Weight-Adjustable Ranking for Keyword Search in  Relational Databases

機構典藏 > College of Business and Management > Graduate Institute & Department of Information Management > Proceeding > Item 987654321/115342

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/115342

Title:	Weight-Adjustable Ranking for Keyword Search in Relational Databases
Authors:	Chichang Jou, Sian Lun Lau
Keywords:	Keyword Search, Information Retrieval, Ranking, Mean Average Precision, Rank Reciprocal Difference
Date:	2018-08-08
Issue Date:	2018-10-23 12:11:49 (UTC+8)
Abstract:	Huge volumes of invaluable information are hidden behind web relational databases. They could not be extracted by search engines. The problem is especially severe for long text data, for example: book reviews, company descriptions, and product specifications. Many researches have investigated to integrate information retrieval and database indexing technologies to provide keyword search functionality for these useful contents. Due to diversifying data relationships in application domains and miscellaneous personal preferences, current ranking results of related researches do not satisfy user requirements. We design and implement a Weight-Adjustable Ranking for Keyword Search (WARKS) system to address the issue. Mean average precision (MAP) and mean rank reciprocal difference (MRRD) are proposed as measurements of ranking effectiveness. We use an integrated international trade show database as our experimental domain. User study demonstrates that WARKS performs better than previous practices.
Relation:	NA
Appears in Collections:	[Graduate Institute & Department of Information Management] Proceeding

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