TrQuery: An Embedding-based Framework for Recommanding SPARQL Queries

TL;DR

TrQuery is an embedding-based framework that recommends approximate SPARQL query solutions by combining embedding similarity with edit distance, improving recommendation granularity and effectiveness on large RDF datasets.

Contribution

It introduces a novel embedding-based scoring model for SPARQL query recommendation, capable of distinguishing structurally similar solutions beyond traditional edit distance methods.

Findings

Effective in recommending approximate solutions

Outperforms traditional methods in accuracy

Efficient on large RDF datasets

Abstract

In this paper, we present an embedding-based framework (TrQuery) for recommending solutions of a SPARQL query, including approximate solutions when exact querying solutions are not available due to incompleteness or inconsistencies of real-world RDF data. Within this framework, embedding is applied to score solutions together with edit distance so that we could obtain more fine-grained recommendations than those recommendations via edit distance. For instance, graphs of two querying solutions with a similar structure can be distinguished in our proposed framework while the edit distance depending on structural difference becomes unable. To this end, we propose a novel score model built on vector space generated in embedding system to compute the similarity between an approximate subgraph matching and a whole graph matching. Finally, we evaluate our approach on large RDF datasets DBpedia and YAGO, and experimental results show that TrQuery exhibits an excellent behavior in terms of both effectiveness and efficiency.