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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
4896
Venue
SIGMOD
Year
2014
Pagerank
7.3743561e-05
Overall Rank
3,211 | 77.67%
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
7 Optimal Aggregation Algorithms for Middleware [Extended Abstract] 2001 PODS 0.0015496097
506 On Graph Query Optimization in Large Networks 2010 VLDB 0.00021475362
651 Efficient Subgraph Matching on Billion Node Graphs 2012 VLDB 0.00018648572
2,196 gStore: Answering SPARQL Queries via Subgraph Matching 2011 VLDB 9.3089621e-05
4,686 Discovering and Exploring Relations on the Web 2012 VLDB 5.9987806e-05
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