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Related Experiment Video

Updated: Apr 17, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Exploiting semantic annotations and Q-learning for constructing an efficient hierarchy/graph texts organization.

Asmaa M El-Said¹, Ali I Eldesoky¹, Hesham A Arafat¹

¹Department of Computers and Systems, Faculty of Engineering, Mansoura University, Mansoura, Egypt.

Thescientificworldjournal

|February 17, 2015

Summary

This study introduces a new text mining method using semantic annotation and Q-learning for better knowledge discovery. It improves text analysis by understanding word meanings and relationships, leading to enhanced information retrieval.

Related Experiment Videos

Last Updated: Apr 17, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Natural Language Processing
Information Retrieval
Machine Learning

Background:

Conventional text mining relies on keyword frequency, limiting semantic understanding.
The exponential growth of textual data necessitates advanced analysis techniques.

Purpose of the Study:

To propose an efficient methodology for text organization and representation.
To enhance knowledge discovery from textual documents using semantic understanding.

Main Methods:

Developing a hierarchy/graph-based text organization scheme.
Utilizing semantic annotation and Q-learning for text representation.
Inferring conceptual dependencies and measuring text relatedness.

Main Results:

The proposed representation scheme accurately reflects conceptual relationships.
Improved relatedness measurements between text documents.
Enhanced text mining performance demonstrated through extensive experiments.

Conclusions:

The semantic-based approach significantly improves text mining capabilities.
The methodology offers a more effective way to explore and analyze large text datasets.
Experimental results validate the importance and effectiveness of the proposed approach.