Zhong Zeng

Wuhan University

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Tok Wang Ling

Neusoft (China)

Mong Li Lee

National University of Singapore

Bing Yang

Wuhan University

Thuy Ngoc Le

National University of Singapore

Xiangyu Zhang

Wuhan University

Zhifeng Bao

MIT University

Xiaomei Zeng

Wuhan University

Vasiliy Pelenovich

Wuhan University

Gillian Dobbie

University of Auckland

A.D. Pogrebnjak

Sumy State University

Cooperative Institutions

Wuhan University

Chinese Academy of Sciences

National University of Singapore

Central South University

Wuhan University of Technology

Zhejiang University

Huazhong University of Science and Technology

Guizhou Aerospace Power Science & Tech (China)

Ministry of Agriculture and Rural Affairs

Huazhong Agricultural University

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A Semantic Approach to Keyword Search over Relational Databases

Lecture notes in computer science (2013)

Zhong Zeng Zhifeng Bao Mong Li Lee Tok Wang Ling

Schema (genetic algorithms)

Entity–relationship model

Semantic Search

10.1007/978-3-642-41924-9_21

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Citations (17)

Towards An Interactive Keyword Search over Relational Databases

Zhong Zeng Zhifeng Bao Mong Li Lee Tok Wang Ling

Keyword search over relational databases has been widely studied for the exploration of structured data in a user-friendly way. However, users typically have limited domain knowledge or are unable to precisely specify their search intention. Existing methods find the minimal units that contain all the query keywords, and largely ignore the interpretation of possible users' search intentions. As a result, users are often overwhelmed with a lot of irrelevant answers. Moreover, without a visually pleasing way to present the answers, users often have difficulty understanding the answers because of their complex structures. Therefore, we design an interactive yet visually pleasing search paradigm called ExpressQ. ExpressQ extends the keyword query language to include keywords that match meta-data, e.g., names of relations and attributes. These keywords are utilized to infer users' search intention. Each possible search intention is represented as a query pattern, whose meaning is described in human natural language. Through a series of user interactions, ExpressQ can determine the search intention of the user, and translate the corresponding query patterns into SQLs to retrieve answers to the query. The ExpressQ prototype is available at http://expressq.comp.nus.edu.sg.

Search-oriented architecture

10.1145/2740908.2742830

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Citations (7)

ExpressQ

Zhong Zeng Zhifeng Bao Thuy Ngoc Le Mong Li Lee Wang Tok Ling

Keyword search in relational databases has gained popularity due to its ease of use. However, the challenge to return query answers that satisfy users' information need remains. Traditional keyword queries have limited expressive capability and are ambiguous. In this work, we extend keyword queries to enhance their expressive power and describe an semantic approach to process these queries. Our approach considers keywords that match meta-data such as the names of relations and attributes, and utilizes them to provide the context of subsequent keywords in the query. Based on the ORM schema graph which captures the semantics of objects and relationships in the database, we determine the objects and relationships referred to by the keywords in order to infer the search target of the query. Then, we construct a set of minimal connected graphs called query patterns, to represent user's possible search intentions. Finally, we translate the top-k ranked query patterns into SQL statements in order to retrieve information that the user is interested in. We develop a system prototype called ExpressQ to process the extended keyword queries. Experimental results show that our system is able to generate SQL statements that retrieve user intended information effectively.

Schema (genetic algorithms)

Spatial Query

10.1145/2661829.2661870

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Citations (14)

Machine Learning-Based Design of Superhard High-Entropy Nitride Coatings

ACS Applied Materials & Interfaces (2024)

Xiangyu Zhang Binyuan Jia Zhong Zeng Xiaomei Zeng Qiang Wan

Limited by the inefficiency of the conventional trial-and-error method and the boundless compositional design space of high-entropy alloys (HEAs), accelerating the discovery of superior-performing high-entropy nitride (HEN) coatings remains a formidable challenge. Herein, the superhard HEN coatings were designed and prepared using the rapidly developing data-driven model machine learning (ML). A database containing hardness and different features of HEN coatings was established and categorized into four subsets covering the information on composition, composition-physical descriptors, composition-technique parameters, and composition-physical descriptors-technique parameters. Feature engineering was employed to reduce dimensionality and interpret the impact of features on the evolution of hardness. Both root mean squared error (RMSE) and decision coefficient (

10.1021/acsami.4c05427

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Citations (3)

Answering Keyword Queries involving Aggregates and GROUPBY on Relational Databases.

