Deep Active Alignment of Knowledge Graph Entities and Schemata
Summary: DAAKG learns embeddings for entities, relations, and classes to align entities and schemata across KG pairs semi-supervisedly. Active learning guides batch labeling with approximation methods, yielding strong accuracy and generalization on benchmarks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiacheng Huang (Nanjing University)
- 2. Zequn Sun (Nanjing University)
- 3. Qijin Chen (Alibaba)
- 4. Xiaozhou Xu (Alibaba)
- 5. Weijun Ren (Alibaba)
- 6. Wei Hu (Nanjing University)
BibTeX Citation
@inproceedings{huang_sigmod23,
title = {{Deep Active Alignment of Knowledge Graph Entities and Schemata}},
author = {Huang, Jiacheng and Sun, Zequn and Chen, Qijin and Xu, Xiaozhou and Ren, Weijun and Hu, Wei},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589304},
url = {https://dl.acm.org/doi/10.1145/3589304},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,374 | GeoKGM: A Multimodal Large Language Model for Zero-Shot Knowledge Graph Completion in Geospatial Databases | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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