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Related Concept Videos

ER Retrieval Pathway01:45

ER Retrieval Pathway

In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
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SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
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SBAR I: Understanding the Concept

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Bar Graph01:07

Bar Graph

A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
Multiple Bar Graph01:07

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Related Experiment Video

Updated: May 29, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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

Published on: June 13, 2025

KBDR: document retrieval based on graph matching with knowledge enhancement.

Jingxuan Liu1, Yihan Huang1, Jialuoyi Tan2

  • 1School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.

Scientific Reports
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Knowledge Based Document Retrieval (KBDR) improves search accuracy by using knowledge graphs to understand document meaning. This approach enhances retrieval efficiency and robustness, overcoming limitations of traditional methods.

Keywords:
Document graph constructionDocument retrievalGraph matchingKnowledge baseKnowledge expansion

Related Experiment Videos

Last Updated: May 29, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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:

  • Information Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Traditional document retrieval struggles with vocabulary synonymy, leading to inaccuracies.
  • Existing matching algorithms often fail to capture semantic relevance beyond exact keyword matches.

Purpose of the Study:

  • To introduce Knowledge Based Document Retrieval (KBDR) for enhanced semantic understanding.
  • To address limitations in current document retrieval systems by incorporating external knowledge.

Main Methods:

  • Constructing document graphs using named entity recognition and relation extraction.
  • Expanding entities with external knowledge bases (HowNet, ConceptNet) for richer semantic connections.
  • Applying a Graph Attention Network (GAT) to model inter-entity dependencies for relevance ranking.

Main Results:

  • KBDR demonstrated superior performance compared to baseline methods across five datasets (CIPP, CNSE, CNSS, AAN, OC).
  • Achieved the highest Recall@1 of 25.88% on the CNSE dataset, indicating significant retrieval improvement.
  • The proposed method enhances efficiency and robustness in document retrieval tasks.

Conclusions:

  • KBDR effectively overcomes conceptual deviations in document retrieval.
  • The graph-based knowledge expansion approach advances the development of more sophisticated retrieval systems.