# An Analytical Study of Large SPARQL Query Logs

arXiv: https://arxiv.org/abs/1708.00363 · HTML: https://mlsanity.com/paper/1708.00363/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2017.

## Built on by (most cited first, 14 of 14)

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- [CBench: Towards Better Evaluation of Question Answering Over Knowledge Graphs](https://mlsanity.com/paper/2105.00811/) (2021)
- [Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks](https://mlsanity.com/paper/2303.01140/) (2023)
- [Cardinality Estimation on Hyper-relational Knowledge Graphs](https://mlsanity.com/paper/2405.15231/) (2024)
- [From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries (Extended Version)](https://mlsanity.com/paper/2402.08509/) (2024)
- [InteracSPARQL: An Interactive System for SPARQL Query Refinement Using Natural Language Explanations](https://mlsanity.com/paper/2511.02002/) (2025)
- [Enhancing Datalog Reasoning with Hypertree Decompositions](https://mlsanity.com/paper/2305.06854/) (2023)
- [End-to-end solution for linked open data query logs analytics](https://mlsanity.com/paper/2403.06016/) (2024)
- [Mining Path Association Rules in Large Property Graphs (with Appendix)](https://mlsanity.com/paper/2408.02029/) (2024)
- [Query Logs Analytics: A Aystematic Literature Review](https://mlsanity.com/paper/2508.13949/) (2025)
- [EMA: Approximate Nearest Neighbor Search with General Attribute Filtering and Dynamic Updates](https://mlsanity.com/paper/2606.00734/) (2026)

## Abstract (arXiv)

With the adoption of RDF as the data model for Linked Data and the Semantic Web, query specification from end- users has become more and more common in SPARQL end- points. In this paper, we conduct an in-depth analytical study of the queries formulated by end-users and harvested from large and up-to-date query logs from a wide variety of RDF data sources. As opposed to previous studies, ours is the first assessment on a voluminous query corpus, span- ning over several years and covering many representative SPARQL endpoints. Apart from the syntactical structure of the queries, that exhibits already interesting results on this generalized corpus, we drill deeper in the structural char- acteristics related to the graph- and hypergraph represen- tation of queries. We outline the most common shapes of queries when visually displayed as pseudographs, and char- acterize their (hyper-)tree width. Moreover, we analyze the evolution of queries over time, by introducing the novel con- cept of a streak, i.e., a sequence of queries that appear as subsequent modifications of a seed query. Our study offers several fresh insights on the already rich query features of real SPARQL queries formulated by real users, and brings us to draw a number of conclusions and pinpoint future di- rections for SPARQL query evaluation, query optimization, tuning, and benchmarking.