most citedPRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction

12 citations · 24 across the 7 of their papers we have counts for

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cs.CL202112 cited

PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction

Hengyi Zheng, Rui Wen, Xi Chen +7

Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer fro…

cs.CL20212 cited

Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding

Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen +4

Knowledge Graph (KG) alignment is to discover the mappings (i.e., equivalent entities, relations, and others) between two KGs. The existing methods can be divided into the embeddin…

cs.CL20214 cited

PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System

Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen +2

Knowledge Graph (KG) alignment aims at finding equivalent entities and relations (i.e., mappings) between two KGs. The existing approaches utilize either reasoning-based or semanti…

cs.CL20213 cited

OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding

Yuejia Xiang, Ziheng Zhang, Jiaoyan Chen +3

Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities. Current methods have explored and utilized the graph structure, the entity names and att…

cs.CL20212 cited

Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management

Zhengxu Hou, Bang Liu, Ruihui Zhao +4

For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the spa…

cs.CL20201 cited

An Industry Evaluation of Embedding-based Entity Alignment

Ziheng Zhang, Jiaoyan Chen, Xi Chen +4

Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of u…