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  1. Proceedings of Workshop on GRAph Data management Experiences and Systems (GRADES'14)
  2. Towards a Query-by-Example System for Knowledge Graphs
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Graph Processing on an "almost" Relational Database
MapGraph: A High Level API for Fast Development of High Performance Graph Analytics on GPUs
HelP: High-level Primitives For Large-Scale Graph Processing
Asymmetry in Large-Scale Graph Analysis, Explained
PGX.ISO: Parallel and Efficient In-Memory Engine for Subgraph Isomorphism
Using semijoin programs to solve traversal queries in graph databases
How community-like is the structure of synthetically generated graphs?
Graph Pattern Matching: Do We Have to Reinvent the Wheel?
GRATIN: Accelerating Graph Traversals in Main-Memory Column Stores
A Highly Efficient Runtime and Graph Library for Large Scale Graph Analytics
Towards a Query-by-Example System for Knowledge Graphs
Toward Representation Independent Similarity Search Over Graphs

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©silberschatz, Korth and Sudarshan 5.1 Database System Concepts Query-by-example (qbe) Query Query--by By--example (qbe) Example (qbe)

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Towards a Query-by-Example System for Knowledge Graphs

Content Provider ACM Digital Library
Author Li, Chengkai Khan, Arijit Elmasri, Ramez Yan, Xifeng Jayaram, Nandish
Abstract We witness an unprecedented proliferation of knowledge graphs that record millions of heterogeneous entities and their diverse relationships. While knowledge graphs are structure-flexible and content-rich, it is difficult to query them. The challenge lies in the gap between their overwhelming complexity and the limited database knowledge of non-professional users. If writing structured queries over "simple" tables is difficult, it gets even harder to query complex knowledge graphs. As an initial step toward improving the usability of knowledge graphs, we propose to query such data by example entity tuples, without requiring users to write complex graph queries. Our system, GQBE (Graph Query By Example), is a proof of concept to show the possibility of this querying paradigm working in practice. The proposed framework automatically derives a hidden query graph based on input query tuples and finds approximate matching answer graphs to obtain a ranked list of top-k answer tuples. It also makes provisions for users to give feedback on the presented top-k answer tuples. The feedback is used to refine the query graph to better capture the user intent. We conducted initial experiments on the real-world Freebase dataset, and observed appealing accuracy and efficiency. Our proposal of querying by example tuples provides a complementary approach to the existing keyword-based and query-graph-based methods, facilitating user-friendly graph querying. To the best of our knowledge, GQBE is among the first few emerging systems to query knowledge graphs by example entity tuples.
Starting Page 1
Ending Page 6
Page Count 6
File Format PDF
ISBN 9781450329828
DOI 10.1145/2621934.2621937
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-06-22
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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