Extending Database Technology (2016)

Zhong Zeng Mong Li Lee Tok Wang Ling

Keyword search over relational databases has gained popularity as it provides a user-friendly way to explore structured data. Current research in keyword search has largely ignored queries to retrieve statistical information from the database. The work in [13] extends keywords by supporting aggregate functions in their SQAK system. However, SQAK does not consider the semantics of objects and relationships in the database, and thus suffers from the problems of returning incorrect answers. In this work, we propose a semantic approach to answer keyword queries involving aggregates and GROUPBY. Our approach utilizes the ORM schema graph to capture the Object-Relationship-Attribute (ORA) semantics in the database, and determines the various interpretations of a query before generating the corresponding SQL statements. These semantics enable us to distinguish objects with the same attribute value and detect duplications of objects in relationships to compute the answers correctly. Our approach can also handle unnormalized relations in the database and GROUPBY in keyword queries which SQAK cannot. Experiments on the TPC-H and ACM Digital Library publication datasets demonstrate the advantages of the proposed semantic approach in retrieving correct statistical information for users.

Schema (genetic algorithms)

Source

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Citations (11)

The Research and Application of Data Extraction of Internet-Based Enrollment System

Computer Science (2006)

Zhong Zeng

During the process of its use,the enrollment management system adopted by Chinese universities is found that the functions following the submission of students' file is rather simple since a limited amount of data can be shared.This paper deals with a careful analysis of the system,on the basis an improved system is proposed with view to wholly presenting students' e-files and to facilitating universities' follow-up management.The new system has un dergone 4-year trial use and betterment and proved to meet the needs for basic information such as enrollment,registra- tion of freshmen and improve the quality and efficiency of data processing.It also lays a strong foundation for the con- struction of basic database in digitalized campus program.

Source

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Citations (0)

Towards an intelligent keyword search over XML and relational databases

Tok Wang Ling Thuy Ngoc Le Zhong Zeng

Keyword search has been the major form of retrieval method in information retrieval system, and has become an important way for novice to explore data-centric XML and relational databases (RDB). Recent years have witnessed many approaches proposed for keyword search over XML and RDB. However, those approaches cannot intelligently exploit hidden semantics in XML or RDB, and thus encounter serious problems in processing keyword queries. In this paper, we point out mismatches between query answers returned by existing approaches and the common expectations in keyword search over XML and RDB. We analyze these mismatches and discover that the main reasons are due to the unawareness of semantics of object, relationship and attribute in databases. To capture these semantics, we construct Object Relationship (OR) data graph for XML and Object Relationship Mixed (ORM) data graph for RDB, and propose an intelligent keyword search based on OR and ORM data graph model to retrieve more informative answers. Finally, to further facilitate the usability of keyword search, we also show our ongoing work to enhance the expressive power of keyword queries. Particularly, we 1) enable users to explicitly indicate their search intentions by relation, attribute and tag names in keyword queries; 2) handle recursive relationships and identifier-dependency relationships (IDD) in databases; and 3) incorporate aggregate function into keyword queries so that users can explore databases with aggregate queries.

Keyword density

10.1109/bigcomp.2014.6741406

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Citations (3)

Study on the multiple attribute evaluation of technological renovation project: Based on mixed indexes

Biying Xiong Zhong Zeng Kun Wan Bin Zhang Ruiyu Liu

Technological renovation project is one kind of expanded reproduction within the electrical grid enterprises to achieve technological progress. The success of a technological renovation project, to a great extent, depends on the effective evaluation and selection of alternatives in the earlier stage. However, both certain and uncertain attributes are included in the assessment of alternatives, which could be a difficulty in evaluating project. Based on previous literatures, this paper comes up with a two-step multiple attribute evaluation approach to address the coexistence of certain and uncertain attributes. Finally, an illustrative example project is provided to verify the effectiveness, practicality and versatility of the developed approach.

10.1109/iciii.2013.6702950

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Citations (0)

Structure and mechanical properties of multi-principal AlCrNbSiTiON oxynitride coatings

Vacuum (2024)

Jie Liu Xiangyu Zhang Vasiliy Pelenovich Xiaomei Zeng Zhong Zeng

10.1016/j.vacuum.2024.113366

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Citations (4)

Finding Missing Answers due to Object Duplication in XML Keyword Search

Lecture notes in computer science (2014)

Thuy Ngoc Le Zhong Zeng Tok Wang Ling

10.1007/978-3-319-10073-9_12

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Citations (1)