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Yongjia Lei

dblp:324/3182 · DBLP profile ↗
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3ranked-venue papers
2as first author
3since 2021 · last 2026
0000-0002-3022-7076ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 2 first-author · 3 since 2021Databases, data management, data science and information retrieval · 2 · 1 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
2 papers
Information retrieval · 53% Data mining · 32% Knowledge graphs · 16%
Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%

Topics — the 3 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
retrieval-augmented generation
1.012026
Rigorizing Retrieval-augmented Generation with Structured Knowledge Intelligence (6 Hrs) · WSDM 2026
Data mining
pattern mining
0.312026
Rule Mining and Learning for Structured Knowledge Retrieval · WSDM 2026
Data mining › pattern mining
rule mining
0.312026
Rule Mining and Learning for Structured Knowledge Retrieval · WSDM 2026

Methods — techniques the papers use, named apart from their topics

rule learning · 2.0retrieval-augmented generation · 1.0
YearPublicationVenuePosition
2026 Rule Mining and Learning for Structured Knowledge Retrieval
Yongjia Lei, Mahantesh Halappanavar, Yu Wang 0160
WSDM1
2026 Rigorizing Retrieval-augmented Generation with Structured Knowledge Intelligence (6 Hrs)
abstract
Retrieving external knowledge to Augment Generations of downstream task solutions (RAGs) has become a standard practice in powering knowledge-intensive applications. However, real-world knowledge often manifests in heterogeneous yet distinctive structures (e.g., tabular schemas, social networks, and document trees), the effective modeling of which demands specialized modeling, practical engineering, and domain expertise. Meanwhile, adopting RAGs in high-stakes scenarios underscores rigorous safety considerations. Despite the importance of this structural perspective, the current landscape remains fragmented. Moreover, few approaches adequately consider how structured knowledge shapes RAG's safety. Against this backdrop, our tutorial offers a structural perspective on RAGs. We begin by overviewing structured RAGs across their full lifecycle, highlighting their canonical designs. We then examine how design principles can be specialized for different knowledge structures, showcasing their unique applications and security attack/defense strategies. The tutorial slide is available https://kindlab-fly.github.io/tutorials/WSDM26/
Zhisheng Qi, Yongjia Lei, Haoyu Han 0001, Harry Shomer, Kaize Ding, Yu Zhang 0044, Ryan Rossi, Hui Liu 0031, Yu Wang 0160
WSDM2
2024 GNN-fused CapsNet with multi-head prediction for diabetic retinopathy grading
Yongjia Lei, Shuyuan Lin, Zhiying Li 0003, Taotao Lai
Eng. Appl. Artif. Intell.1