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  1. Proceedings of the First International Workshop on Keyword Search on Structured Data (KEYS '09)
  2. Efficient top-k algorithms for fuzzy search in string collections
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Structured data and web documents: better together?
Querying text databases and the web: beyond traditional keyword search
Hierarchical result views for keyword queries over relational databases
Query segmentation using conditional random fields
Building search applications with Marklogic Server
Efficient top-k algorithms for fuzzy search in string collections
Keyword query cleaning using hidden Markov models
XML keyword query refinement
A first study on strategies for generating workflow snippets
Do we mean the same?: disambiguation of extracted keyword queries for database search
Language-model-based ranking in entity-relation graphs
Generic and effective semi-structured keyword search
DBDOC: querying and browsing databases and interrelated documents

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Efficient top-k algorithms for fuzzy search in string collections

Content Provider ACM Digital Library
Author Vernica, Rares Li, Chen
Abstract An approximate search query on a collection of strings finds those strings in the collection that are similar to a given query string, where similarity is defined using a given similarity function such as Jaccard, cosine, and edit distance. Answering approximate queries efficiently is important in many applications such as search engines, data cleaning, query relaxation, and spell checking, where inconsistencies and errors exist in user queries as well as data. In this paper, we study the problem of efficiently computing the best answers to an approximate string query, where the quality of a string is based on both its importance and its similarity to the query string. We first develop a progressive algorithm that answers a ranking query by using the results of several approximate range queries, leveraging existing search techniques. We then develop efficient algorithms for answering ranking queries using indexing structures of gram-based inverted lists. We answer a ranking query by traversing the inverted lists, pruning and skipping irrelevant string ids, iteratively increasing the pruning and skipping power, and doing early termination. We have conducted extensive experiments on real datasets to evaluate the proposed algorithms and report our findings.
Starting Page 9
Ending Page 14
Page Count 6
File Format PDF
ISBN 9781605585703
DOI 10.1145/1557670.1557677
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-06-28
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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