Abstract The partial evaluation and assembly framework has recently been applied for processing subgraph matching queries over large-scale knowledge graphs in the distributed environment. The framework is implemented on the master-slave architecture, endowed with outstanding scalability. However, there are two drawbacks of partial evaluation: if the volume of intermediate results is large, a large number of repeated partial matches will be generated; and the assembly computation handled by the master would be a bottleneck. In this paper, we propose an optimal partial evaluation algorithm and a filter method to reduce partial matches by exploring the computing characteristics of partial evaluation and assembly framework. (1) An index structure named inner boundary node index (IBN-Index) is constructed to prune for graph exploration to improve the searching efficiency of the partial evaluation phase. (2) The boundary characteristics of local partial matches are utilized to construct a boundary node index (BN-Index) to reduce the number of local partial matches. (3) The experimental results over benchmark datasets show that our approach outperforms the state-of-the-art methods.

With the popularization and application of Artificial Intelligence technology, knowledge graph embedding methods are widely used for a variety of machine learning tasks. However, most of the current knowledge graph embedding models are trained with a large number of parameters and high computational time complexity. This becomes a main obstacle to apply these existing models to large-scale knowledge graphs. To address this challenge, we propose HET-KG, a distributed system for training knowledge graph embedding efficiently. HET-KG can reduce the communication overheads by introducing a cache embedding table structure to maintain hot-embeddings at each worker. To improve the effectiveness of the cache mechanism, we design a prefetching algorithm and a filtering algorithm for adaptively selecting hot-embeddings, and provide two kinds of hot-embedding table construction strategies. To address the issue of inconsistency between the local cached hot-embeddings and the global embeddings, we also develop a hot-embedding synchronization algorithm for dynamically updating the cache embedding table, which can guarantee the inconsistency bounded within a given threshold. Finally, extensive experiments are conducted on three knowledge graph datasets FB15k, WN18, and Freebase-86m. The experimental results show that HET-KG achieves 3.7x and 1.1x speedup over the state-of-the-art systems PyTorch-BigGraph and DGL-KE, respectively.

With the rapid development of artificial intelligence, knowledge graphs have been widely recognized as a critical component in many AI techniques and systems. A complex knowledge graph may contain hundreds of millions of nodes and edges, thus is challenging for end-users to understand and query. In this paper, we present a knowledge graph interactive visual query language, KGVQL, to improve the efficiency of end-users’ understanding and querying of knowledge graphs. Furthermore, KGVQL realizes the novel capability of flexible bidirectional transformations between query graphs and query results, therefore significantly assisting end-users in constructing queries over large and unfamiliar knowledge graphs in an incremental way. We present the visual syntax of KGVQL, discuss our design rationale behind this interactive visual query language, and illustrate a number of case studies. We empirically evaluate the effectiveness of a visual query system based on KGVQL against a number of textual and visual query environments over a large knowledge graph, DBpedia. Our evaluation demonstrates the superiority of KGVQL in effectiveness and accuracy.

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 this demonstration, we propose a unified interoperable knowledge graph database system, UniKG. (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 characteristicset-based method, the storage problem of untyped entities is addressed in UniKG. (3) UniKG realizes the interoperability of SPARQL and Cypher, and enables them to interchangeably operate on the same knowledge graph. (4) With a unified Web interface, users are allowed to query with two different languages over the same knowledge graph and visualize query results and explanations.