Titlebook: Entity Alignment; Concepts, Recent Adv Xiang Zhao,Weixin Zeng,Jiuyang Tang Book‘‘‘‘‘‘‘‘ 2023 The Editor(s) (if applicable) and The Author(s

CLAST

書目名稱Entity Alignment影響因子(影響力)




書目名稱Entity Alignment影響因子(影響力)學(xué)科排名




書目名稱Entity Alignment網(wǎng)絡(luò)公開度




書目名稱Entity Alignment網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Entity Alignment被引頻次




書目名稱Entity Alignment被引頻次學(xué)科排名




書目名稱Entity Alignment年度引用




書目名稱Entity Alignment年度引用學(xué)科排名




書目名稱Entity Alignment讀者反饋




書目名稱Entity Alignment讀者反饋學(xué)科排名

Banister

FELON

Long-Tail Entity Alignment majority of entities have a sparse neighborhood structure, while only a few entities are densely connected to others. These less-connected entities are referred to as ., and this phenomenon limits the effectiveness of using structural information for entity alignment..To address this issue, we prop

施舍

Weakly Supervised Entity Alignment calls for the study of EA with .. To resolve this issue, we put forward a . entity alignment framework to select the entities to be manually labeled with the aim of enhancing alignment performance with minimal labeling efforts. Under this framework, we further devise an unsupervised . loss to contr

jet-lag

CRACK

Multimodal Entity Alignment to combine these different forms of information into a knowledge graph, creating a multi-modal knowledge graph (MMKG). However, multi-modal knowledge graphs (MMKGs) often face issues of insufficient data coverage and incompleteness. In order to address this issue, a possible strategy is to incorpor

CRACK

使害羞

Book‘‘‘‘‘‘‘‘ 2023t knowledge graphs. Entity alignment represents an essential step in enhancing the quality of knowledge graphs, and hence is of significance to downstream applications, e.g., question answering and recommender systems. Recent years have witnessed a rapid increase in the number of entity alignment fr

信徒

干旱

Weakly Supervised Entity Alignmentast different views of entity representations and augment the limited supervision signals by exploiting the vast unlabeled data. We empirically evaluate our proposal on eight popular KG pairs, and the results demonstrate that our proposed model and its components consistently boost the alignment performance under scarce supervision.