Generating Compact and Relaxable Answers to Keyword Queries over Knowledge Graphs
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This document presents an algorithm called CORE for generating compact yet relaxable answers to keyword queries over knowledge graphs. CORE aims to balance answer compactness, defined as having a bounded diameter, with answer completeness, defined as covering the most query keywords. It provides theoretical foundations for the existence of such answers and uses a best-first search approach. An evaluation shows CORE efficiently computes answers that are more complete than alternatives while remaining compact.
Generating Compact and Relaxable Answers to Keyword Queries over Knowledge Graphs
- 1. Generating Compact and Relaxable Answers to Keyword Queries over Knowledge Graphs Gong Cheng1, Shuxin Li1, Ke Zhang1, Chengkai Li2 1State Key Laboratory for Novel Software Technology, Nanjing University, China 2Department of Computer Science and Engineering, University of Texas at Arlington, United States ISWC 2020 1
- 2. ISWC 2020 2 Keyword Search over Knowledge Graphs United States (US) United Kindom (UK) Montana (MT) Yellowstone National Park (YSNP) isLocatedIn BBC headquarters Yellowstone (YS) The Trip (TT) Q: united states yellowstone park trip Ohio country London country producedBy G7 member member producedBy country G
- 3. Two Paradigms For lookup tasks: semantic parsing (keyword query SPARQL query) ISWC 2020 3 Keyword Search over Knowledge Graphs United States (US) United Kindom (UK) Montana (MT) Yellowstone National Park (YSNP) isLocatedIn BBC headquarters Yellowstone (YS) The Trip (TT) Q: united states yellowstone park trip Ohio country London country producedBy G7 member member producedBy country G
- 4. Two Paradigms For lookup tasks: semantic parsing (keyword query SPARQL query) For exploratory tasks: answer subgraph extraction (keyword query GST) ISWC 2020 4 Keyword Search over Knowledge Graphs United States (US) United Kindom (UK) Montana (MT) Yellowstone National Park (YSNP) isLocatedIn BBC headquarters Yellowstone (YS) The Trip (TT) Q: united states yellowstone park trip Ohio country London country producedBy G7 member member producedBy countryGroup Steiner Tree (GST)
- 5. Answer completeness? Covering all the query keywords Answer compactness? Having a compact structure (e.g., a small diameter) ISWC 2020 5 Motivation --- Pros and Cons of GST Group Steiner Tree (GST) United States (US) United Kindom (UK) Montana (MT) Yellowstone National Park (YSNP) isLocatedIn BBC headquarters The Trip (TT) country London country producedBy G7 member member diameter: 7 uncovered keywords: 0
- 6. Computing compact but relaxable subgraphs Guaranteed answer compactness: having a bounded diameter (D) Maximized answer completeness: covering the largest number of query keywords ISWC 2020 6 Main Idea Group Steiner Tree (GST) United States (US) United Kindom (UK) Montana (MT) Yellowstone National Park (YSNP) isLocatedIn BBC headquarters The Trip (TT) country London country producedBy G7 member member diameter: 7 uncovered keywords: 0
- 7. Computing compact but relaxable subgraphs Guaranteed answer compactness: having a bounded diameter (D) Maximized answer completeness: covering the largest number of query keywords ISWC 2020 7 Main Idea Minimally Relaxed Answer (MRA) United States (US) United Kindom (UK) Montana (MT) Yellowstone National Park (YSNP) isLocatedIn BBC headquarters The Trip (TT) country London country producedBy G7 member member diameter: 2 uncovered keywords: 1 (D=2)
- 8. A necessary and sufficient condition for the existence of a compactness-bounded complete answer to a keyword query ISWC 2020 8 Approach --- Theoretical Foundations United States (US) Montana (MT) Yellowstone National Park (YSNP) isLocatedIn country We refer to v as a certificate vertex for Q. • E.g., Montana for "united states yellowstone park" under D=2
- 9. ISWC 2020 9 Approach --- Algorithm CORE A best-first search algorithm one independent search starting from each keyword vertex a shared priority queue keeping search frontiers (priority: potentially uncovered keywords, based on distances) a more complete answer which the current vertex is a certificate vertex for early stop unvisited neighbors
- 10. Running example ISWC 2020 10 Approach --- Algorithm CORE United States (US) United Kindom (UK) Montana (MT) Yellowstone National Park (YSNP) isLocatedIn BBC headquarters Yellowstone (YS) The Trip (TT) Q: united states yellowstone park trip Ohio country London country producedBy G7 member member producedBy country G
- 11. Running example ISWC 2020 11 Approach --- Algorithm CORE United States (US) United Kindom (UK) Montana (MT) Yellowstone National Park (YSNP) isLocatedIn BBC headquarters Yellowstone (YS) The Trip (TT) Q: united states yellowstone park trip Ohio country London country producedBy G7 member member producedBy country G
- 12. Running example ISWC 2020 12 Approach --- Algorithm CORE United States (US) United Kindom (UK) Montana (MT) Yellowstone National Park (YSNP) isLocatedIn BBC headquarters Yellowstone (YS) The Trip (TT) Q: united states yellowstone park trip Ohio country London country producedBy G7 member member producedBy country G
- 13. Running example ISWC 2020 13 Approach --- Algorithm CORE United States (US) United Kindom (UK) Montana (MT) Yellowstone National Park (YSNP) isLocatedIn BBC headquarters Yellowstone (YS) The Trip (TT) Q: united states yellowstone park trip Ohio country London country producedBy G7 member member producedBy country G
- 14. Datasets Baselines CertQR+ (adapted to our problem) GST-based answers ISWC 2020 14 Experiment Settings
- 15. Main finding Trading off answer completeness for compactness is necessary. ISWC 2020 15 Results --- Compactness of GST-Based Answers (doc = diameter; |K| = max keyword hits)
- 16. Main finding The completeness of our computed answers is very high. ISWC 2020 16 Results --- Completeness of Relaxable Answers (dor = number of uncovered keywords; |K| = max keyword hits)
- 17. Main finding CORE is efficient and significantly outperforms CertQR+. ISWC 2020 17 Results --- Efficiency of CORE
- 18. Take-home messages Necessity of trading off answer completeness for compactness Polynomial-time algorithm for generating compact but relaxable answers https://github.com/nju-websoft/CORE Future work Vertex and/or edge weights ISWC 2020 18 Conclusion