Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Filters
























1 Hit in 2.8 sec

PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs [article]

Ying Su, Jipeng Zhang, Yangqiu Song, Tong Zhang
2024 arXiv   pre-print

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (585.5 kB)
https://web.archive.org/web/20240206213801/https://arxiv.org/pdf/2401.17536v1.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL. The file type is application/pdf.

Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:

2024 pre-print arXiv:2401.17536v1 fatcat:tv5jomp5tbdrpamcjy5hs36wcm
It is well acknowledged that incorporating explicit knowledge graphs (KGs) can benefit question answering.  ...  In this paper, we target at finding semantically related entity nodes in the subgraph to improve the efficiency of graph reasoning with KG.  ...  Conclusion In this work, we propose PipeNet, a groundingpruning-reasoning pipeline for question answering with knowledge graph.  ... 
arXiv:2401.17536v2 fatcat:45g62y7usnaqpf5pcoeorz6yue
Open Access Multiple Versions
Citation

Ying Su, Jipeng Zhang, Yangqiu Song, Tong Zhang. "PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs." arXiv (2024)