Please use this identifier to cite or link to this item: https://doi.org/10.1145/1376616.1376706
Title: EASE: An effective 3-in-1 keyword search method for unstructured, semi-structured and structured data
Authors: Li, G.
Ooi, B.C. 
Feng, J.
Wang, J.
Zhou, L.
Keywords: Graph index
Indexing
Keyword search
Ranking
Issue Date: 2008
Source: Li, G.,Ooi, B.C.,Feng, J.,Wang, J.,Zhou, L. (2008). EASE: An effective 3-in-1 keyword search method for unstructured, semi-structured and structured data. Proceedings of the ACM SIGMOD International Conference on Management of Data : 903-914. ScholarBank@NUS Repository. https://doi.org/10.1145/1376616.1376706
Abstract: Conventional keyword search engines are restricted to a given data model and cannot easily adapt to unstructured, semi-structured or structured data. In this paper, we propose an efficient and adaptive keyword search method, called EASE, for indexing and querying large collections of heterogenous data. To achieve high efficiency in processing keyword queries, we first model unstructured, semi-structured and structured data as graphs, and then summarize the graphs and construct graph indices instead of using traditional inverted indices. We propose an extended inverted index to facilitate keyword-based search, and present a novel ranking mechanism for enhancing search effectiveness. We have conducted an extensive experimental study using real datasets, and the results show that EASE achieves both high search efficiency and high accuracy, and outperforms the existing approaches significantly. Copyright 2008 ACM.
Source Title: Proceedings of the ACM SIGMOD International Conference on Management of Data
URI: http://scholarbank.nus.edu.sg/handle/10635/40423
ISBN: 9781605581026
ISSN: 07308078
DOI: 10.1145/1376616.1376706
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

241
checked on Dec 13, 2017

Page view(s)

142
checked on Dec 16, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.