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Growing and Serving Large Open-domain Knowledge Graphs

Summary: Extends Saga to scalable open-domain KG construction and serving, with embeddings for ranking and verification. Semantic Annotation links Web content to KG, enabling extraction to fix gaps and enrich KG, with on-device private KG and cross sync. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6561
Venue
SIGMOD
Year
2023
Pagerank
6.1335866e-05
Overall Rank
5,652 | 61.23%
DOI
10.1145/3555041.3589672

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{ilyas_sigmod23,
        title = {{Growing and Serving Large Open-domain Knowledge Graphs}},
        author = {Ilyas, Ihab F. and Lacerda, JP and Li, Yunyao and Minhas, Umar Farooq and Mousavi, Ali and Pound, Jeffrey and Rekatsinas, Theodoros and Sumanth, Chiraag},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3555041.3589672},
        url = {https://dl.acm.org/doi/10.1145/3555041.3589672},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
10,755 Credible Intervals for Knowledge Graph Accuracy Estimation 2025 SIGMOD 5.093636e-05
11,187 GE2: A General and Efficient Knowledge Graph Embedding Learning System 2024 SIGMOD 5.093636e-05
11,239 Efficient and Reliable Estimation of Knowledge Graph Accuracy 2024 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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