Diversified spatial keyword search on RDF data

Zhi Cai,Georgios Kalamatianos,Dimitris Papadias,Georgios J Fakas,Nikos Mamoulis

doi:10.1007/s00778-020-00610-z

Abstract

The abundance and ubiquity of RDF data (such as DBpedia and YAGO2) necessitate their effective and efficient retrieval. For this purpose, keyword search paradigms liberate users from understanding the RDF schema and the SPARQL query language. Popular RDF knowledge bases (e.g., YAGO2) also include spatial semantics that enable location-based search. In an earlier location-based keyword search paradigm, the user inputs a set of keywords, a query location, and a number of RDF spatial entities to be retrieved. The output entities should be geographically close to the query location and relevant to the query keywords. However, the results can be similar to each other, compromising query effectiveness. In view of this limitation, we integrate textual and spatial diversification into RDF spatial keyword search, facilitating the retrieval of entities with diverse characteristics and directions with respect to the query location. Since finding the optimal set of query results is NP-hard, we propose two approximate algorithms with guaranteed quality. Extensive empirical studies on two real datasets show that the algorithms only add insignificant overhead compared to non-diversified search, while returning results of high quality in practice (which is verified by a user evaluation study we conducted).

Highlights

With the proliferation of knowledge-sharing communities, such as Wikipedia, and the advances in automated information extraction from the Web, large knowledge bases, including DBpedia [14] and YAGO [54], are made avail-Recently, resource description framework (RDF) has been enriched with spatial semantics
Definition 1 Qualifying Tree Given a k semantic place (kSP) query q and an RDF graph G = V, E, a qualifying tree T = V, E is a subgraph of G, i.e., V ⊆ V, E ⊆ E, such that T is rooted at a place vertex and ∪v∈V v · ψ ⊇ q · ψ
We evaluate the efficiency of the proposed greedy algorithms and the effectiveness of the proposed k diversified semantic place (kDSP) framework against the kSP framework of [45]

Summary

Introduction

With the proliferation of knowledge-sharing communities, such as Wikipedia, and the advances in automated information extraction from the Web, large knowledge bases, including DBpedia [14] and YAGO [54], are made avail-Recently, RDF has been enriched with spatial semantics. YAGO2 [34] is an extension of YAGO that includes spatial and temporal data RDF stores such as Virtuoso [53], Parliament [43], and Strabon [39] are developed to support GeoSPARQL features Retrieval on such systems requires that query issuers fully understand the query language (e.g., SPARQL or GeoSPARQL) and the data domain, which is restrictive and discouraging for common users. Definition 1 Qualifying Tree Given a kSP query q and an RDF graph G = V , E , a qualifying tree T = V , E is a subgraph of G, i.e., V ⊆ V , E ⊆ E, such that T is rooted at a place vertex and ∪v∈V v · ψ ⊇ q · ψ.

Methods

Findings

Discussion

Conclusion