scholarly journals Finding Top-k Answers in Keyword Search over Relational Databases Using Tuple Units

2011 ◽  
Vol 23 (12) ◽  
pp. 1781-1794 ◽  
Author(s):  
Jianhua Feng ◽  
Guoliang Li ◽  
Jianyong Wang
Keyword(s):  
Relational Databases ◽  
Keyword Search

scholarly journals Keyword Search over the COVID-19 Data

2020 ◽  
Author(s):  
Yenier Torres Izquierdo ◽  
Grettel Monteagudo Garcia ◽  
Melissa Lemos ◽  
Alexandre Novello ◽  
Bruno Novelli ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Relational Databases ◽  
Keyword Search ◽  
Attractive Alternative ◽  
Knowledge Retrieval ◽  
Database Access ◽  
Retrieval Systems ◽  
Information Retrieval Systems

Keyword search is typically associated with information retrieval systems. However, recently, keyword search has been expanded to relational databases and RDF datasets, as an attractive alternative to traditional database access. With this motivation, this paper first introduces a platform for data and knowledge retrieval, called DANKE, concentrating on the keyword search component. It then describes an application that uses DANKE to implement keyword search over two COVID-19 data scenarios.

Audio Retrieval Based on Chinese Keyword Search in Relational Databases

2013 ◽  
Vol 756-759 ◽  
pp. 3236-3240
Author(s):  
Bo Yan Zhu ◽  
Guang Liu ◽  
Liang Zhu
Keyword(s):  
Relational Database ◽  
Relational Databases ◽  
Keyword Search ◽  
Database System ◽  
Experimental Results ◽  
Chinese Characters ◽  
Relational Database System ◽  
Audio Retrieval ◽  
Ranking Strategy ◽  
Audio Files

In this paper, we propose a new method based on Chinese keyword search to select the WAV or MP3 files in audio post-production. First, we listen to each file and label it with Chinese characters, and then classify and store the files in a relational database system. Then, we use the techniques of Chinese keyword search to match query characters and the tuple characters quickly, and to compute similarities between the query and candidate tuples. For the characteristics of Chinese keyword search, we present a ranking strategy and an algorithm to refine the candidate tuples resulting from the first round matching, and finally get top-Nresults of audio files. The experimental results show that our method is efficient and effective.

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