Yongquan Dong

dblp:27/5175 · also Yong-Quan Dong · DBLP profile ↗
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8ranked-venue papers in the field
2as first author
3since 2021 · last 2026
—ORCID · conflict

Domains — venue-derived; a paper can count in several

Knowledge Engineering, Semantic Web & Information Systems · 5Database Systems & Data Management · 2 (2 first)Information Retrieval & Web Search · 1
YearPublicationVenuePosition
2026 GDCMAD: Graph-based dual-contrastive representation learning for multivariate time series anomaly detection
Mingjing Du 0001, Xiang Jiang 0008, Yongquan Dong
Inf. Sci.5
2024 Multi-hyperplane twin support vector regression guided with fuzzy clustering
Zichen Zhang 0002, Wei-Chiang Hong, Yongquan Dong
Inf. Sci.3
2022 Solving dynamic multi-objective problems using polynomial fitting-based prediction algorithm
Qingyang Zhang 0002, Shengxiang Yang, Yongquan Dong, Shouyong Jiang
Inf. Sci.4
2020 An End-to-End Deep Neural Network for Truth Discovery
Yongquan Dong
WISA2
2019 A Method for Duplicate Record Detection Using Deep Learning
Yongquan Dong
WISA2
2019 Normalization of Duplicate Records from Multiple Sources
abstract
Data consolidation is a challenging issue in data integration. The usefulness of data increases when it is linked and fused with other data from numerous (Web) sources. The promise of Big Data hinges upon addressing several big data integration challenges, such as record linkage at scale, real-time data fusion, and integrating Deep Web. Although much work has been conducted on these problems, there is limited work on creating a uniform, standard record from a group of records corresponding to the same real-world entity. We refer to this task as record normalization. Such a record representation, coined normalized record, is important for both front-end and back-end applications. In this paper, we formalize the record normalization problem, present in-depth analysis of normalization granularity levels (e.g., record, field, and value-component) and of normalization forms (e.g., typical versus complete). We propose a comprehensive framework for computing the normalized record. The proposed framework includes a suit of record normalization methods, from naive ones, which use only the information gathered from records themselves, to complex strategies, which globally mine a group of duplicate records before selecting a value for an attribute of a normalized record. We conducted extensive empirical studies with all the proposed methods. We indicate the weaknesses and strengths of each of them and recommend the ones to be used in practice.
Yongquan Dong, Eduard C. Dragut, Weiyi Meng
IEEE Trans. Knowl. Data Eng.1
2010 MI-WDIS: web data integration system for market intelligence
abstract
As an important supporting technology of Market Intelligence (MI), Web data integration is facing new challenges, such as the integrity of data acquisition, the quality of data extraction and data consolidation. To solve such problems, we propose an MI-oriented web data integration system (MI-WDIS), which achieves excellent performances in integrating Surface Web and Deep Web data with much less manual work. Based on MI-WDIS, we have developed a platform for intelligent analysis of job data. The platform collects tens of thousands of job data daily and provides personalized services for job seekers through diversified channels. Besides, it provides other advanced services, including intelligence analysis, automatic monitoring and alerting, for various organizations, such as enterprises, training institutions and recruitment agencies.
Zhongmin Yan, Qingzhong Li, Shidong Zhang, Zhaohui Peng, Yongquan Dong, Yanhui Ding, Xiuxing Xu
CIKM5
2010 Semantic Annotation of Web Objects Using Constrained Conditional Random Fields
Yongquan Dong, Qingzhong Li, Yongqing Zheng
WAIM1