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KBQA: Learning Question Answering over QA Corpora and Knowledge Bases

Summary: KBQA: a template-based NL-to-query system over QA corpora and knowledge bases. Learned 27M templates for 2782 intents; 57× expansion of RDF predicates boosts KB coverage; supports binary factoid and multi-hop questions, achieving SOTA on QALD. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11740
Venue
VLDB
Year
2017
Pagerank
7.1882315e-05
Overall Rank
3,699 | 74.63%
DOI
10.14778/3055540.3055541

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{cui_vldb17,
        title = {{KBQA: Learning Question Answering over QA Corpora and Knowledge Bases}},
        author = {Cui, Wanyun and Xiao, Yanghua and Wang, Haixun and Song, Yangqiu and Hwang, Seung-won and Wang, Wei},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {5},
        pages = {565--576},
        doi = {10.14778/3055540.3055541},
        url = {https://doi.org/10.14778/3055540.3055541},
        year = {2017}
}

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