Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Yang, Cehao"'
Autor:
Li, Muzhi, Yang, Cehao, Xu, Chengjin, Jiang, Xuhui, Qi, Yiyan, Guo, Jian, Leung, Ho-fung, King, Irwin
The Knowledge Graph Completion~(KGC) task aims to infer the missing entity from an incomplete triple. Existing embedding-based methods rely solely on triples in the KG, which is vulnerable to specious relation patterns and long-tail entities. On the
Externí odkaz:
http://arxiv.org/abs/2411.08165
Autor:
Li, Muzhi, Yang, Cehao, Xu, Chengjin, Song, Zixing, Jiang, Xuhui, Guo, Jian, Leung, Ho-fung, King, Irwin
Inductive knowledge graph completion (KGC) aims to predict missing triples with unseen entities. Recent works focus on modeling reasoning paths between the head and tail entity as direct supporting evidence. However, these methods depend heavily on t
Externí odkaz:
http://arxiv.org/abs/2410.16803
Autor:
Ma, Shengjie, Xu, Chengjin, Jiang, Xuhui, Li, Muzhi, Qu, Huaren, Yang, Cehao, Mao, Jiaxin, Guo, Jian
Retrieval-augmented generation (RAG) has enhanced large language models (LLMs) by using knowledge retrieval to address knowledge gaps. However, existing RAG approaches often fail to ensure the depth and completeness of the information retrieved, whic
Externí odkaz:
http://arxiv.org/abs/2407.10805
Artificial intelligence is making significant strides in the finance industry, revolutionizing how data is processed and interpreted. Among these technologies, large language models (LLMs) have demonstrated substantial potential to transform financia
Externí odkaz:
http://arxiv.org/abs/2407.00365
Knowledge Graphs (KGs) are foundational structures in many AI applications, representing entities and their interrelations through triples. However, triple-based KGs lack the contextual information of relational knowledge, like temporal dynamics and
Externí odkaz:
http://arxiv.org/abs/2406.11160