Zobrazeno 1 - 10
of 422
pro vyhledávání: '"Xu Yajing"'
Publikováno v:
Di-san junyi daxue xuebao, Vol 43, Iss 21, Pp 2302-2306 (2021)
Objective To investigate the effect and safety of iron chelation therapy on hematopoiesis in hematological malignancy patients with iron overload after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Methods The clinical data of 38 he
Externí odkaz:
https://doaj.org/article/aff6134d7f3e4637b48cc93cef7c13a5
Graph Neural Network (GNN), with the main idea of encoding graph structure information of graphs by propagation and aggregation, has developed rapidly. It achieved excellent performance in representation learning of multiple types of graphs such as h
Externí odkaz:
http://arxiv.org/abs/2407.02762
Autor:
Zhang, Wen, Xu, Yajing, Ye, Peng, Huang, Zhiwei, Xu, Zezhong, Chen, Jiaoyan, Pan, Jeff Z., Chen, Huajun
Knowledge graph (KG) completion aims to find out missing triples in a KG. Some tasks, such as link prediction and instance completion, have been proposed for KG completion. They are triple-level tasks with some elements in a missing triple given to p
Externí odkaz:
http://arxiv.org/abs/2406.18166
Autor:
Zhang, Yichi, Chen, Zhuo, Guo, Lingbing, Xu, Yajing, Hu, Binbin, Liu, Ziqi, Zhang, Wen, Chen, Huajun
Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks within the MMKGs, such as MMKG completion (MMKGC). The main challenge is to
Externí odkaz:
http://arxiv.org/abs/2405.16869
Autor:
Zhang, Yichi, Chen, Zhuo, Guo, Lingbing, Xu, Yajing, Hu, Binbin, Liu, Ziqi, Chen, Huajun, Zhang, Wen
Multi-modal knowledge graphs (MMKG) store structured world knowledge containing rich multi-modal descriptive information. To overcome their inherent incompleteness, multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge
Externí odkaz:
http://arxiv.org/abs/2404.09468
Autor:
Zhang, Yichi, Chen, Zhuo, Guo, Lingbing, Xu, Yajing, Hu, Binbin, Liu, Ziqi, Zhang, Wen, Chen, Huajun
Multi-modal knowledge graph completion (MMKGC) aims to automatically discover the unobserved factual knowledge from a given multi-modal knowledge graph by collaboratively modeling the triple structure and multi-modal information from entities. Howeve
Externí odkaz:
http://arxiv.org/abs/2406.17605
Publikováno v:
Turkish Journal of Hematology, Vol 34, Iss 1, Pp 10-15 (2017)
Objective: Previous studies compared the predictive ability of the European Treatment Outcome Study (EUTOS), Sokal, and Hasford scoring systems and demonstrated inconsistent findings with unknown reasons. This study was conducted to determine a usefu
Externí odkaz:
https://doaj.org/article/9c0da89229074bcca1306f59a93791de
Large language model (LLM) based knowledge graph completion (KGC) aims to predict the missing triples in the KGs with LLMs. However, research about LLM-based KGC fails to sufficiently harness LLMs' inference proficiencies, overlooking critical struct
Externí odkaz:
http://arxiv.org/abs/2310.06671
Autor:
Liu, Xingxian, Xu, Yajing
Query-focused meeting summarization(QFMS) aims to generate a specific summary for the given query according to the meeting transcripts. Due to the conflict between long meetings and limited input size, previous works mainly adopt extract-then-summari
Externí odkaz:
http://arxiv.org/abs/2305.12753
Query-focused meeting summarization (QFMS) aims to generate summaries from meeting transcripts in response to a given query. Previous works typically concatenate the query with meeting transcripts and implicitly model the query relevance only at the
Externí odkaz:
http://arxiv.org/abs/2303.04487