What question did this study set out to answer?

To develop a framework that automates the extraction and structuring of earthquake-related information from diverse web sources.

May 7, 2026ComputersOpen Access

A Self-Adaptive LLM-Based Framework for Automated Extraction and Structuring of Earthquake Information from Heterogeneous Web Sources

View Full Paper

Ask AI

Bookmark

To develop a framework that automates the extraction and structuring of earthquake-related information from diverse web sources.
Proposed a self-adaptive framework based on large language models for information extraction.
Integrated transformer-based schema generation and repository-guided schema matching.
Utilized an iterative refinement mechanism to adapt to varying document structures.
Achieved a success rate of 85% across evaluated models.
Best-performing configuration reached an extraction accuracy of 96.5%.
Significantly reduced false positives and false negatives in information extraction.

Discussion

No takes yet. Share an insight, caveat, or question.

Implication

The proposed framework achieves 85% success in extracting earthquake data in real-time from heterogeneous web sources, indicating scalable solutions for safety-critical domains.

Cite This Study

Turarbek et al. (2026) studied this question.

synapsesocial.com/papers/69fbf004164b5133a91a4297 https://doi.org/10.3390/computers15050294

View Full Paper

Ask AI

Bookmark

Implication

The proposed framework achieves 85% success in extracting earthquake data in real-time from heterogeneous web sources, indicating scalable solutions for safety-critical domains.

A Self-Adaptive LLM-Based Framework for Automated Extraction and Structuring of Earthquake Information from Heterogeneous Web Sources

Key Points

Discussion

Implication

Cite This Study

A Self-Adaptive LLM-Based Framework for Automated Extraction and Structuring of Earthquake Information from Heterogeneous Web Sources

Key Points

Discussion

Implication

Cite This Study

Also Consider

Also Consider