49 citations · 136 across the 14 of their papers we have counts for
15 papers
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…
Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search
Shuai Wang, Harrisen Scells, Bevan Koopman +1
High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for releva…
How does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval?
Hang Li, Ahmed Mourad, Bevan Koopman +1
Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation…
Is Non-IID Data a Threat in Federated Online Learning to Rank?
Shuyi Wang, Guido Zuccon
In this perspective paper we study the effect of non independent and identically distributed (non-IID) data on federated online learning to rank (FOLTR) and chart directions for fu…
To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers
Hang Li, Shuai Wang, Shengyao Zhuang +4
Current pre-trained language model approaches to information retrieval can be broadly divided into two categories: sparse retrievers (to which belong also non-neural approaches suc…