Accelerating Keyword Search for Big RDF Web Data on Many-Core Systems

Choksuchat, Chidchanok; Chantrapornchai, Chantana; Haidl, Michael; Gorlatch, Sergei

doi:10.1007/978-3-319-22689-7_14

Cited by 4 publications

(3 citation statements)

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“…for each keyword t i in query do (3) t i ∈ I (4) θ � +∞ (5) while key � GETKEY(R, q) do (6) if S(q, key) ≥ θ then break (7) if key refers to a place p then (8) T p � GETKSP(query, p) do (9) if L(T p ) � +∞ then continue (10) f(L(T p ), S(q, p)) (11) if 9 is an experiment of the influence of the parameter m on the spatial index query time under the YAGO data set. m is the percentage of the total number of locations.…”

Section: Parameter Confirmationmentioning

confidence: 99%

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Place Retrieval in Knowledge Graph

Shan

Qiu

Wang

et al. 2020

Scientific Programming

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With the rapid development of Internet and big data, place retrieval has become an indispensable part of daily life. However, traditional retrieval technology cannot meet the semantic needs of users. Knowledge graph has been introduced into the new-generation retrieval systems to improve retrieval performance. Knowledge graph abstracts things into entities and establishes relationships among entities, which are expressed in the form of triples. However, with the expansion of knowledge graph and the rapid increase of data volume, traditional place retrieval methods on knowledge graph have low performance. This paper designs a place retrieval method in order to improve the efficiency of place retrieval. Firstly, perform data preprocessing and problem model building in the offline stage. Meanwhile, build semantic distance index, spatial quadtree index, and spatial semantic hybrid index according to semantic and spatial information. At the same time, in the online retrieval stage, this paper designs an efficient query algorithm and ranking model based on the index information constructed in the offline stage, aiming at improving the overall performance of the retrieval system. Finally, we use experiment to verify the effectiveness and feasibility of the place retrieval method based on knowledge graph in terms of retrieval accuracy and retrieval efficiency under the real data.

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Section: Parameter Confirmationmentioning

confidence: 99%

“…Another one is based on the RDF graph query method. is approach allows capturing more information from RDF graph structure such as subgraphs [7][8][9][10], neighbour [11], path [5], and distance [12].…”

Section: Introductionmentioning

confidence: 99%

Place Retrieval in Knowledge Graph

Shan

Qiu

Wang

et al. 2020

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…[6] focuses on accelerating SELECT queries and describes the considerations in an efficient GPU implementation. [14] accelerates the search in big RDF data by exploiting modern multi-core architectures based on GPU. In [33], a new efficient and scalable index is proposed.…”

Section: Query Processing Based On Gpumentioning

confidence: 99%

A Scalable Sparse Matrix-Based Join for SPARQL Query Processing

Zhang

Peng

et al. 2019

Database Systems for Advanced Applications

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Resource Description Framework (RDF) has been widely used to represent information on the web, while SPARQL is a standard query language to manipulate RDF data. Given a SPARQL query, there often exist many joins which are the bottlenecks of efficiency of query processing. Besides, the real RDF datasets often reveal strong data sparsity, which indicates that a resource often only relates to a few resources even the number of total resources is large. In this paper, we propose a sparse matrix-based (SM-based) SPARQL query processing approach over RDF datasets which considers both join optimization and data sparsity. Firstly, we present a SM-based storage for RDF datasets to lift the storage efficiency, where valid edges are stored only, and then introduce a predicatebased hash index on the storage. Secondly, we develop a scalable SM-based join algorithm for SPARQL query processing. Finally, we analyze the overall cost by accumulating all intermediate results and design a query plan generated algorithm. Besides, we extend our SM-based join algorithm on GPU for parallelizing SPARQL query processing. We have evaluated our approach compared with the state-of-the-art RDF engines over benchmark RDF datasets and the experimental results show that our proposal can significantly improve SPARQL query processing with high scalability.

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