Zeimetz, T., Hose, K., & Schenkel, R. (2023). Tunable Query Optimizer for Web APIs and User Preferences. In B. Venable, D. Garijoa, & B. Jalaian (Eds.), Proceedings of the 12th Knowledge Capture Conference 2023 (pp. 92–100). Association for Computing Machinery (ACM). https://doi.org/10.1145/3587259.3627542
To answer queries many SPARQL query processors use different sources, e.g., various knowledge bases (KBs) or end points. RESTful Web APIs are rarely the focus of those systems as they come with many limitations, like not being able to process SPARQL queries. Moreover, most existing approaches optimize their query plans only for performance, even though users often have additional preferences, e.g., coverage, reliability, or currency. Additionally, data is often provided with different levels of quality so that not all sources should be trusted equally. In this paper, we therefore present TunA, a query engine that is able to combine RESTful Web APIs and local RDF KBs in the form of triple stores while tuning its (query) plans towards user preferences. Erroneous information from Web APIs is detected using hierarchical agglomerative clustering. Our evaluation shows that TunA outperforms current state-of-the-art systems and is less vulnerable to erroneous information, even in settings where only unreliable sources are available.
