NDLI: STRUCT: incorporating contextual information for English query search on relational databases

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STRUCT: incorporating contextual information for English query search on relational databases

Content Provider	ACM Digital Library
Author	Chen, Zhengxin Patil, Rajvardhan
Abstract	Research on keyword search in database community has achieved a lot of success, and areas of interests have been moved from keyword search in relational databases to various advanced issues such as keyword search in multimedia data and data streams. Yet, many fundamental issues on keyword search in traditional databases remain. One such issue is how to interpret users' information needs behind keywords they provided. A common approach of many prototype systems is to make such interpretation as a designer's choice (such as imposing AND or OR semantics, or a combination), leaving no choice to the users. A much more meaningful approach would be allowing users themselves to specify the required semantics through contextual information. So can we build a system which stays with the simplicity of Keyword search, yet can incorporate the contextual information provided in the user query? In this paper we introduce STRUCT to explore this idea. STRUCT takes English language queries involving intended keywords; we refer to such search as English query search. Instead of resorting on a full-fledged natural language processing, the unneeded words in the queries are discarded. Only the specific contextual information along with the keywords containing database contents will be used to construct SQL queries. The contextual information is used to interpret the meaning of the queries, including the semantics involving AND, OR and NOT. In this paper we describe the architecture of STRUCT, the procedure of English query processing (parsing), the basic idea of the grouping algorithm, SQL query construction and sample results of experiments.
Starting Page	11
Ending Page	22
Page Count	12
File Format	PDF
ISBN	9781450311984
DOI	10.1145/2254736.2254742
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-05-20
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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