期刊
PROCEEDINGS OF THE VLDB ENDOWMENT
卷 14, 期 11, 页码 2642-2654出版社
ASSOC COMPUTING MACHINERY
DOI: 10.14778/3476249.3476309
关键词
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资金
- NSF [1527536, 1545071, 2032525, 1952247, 1528995, 2008993, 2044107, 2139103]
- DARPA [FA8750-16-2-0021]
- KSU's Graduate Studies Scholarship
- Direct For Computer & Info Scie & Enginr [1528995, 1952247] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [2044107] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [2032525] Funding Source: National Science Foundation
- Division Of Computer and Network Systems [1952247, 1528995] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [2008993] Funding Source: National Science Foundation
The paper proposes TQEL framework to minimize the joint cost of EL calls and top-k query processing, with TQEL-approximate significantly improving performance compared to TQEL-exact while providing strong probabilistic guarantees. TQEL-exact itself is orders of magnitude better than a naive approach calling EL functions on the entire dataset.
Social media analysis over blogs (such as tweets) often requires determining top-k mentions of a certain category (e.g., movies) in a collection (e.g., tweets collected over a given day). Such queries require entity linking (EL) function to be executed that is often expensive. We propose TQEL, a framework that minimizes the joint cost of EL calls and top-k query processing. The paper presents two variants - TQEL-exact and TQEL-approximate that retrieve the exact / approximate top-k results. TQEL-approximate, using a weaker stopping condition, achieves significantly improved performance (with the fraction of the cost of TQEL-exact) while providing strong probabilistic guarantees (over 2 orders of magnitude lower EL calls with 95% confidence threshold compared to TQEL-exact). TQEL-exact itself is orders of magnitude better compared to a naive approach that calls EL functions on the entire dataset.
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