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Aggregating Semantic Annotators

Summary: Ontology-aware aggregation unifies diverse semantic annotators into a richer annotation layer. A training-free, repair-based method, benchmarked against supervised models and ontology-unaware baselines, improves accuracy via inter-annotator signals. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10638
Venue
VLDB
Year
2013
Pagerank
4.1945683e-05
Overall Rank
12,085 | 15.93%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
6,133 DIADEM: Thousands of Websites to a Single Database 2014 VLDB 5.1954702e-05
6,195 WADaR: Joint Wrapper and Data Repair 2015 VLDB 5.1618114e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
855 Integrating Conflicting Data: The Role of Source Dependence 2009 VLDB 0.00015906735
3,678 Automatic Wrappers for Large Scale Web Extraction 2011 VLDB 6.8517545e-05
4,156 Uncertainty Management in Rule-Based Information Extraction Systems 2009 SIGMOD 6.3999205e-05
12,068 ROSeAnn: Reconciling Opinions of Semantic Annotators 2013 VLDB 4.1945683e-05
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