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Found 6 papers, 1 papers with code
Grouped-Attention for Content-Selection and Content-Plan Generation
A key component of the GSC-attention is grouped-attention, which is token-level attention constrained within each input attribute that enables our proposed model captures both local and global context.
i-Align: an interpretable knowledge graph alignment model
One of the strategies to address this problem is KG alignment, i. e., forming a more complete KG by merging two or more KGs.
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models
In this paper, we propose the first fully automatic alignment method named AutoAlign, which does not require any manually crafted seed alignments.
TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity.
Neural Relation Extraction for Knowledge Base Enrichment
This way, NED errors may cause extraction errors that affect the overall precision and recall. To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model.
GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data
However, this representation is not in a natural language form, which is difficult for humans to understand.
Ranked #12 on
Data-to-Text Generation
on WebNLG
