Cluster query: a new query pattern on temporal knowledge graph A temporal knowledge graph (TKG) is theoretically a temporal graph. Recently, systems have been developed to support snapshot queries over temporal graphs. However, snapshot queries can only give separate answers. To retrieve forwardbackward correlation facts from temporal knowledge graph, cluster query is proposed in this paper. To deal with the query, the logical view and physical model are presented. Subsequently, five corresponding basic query patters of unit matching are studied, and then the complete matchings are also addressed. To improve the query performance, indexbased methods and pruning strategies are adopted. Experiments are conducted to evaluate cluster queries on three real datasets. The experimental results show the effectiveness and efficiency of cluster queries on temporal knowledge graphs.

Algebraic Operations on Spatiotemporal Data Based on RDF In the context of the Semantic Web, the Resource Description Framework (RDF), a language proposed by W3C, has been used for conceptual description, data modeling, and data querying. The algebraic approach has been proven to be an effective way to process queries, and algebraic operations in RDF have been investigated extensively. However, the study of spatiotemporal RDF algebra has just started and still needs further attention. This paper aims to explore an algebraic operational framework to represent the content of spatiotemporal data and support RDF graphs. To accomplish our study, we defined a spatiotemporal data model based on RDF. On this basis, the spatiotemporal semantics and the spatiotemporal algebraic operations were investigated. We defined five types of graph algebras, and, in particular, the filter operation can filter the spatiotemporal graphs using a graph pattern. Besides this, we put forward a spatiotemporal RDF syntax specification to help users browse, query, and reason with spatiotemporal RDF graphs. The syntax specification illustrates the filter rules, which contribute to capturing the spatiotemporal RDF semantics and provide a number of advanced functions for building data queries.