activity
20172022
most citedLearning to Exploit Long-term Relational Dependencies in Knowledge Graphs

106 citations · 246 across the 20 of their papers we have counts for

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Showing cs.CLShow all

10 papers · 1 filter

cs.CL2021

Principled Representation Learning for Entity Alignment

Lingbing Guo, Zequn Sun, Mingyang Chen +3

Embedding-based entity alignment (EEA) has recently received great attention. Despite significant performance improvement, few efforts have been paid to facilitate understanding of…

cs.CL20211 cited

Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction

Kailong Hao, Botao Yu, Wei Hu

Distantly supervised relation extraction (RE) automatically aligns unstructured text with relation instances in a knowledge base (KB). Due to the incompleteness of current KBs, sen…

cs.CL20211 cited

Knowing the No-match: Entity Alignment with Dangling Cases

Zequn Sun, Muhao Chen, Wei Hu

This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alig…

cs.CL202010 cited

Knowledge Association with Hyperbolic Knowledge Graph Embeddings

Zequn Sun, Muhao Chen, Wei Hu +3

Capturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits NLP applications with comprehensive knowledge rep…

cs.CL20206 cited

Global-to-Local Neural Networks for Document-Level Relation Extraction

Difeng Wang, Wei Hu, Ermei Cao +1

Relation extraction (RE) aims to identify the semantic relations between named entities in text. Recent years have witnessed it raised to the document level, which requires complex…

cs.CL2020

A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs

Zequn Sun, Qingheng Zhang, Wei Hu +4

Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the same real-world object. Recent advancement in KG embedding impels the advent of embedd…