Titlebook: Knowledge Graph and Semantic Computing: Knowledge Graph Empowers the Digital Economy; 7th China Conference Maosong Sun,Guilin Qi,Yubo Chen

過分自信

Document-Level Relation Extraction with?a?Dependency Syntax Transformer and?Supervised Contrastive Live learning with fusion knowledge captures global information among relational triples. Gaussian probability distributions are also designed to capture local information around entities. Our experiments on two document-level relation extraction datasets, CDR and GDA, have remarkable results.

surmount

cocoon

Incorporating Uncertainty of?Entities and?Relations into?Few-Shot Uncertain Knowledge Graph Embeddinion of entities and relations in the few-shot scenario. Experimental results show that our proposed method can learn better embeddings in terms of the higher accuracy in both confidence score prediction and tail entity prediction.

套索

narcissism

KGSG: Knowledge Guided Syntactic Graph Model for Drug-Drug Interaction Extractionatical relation. We conducted comparative experiments and ablation studies on the DDI extraction 2013 dataset. The experimental results show that our method can effectively integrate domain knowledge and syntactic information to improve the performance of DDI extraction compared with the existing methods.

止痛藥

Conference proceedings 2022n and knowledge base construction; linked data, knowledge integration, and knowledge graph storage managements; natural language understanding and semantic computing; knowledge graph applications; and knowledge graph open resources..

inscribe

膠狀

字的誤用

https://doi.org/10.1007/978-981-19-7596-7artificial intelligence; semantics; natural language processing; natural languages; information retrieva

Migratory