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SE-MSLC: Semantic Entropy-Driven Keyword Analysis and Multi-Stage Logical Combination Recall for Search Engine.

Haihua Lu1, Liang Yu1,2,3, Yantao He1

  • 1School of Computer Science, Guangdong University of Science and Technology, Dongguan 523083, China.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

This study introduces a new information retrieval framework (SE-MSLC) using semantic entropy to boost search engine accuracy. The SE-MSLC framework enhances keyword analysis and recall strategies for better user intent recognition and search results.

Keywords:
keyword importance analysislogical combination recallmulti-stage recallsearch enginesemantic entropy

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Area of Science:

  • Computer Science
  • Information Science

Background:

  • Information retrieval is crucial for accessing data.
  • Existing methods struggle with accuracy in vertical domain search engines.

Purpose of the Study:

  • To propose an information retrieval framework (SE-MSLC) that enhances retrieval effectiveness using information theory.
  • To improve the quality of search results in intelligent vertical domain search engines.

Main Methods:

  • Developed a semantic entropy-driven keyword importance analysis (SE-KIA) to dynamically weight query keywords based on logs, corpus, and semantic entropy.
  • Implemented a hybrid recall strategy (HRS-MSLC) combining multi-stage and logical combination strategies for multi-granularity word segmentation and "AND"/"OR" logic.
  • Managed retrieval uncertainty by prioritizing keywords with high information content.

Main Results:

  • The SE-MSLC framework improved Hit Rate@1 by 7.3% and Hit Rate@3 by 6.6%.
  • Effectively addressed and resolved problematic cases within the vertical domain search engine.
  • Demonstrated a superior balance between retrieval result quantity and relevance.

Conclusions:

  • The proposed SE-MSLC framework significantly enhances information retrieval effectiveness in vertical domains.
  • Semantic entropy and advanced recall strategies are key to improving search engine performance and user satisfaction.