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Natural Language Question Answering over RDF — A Graph Data Driven Approach

Summary: Graph-data driven RDF Q/A framework; semantic query graph models NL questions and reduces NLQ to subgraph matching. Ambiguity resolved during matching; no-match pruning saves disambiguation cost, boosting precision and speed over SOTA on benchmarks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4897
Venue
SIGMOD
Year
2014
Pagerank
7.3672608e-05
Overall Rank
3,217 | 77.65%
DOI
10.1145/2588555.2610525

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Showing 5 of 5 cited papers.

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

Rank Cited Paper Year Venue Pagerank
8 Optimal Aggregation Algorithms for Middleware [Extended Abstract] 2001 PODS 0.0015436578
502 On Graph Query Optimization in Large Networks 2010 VLDB 0.00021528261
648 Efficient Subgraph Matching on Billion Node Graphs 2012 VLDB 0.00018688754
2,199 gStore: Answering SPARQL Queries via Subgraph Matching 2011 VLDB 9.3082437e-05
4,685 Discovering and Exploring Relations on the Web 2012 VLDB 5.9929983e-05
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