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 chapter aims to explore an algebraic operational framework to represent the content of spatiotemporal data and support RDF graphs. The authors define a spatiotemporal data model based on RDF. On this basis, the spatiotemporal semantics and the spatiotemporal algebraic operations are investigated. The authors define five types of graph algebras. The filter operation can filter the spatiotemporal graphs using a graph pattern. Besides this, they 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 provides a number of advanced functions for building data queries.
Top1 Introduction
The Resource Description Framework (RDF) (Brickley et al, 1998) was originally designed as a metadata model to publish or exchange data on the Semantic Web, and has since become the W3C standard. Recently, it has been used as a general method for conceptual description, and some researchers have started to focus on spatiotemporal data modeling and algebraic operations.
Towards spatiotemporal data modeling, there have been some achievements in representing spatiotemporal entities, such as a temporal data model (Crouch & Kalouli, 2018; Cheng et al., 2020; Batsakis & Petrakis, 2011; Artale & Franconi, 2005), a spatial data model (Reed et al., 2015), and a spatiotemporal data model (Kim et al., 2016). In previous studies on temporal data modeling, Tappolet et al. (Tappolet & Bernstein, 2009) present a syntax and storage format based on named graphs to express a temporal RDF. Due to the fact that all entities can be linked with relations through labels, Hernández et al. (Hernández et al., 2015) put forward an approach that adds temporal qualifiers and values to the RDF model representation in the form of labels. In this approach, the RDF triples are expanded into a five-tuple (s, p, o, q, v), where (s, p, o) refers to the primary relation, q is the temporal qualifier property, and v is the temporal qualifier value. Besides this, there are some models that add temporal labels to RDF triples to form quads (Tappolet & Bernstein, 2009; Gutierrez et al., 2006). In addition to temporal models, there are some studies about spatial RDF modeling. For example, GeoRDF (Consoli et al., 2015) is an RDF-compatible profile for describing geometric information (points, lines, and polygons). In GeoRDF, GeoMetadataOverSvg is the geographic information notation, which plays a significant role in the spatial data connection of the Semantic Web. Benefiting from that, GeoRDF can be used to represent any point on the Earth. Regarding spatiotemporal data, the structured spatiotemporal RDF was first proposed by Koubarakis et al. (Koubarakis & Kyzirakos, 2010). It develops the stRDF data model based on RDF and puts forward the stSPARQL query language. The contribution of stRDF is regulating the representation principle of spatiotemporal data in RDF and standardizing the spatiotemporal data querying. It can also be applied to several spatiotemporal-related applications (Sheng, 2019; Zhao et al., 2019; Huang et al., 2019). However, the spatial information fails to associate with temporal information in the stRDF model, which means that it has a weak ability to record dynamically changing data. When the knowledge graph is updated, the changes in spatiotemporal attribute values cannot be captured in time and lead to inconsistencies in the data. Moreover, it will probably return multiple results or errors when we query it for spatial information at a given time.