Koko: A System for Scalable Semantic Querying of Text
Summary: Koko is a declarative semantic-query system combining surface syntax, dependency-tree structure, evidence aggregation, and linguistic-variation tolerance for scalable text extraction. An interactive interface visualizes parses, token/query correspondences, and extraction scores. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Xiaolan Wang (University of Massachusetts Amherst)
- 2. Jiyu Komiya (Megagon Labs)
- 3. Yoshihiko Suhara (Megagon Labs)
- 4. Aaron Feng (Megagon Labs)
- 5. Behzad Golshan (Megagon Labs)
- 6. Alon Halevy (Megagon Labs)
- 7. Wang-Chiew Tan (Megagon Labs)
BibTeX Citation
@article{wang_vldb18,
title = {{Koko: A System for Scalable Semantic Querying of Text}},
author = {Wang, Xiaolan and Komiya, Jiyu and Suhara, Yoshihiko and Feng, Aaron and Golshan, Behzad and Halevy, Alon and Tan, Wang-Chiew},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {12},
pages = {2018--2021},
doi = {10.14778/3229863.3236249},
url = {https://doi.org/10.14778/3229863.3236249},
year = {2018}
}
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,961 | Scalable Semantic Querying of Text | 2018 | VLDB | 5.093636e-05 |
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