Papers with large-scale knowledge graph construction

2 papers
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction (2020.acl-main)

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Challenge: Existing approaches to extract relational triples from unstructured text are inadequate to solve the overlapping triple problem.
Approach: They propose a cascade binary tagging framework that models relations as functions that map subjects to objects in a sentence.
Outcome: The proposed framework outperforms state-of-the-art methods on two datasets . it outperformed baseline methods by 17.5 and 30.2 absolute gains .
A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach (2022.emnlp-main)

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Challenge: Existing methods do not consider qualifier attributes for each relation triplet, such as time, quantity or location.
Approach: They propose a hyper-relational extraction task to extract more specific facts from text using qualifiers.
Outcome: The proposed model outperforms baselines and reveal possible directions for future research.

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