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Related Experiment Video

Updated: Sep 6, 2025

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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Retrieval and Ranking of Combining Ontology and Content Attributes for Scientific Document.

Xinyu Jiang^1,2,3, Bingjie Tian⁴, Xuedong Tian^1,2,3

¹School of Cyber Security and Computer, Hebei University, Baoding 071002, China.

Entropy (Basel, Switzerland)

|June 24, 2022

Summary

This study introduces a novel retrieval and ranking model for scientific documents that integrates mathematical expressions, text context, and ontological attributes. This approach significantly improves the accuracy of mathematical information retrieval and document ranking.

Keywords:

BiLSTM HFS mathematical expressions ontology attributes scientific document retrieval and ranking

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Area of Science:

Information Science
Computer Science
Scientific Document Analysis

Background:

Traditional search models fail to integrate mathematical expressions with ontological attributes, leading to retrieval gaps.
Existing methods lack comprehensive analysis of mathematical expressions and their contextual relevance.

Purpose of the Study:

To develop an advanced retrieval and ranking model for scientific documents incorporating mathematical expressions and ontological attributes.
To bridge the gap between user queries and retrieval results in scientific document search.

Main Methods:

Constructing a hesitant fuzzy set for multi-attribute mathematical expression matching.
Utilizing BiLSTM for calculating sentence similarity within mathematical expression contexts.
Integrating ontological attributes for ranking retrieved scientific documents.

Main Results:

Achieved a MAP_10 of 0.815 for mathematical expression retrieval.
Obtained an average NDCG@10 of 0.9 for scientific document ranking.
Demonstrated the effectiveness of the proposed retrieval and ranking method.

Conclusions:

The developed model effectively synthesizes mathematical expressions, text context, and ontological attributes.
The integration of hesitant fuzzy sets and BiLSTM enhances retrieval accuracy.
The method significantly improves the ranking of scientific documents based on their ontological properties.