Summary of Banglaautokg: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering, by Azmine Toushik Wasi and Taki Hasan Rafi and Raima Islam and Dong-kyu Chae
BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering
by Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam, Dong-Kyu Chae
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Social and Information Networks (cs.SI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A proposed framework, BanglaAutoKG, aims to automatically construct Knowledge Graphs (KGs) for Bengali texts, addressing a scarcity in this language. The approach utilizes multilingual Language Models to understand entities and relations across languages. By leveraging pre-trained BERT models, translation dictionaries, graph-based polynomial filters, and GNN-based semantic filters, the framework constructs semantically enriched KGs from any Bangla text. Empirical findings demonstrate the effectiveness of BanglaAutoKG in autonomously building KGs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BanglaAutoKG is a new way to create special networks called Knowledge Graphs (KGs) for Bengali texts. Right now, there are not many KGs for this language because it’s hard to find good data and models that can work with Bengali. The team behind BanglaAutoKG wants to change this by creating a system that can automatically make KGs from any Bengali text. They’re using special computer models called multilingual Language Models to understand how words relate to each other in different languages. Then, they’re using filters to clean up the data and make sure it’s accurate. This new framework can help people process information more efficiently and discover new things. |
Keywords
* Artificial intelligence * Bert * Gnn * Translation
