A Learning-Based Semantic Approximate Query over RDF Knowledge Graph

Ge, Zhangpeng; Wang, Yuxiang; Yan, Haijiang; Xu, Xun

doi:10.1109/cbd.2018.00033

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…This work was supported by the National Natural Science Foundation of China under grants 61602410, 61702096, and 61603119; the Primary Research and Development Plan of Zhejiang under grant 2017C01009; the Natural Science Foundation of Zhejiang under grant LY17F020028; and the Public Welfare Research Program of Zhejiang under grant LGG19F020017. Preliminary version appeared in The 6th International Conference on Advanced Cloud and Big Data (CBD 2018) as a regular research paper.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Semantic locality‐based approximate knowledge graph query

Wang

Yan

et al. 2019

Concurrency and Computation

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Summary RDF knowledge graphs (KG) usually contain billions of labeled entities, and how to obtain the desired results efficiently on RDF KG for given SPARQL queries have attracted increasing attentions recently. However, it is difficult for users to write a complex SPARQL query without full knowledge of the underlying KG schema due to the “schema‐free” feature of RDF KG. In this paper, we study the problem of acquiring semantic approximate results for a simple SPARQL query instead of the complex expression. The basic idea behind is to use the real knowledge semantics to extend the query intention of given simplified SPARQL query, and then a semantic based greedy search over is designed to return top‐k similar results. To obtain the knowledge semantics efficiently, we define type similarity to reorganize the original KG as a corpus with semantic locality and then a context aware text embedding model is adopted to achieve the semantic vectors of existing knowledge. Afterwards, an approximate query method over RDF KG is designed to obtain top‐k similar results based on the knowledge semantics above. Extensive experiments over DBpedia dataset and QALD‐4 benchmark confirm our solution's superiority on both effectiveness and efficiency.

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Section: Acknowledgmentsmentioning

confidence: 99%

Semantic locality‐based approximate knowledge graph query

Wang

Yan

et al. 2019

Concurrency and Computation

Self Cite

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SSQTKG: A Subgraph-based Semantic Query Approach for Temporal Knowledge Graph

Zhu,

Duan,

Bai

2025

Data & Knowledge Engineering

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A survey on SPARQL query relaxation under the lens of RDF reification

Fakih,

Serrano-Alvarado

2024

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Query relaxation has been proposed to cope with the problem of queries that produce none or insufficient answers. The goal is to modify these queries to be able to produce alternative results close to those expected in the original query. Existing approaches querying RDF datasets generally relax the SPARQL query constraints based on logical relaxations through RDFS entailment and RDFS ontologies. Techniques also exist that use the similarity of instances based on resource descriptions. These relaxation approaches defined for SPARQL queries over RDF triples have proved their efficiency. Nevertheless, significant challenges arise for query relaxation techniques in the presence of statement-level annotations, i.e., RDF reification. In this survey, we overview SPARQL query relaxation works with a particular focus on issues and challenges posed by representative RDF reification models, namely, standard reification, named graphs, n-ary relations, singleton properties, and RDF-Star.

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A Learning-Based Semantic Approximate Query over RDF Knowledge Graph

Cited by 3 publications

References 18 publications

Semantic locality‐based approximate knowledge graph query

Semantic locality‐based approximate knowledge graph query

SSQTKG: A Subgraph-based Semantic Query Approach for Temporal Knowledge Graph

A survey on SPARQL query relaxation under the lens of RDF reification

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