基石,其目前主要有RDF图和属性图两种数据模型,在这两种数据模型之上有数种查询语言.RDF图上的查询语言为SPARQL,属性图上的查询语言主要为Cypher.10年来,各个社区开发了分别针对RDF图和属性图的不同数据管理方法,不统一的数据模型和查询语言限制了知识图谱的更广泛应用.KGDB(kno...

KGDB: Knowledge Graph Database System with Unified Model and Query Language

Knowledge graph is an important cornerstone of artificial intelligence. It currently has two main data models:RDF graph and property graph. There are several query languages ​​on these two data models. The query language on RDF graph is SPARQL, and the query language on property graph is mainly Cypher. Over the past ten years, various communities have developed different data management methods for RDF graphs and property graphs. Inconsistent data models and query languages ​​limit the wider application of knowledge graphs. KGDB is a knowledge graph database system with unified model and query language:(1) Based on the relational model, we propose a unified storage scheme, support the efficient storage of RDF graphs and attribute graphs, and meet the needs of knowledge graph data storage and query load; (2) Using aggregation method based on characteristic sets, KGDB solves the storage problem of untyped triples; (3) It realizes the interoperability of SPARQL and Cypher, which are two different knowledge graph query languages, and make it be able to operate the same knowledge graph. Thorough experiments on real-world datasets and synthetic datasets are carried out. Experimental results show that compared with existing knowledge graph database management systems, KGDB can not only provide more efficient storage management, but also has higher query efficiency. KGDB saves 30% of the storage space on average than gStore and Neo4j. The experimental results on basic graph pattern matching query show that, in the real-world dataset, the query efficiency of KGDB is generally higher than that of gStore and Neo4j, and can be improved by 2 orders of magnitude on the best case.