Name
Abstract | ||
|---|---|---|
Knowledge graph has gained significant popularity in recent years. As one of the W3C standards, SPARQL has become the de facto standard query language to retrieve the desired data from various knowledge graphs on the Web. Therefore, accurately measuring the similarity between different SPARQL queries is an important and fundamental task for many query-based applications, such as query suggestion, query rewriting, and query relaxation. However, conventional SPARQL similarity computation models only provide poorly-interpretable results, i,e., simple similarity scores for pairs of queries. Explaining the computed similarity scores will lead to an outcome of explaining why a specific computation model offers such scores. This helps users and machines understand the result of similarity measures in different query scenarios and can be used in many downstream tasks. We thus focus on providing explanations for typical SPARQL similarity measures in this paper. Specifically, given similarity scores of existing measures, we implement four explainable models based on Linear Regression, Support Vector Regression, Ridge Regression, and Random Forest Regression to provide quantitative weights to different dimensional SPARQL features, i.e., our models are able to explain different kinds of SPARQL similarity computation models by presenting the weights of different dimensional SPARQL features captured by them. Deep insight analysis and extensive experiments on real-world datasets are conducted to illustrate the effectiveness of our explainable models. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11280-021-00886-3 | WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS |
Keywords | DocType | Volume |
Explainability, Similarity model, Regression, Explainable machine learning | Journal | 24 |
Issue | ISSN | Citations |
5 | 1386-145X | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Wang | 1 | 24 | 11.05 |
| Kefei Chen | 2 | 0 | 0.34 |
| Gang Xiao | 3 | 0 | 0.34 |
| Xinyue Zhang | 4 | 0 | 0.34 |
| Hongxu Chen | 5 | 142 | 12.99 |
| Sen Wang | 6 | 477 | 37.24 |