9 citations · 11 across the 6 of their papers we have counts for
7 papers
How to Forget Clients in Federated Online Learning to Rank?
Shuyi Wang, Bing Liu, Guido Zuccon
Data protection legislation like the European Union's General Data Protection Regulation (GDPR) establishes the \textit{right to be forgotten}: a user (client) can request contribu…
Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking
Shengyao Zhuang, Bing Liu, Bevan Koopman +1
In the field of information retrieval, Query Likelihood Models (QLMs) rank documents based on the probability of generating the query given the content of a document. Recently, adv…
Dependency-aware Self-training for Entity Alignment
Bing Liu, Tiancheng Lan, Wen Hua +1
Entity Alignment (EA), which aims to detect entity mappings (i.e. equivalent entity pairs) in different Knowledge Graphs (KGs), is critical for KG fusion. Neural EA methods dominat…
Guiding Neural Entity Alignment with Compatibility
Bing Liu, Harrisen Scells, Wen Hua +3
Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs). While numerous neural EA models have been devised, they are mainly learned using labelled…
High-quality Task Division for Large-scale Entity Alignment
Bing Liu, Wen Hua, Guido Zuccon +2
Entity Alignment (EA) aims to match equivalent entities that refer to the same real-world objects and is a key step for Knowledge Graph (KG) fusion. Most neural EA models cannot be…
Ensemble Semi-supervised Entity Alignment via Cycle-teaching
Kexuan Xin, Zequn Sun, Wen Hua +4
Entity alignment is to find identical entities in different knowledge graphs. Although embedding-based entity alignment has recently achieved remarkable progress, training data ins…
ActiveEA: Active Learning for Neural Entity Alignment
Bing Liu, Harrisen Scells, Guido Zuccon +2
Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods -- neural EA model…