BLINKS: ranked keyword searches on graphs

By Haixun Wang, Jun Yang, Philip S. Yu. ACM SIGMOD Conference, 2007

651 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/305b8eb917d02c8569f0b2cf93bc91a28810a001

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Xuemin Lin
2. S. Rudolph
3. Jeffrey Xu Yu
4. Jianhua Feng
5. Lu Qin
6. Beng Chin Ooi
7. W. Zhang
8. Reynold Cheng

In Computer Vision and Pattern Recognition

1. Jeffrey Xu Yu
2. Lu Qin
3. Beng Chin Ooi
4. W. Zhang
5. Reynold Cheng
6. Yi Luo
7. Jiafeng Hu
8. Jianyong Wang

In Signal Processing

1. Jianhua Feng
2. Guoliang Li
3. Xuemin Lin
4. S. Rudolph
5. Jeffrey Xu Yu
6. Lu Qin
7. Beng Chin Ooi
8. W. Zhang

Citations in the corpus

• EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data (2008, methodology): “[12] proposed a partition-based method to improve search efficiency with a novel BLINKS index.”
• Spark: top-k keyword query in relational databases (2007, background): “Some advanced features, such as schema term awareness and phrase-based ranking, are also proposed [10], [26], [27], [29], [30], [32], [34], [35], [37], [40], [46]–[51].”
• Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data (2009, methodology): “As noted in [2], this has the advantage that the preferred graphs exhibit tighter connections between keyword elements.”
• A survey of community search over big graphs (2019, methodology): “ner trees. In [79], Golenberg et al. presented a novel algorithm which produces Steiner trees with polynomial delay. In [108], Kacholia et al. proposed a bidirectional search algorithm, and He et al. [90] improved it by introducing a new index structure. Recently, some solutions have output subgraph”
• Keyword Search in Relational Databases: A Survey (2010, methodology): “BLINKS [13] is proposed as a bi-level index to speed up BANKS-II, as no index (except the keyword-node index) is used in BANKS-II.”
• Effective Community Search for Large Attributed Graphs (2016, background): “, Flickr, Facebook, and Twitter), the topic of attributed graphs has attracted attention from industry and research communities [29, 3, 6, 14, 16, 33, 17, 10].”