Hierarchical Tree-structured Knowledge Graph:Gain an Insight View for Specific research Topic (On building, plan to finish uploading the notebook in July, pyfile and app in Sep)

0. About this

Target

1. Create a knowledge interface for insights survey assistants from multiple academic papers on a specific research topic.
1. From the origin (root) of the research task, expand the citation inheritance and relevance associations.
1. Explore the relevance chain within similar research tasks to highlight key research points.

Resources

Link of our preprint :
- Arxiv
- ResearchGate
Link of our dataset :
- https://doi.org/10.34740/KAGGLE/DS/4330260

Citation (Current)

Li, J., Phan, H., Gu, W., Ota, K., & Hasegawa, S. (2024). 
Hierarchical Tree-structured Knowledge Graph For Academic Insight Survey. 
arXiv preprint arXiv:2402.04854.

Declaration

The article of this project has been accepted by "THE 18TH INTERNATIONAL CONFERENCE ON INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS (INISTA 2024)", and future citations should be based on the official version.

1. Content

Overview

Phase1 - Text-Processing.ipynb

Perform section & sentence segmentation on the total dataset of ‘HotpotQA’ topic (Hotpotqa_paper_content.csv) to create the corresponding insight sentence dataset. Experts then assign insight labels('Issue Resolved', 'Neutral', and 'Issue Finding' ) to this dataset.

Phase1* - Cite-Net-Construction.ipynb

The cite-net, established based on the citation relationships among papers in the Hotpotqa topic, is presented in Cite_net_total.html as the overall network. The sub-nets within it will be extracted to form the basic prototype of the tree-structured knowledge graph.

Phase2 - Insight-Sentence-Classification.ipynb

This document implements a classifier for Insight sentences. All insight sentences will be classified into 'Issue Resolved', 'Neutral', and 'Issue Finding' categories using machine learning methods. The classification results will be used to establish the relevance chain in the graph.

Phase3&4 - Hierarchical-tree-building-vis.ipynb

Code for tree-building algorithm & Graph Visualization
To maintain consistency of input and output, the code for cite-net construction and similarity calculation has also been added.

Appendix.1 - Freq-dis.ipynb

This document displays the frequent words in the insight section (conclusion, discussion, limitation).

Appendix.2 - Similarity-Calculation.ipynb

Calculate the similarity of the elements of the insight section & relevance chain (‘Issue finding’ → ‘Issue Resolved’) in the paper-network.

Project

To do

App

To do

2. Preparation

Extract content of papers containing the keyword 'HotpotQA' from the S2ORC dataset.
The data of content of papers is in the file Hotpotqa_paper_content.csv in https://doi.org/10.34740/KAGGLE/DS/4330260

3. Example(HTML)

4. User Interface

Implementation of screen transitions and button functions
GUI building via pyqt

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP20H04295.

Notes

If you utilize any resources (Concepts, code, or methods) from this repository, please cite our paper and acknowledge the source code & dataset.

License

This repository is released under the Apache 2.0 license as found in the License file.

Hasegawa-lab-JAIST/LI_JINGHONG_Hierarchical-Tree-structured-Knowledge-Graph-For-Academic-Insight-Survey