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Ogive Graph01:07

Ogive Graph

6.5K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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RL Circuit without Source01:14

RL Circuit without Source

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When a DC source is suddenly disconnected from an RL (Resistor-Inductor) circuit, the circuit becomes source-free. Assuming the inductor has an initial current denoted as I0, the initial energy stored in the inductor can be determined.
Applying Kirchhoff's voltage law around the loop of the circuit and substituting the voltages across the inductor and resistor yields a first-order differential equation. A logarithmic equation is obtained by rearranging the terms in this equation,...
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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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RL Circuit with Source01:14

RL Circuit with Source

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When an RL (Resistor-Inductor) circuit is connected to a DC source, the complete response of the circuit can be divided into two parts: the transient response and the steady-state response.
The transient response of the circuit is its temporary reaction to the sudden application of the DC source. This response is characterized by a current that exponentially decays to zero as time approaches infinity. During this transitional period, the inductor behaves like a short circuit, causing the source...
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Natural and Artificial Concepts01:24

Natural and Artificial Concepts

473
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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ER Retrieval Pathway01:45

ER Retrieval Pathway

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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.
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...
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Related Experiment Video

Updated: Dec 25, 2025

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

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Deep learning based searching approach for RDF graphs.

Hatem Soliman1

  • 1College of Computer Science & Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.

Plos One
|March 24, 2020
PubMed
Summary

This study introduces a deep learning approach for searching Resource Description Framework (RDF) graphs, improving information retrieval accuracy. The method enhances semantic understanding and surpasses existing techniques for complex data searching.

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Related Experiment Videos

Last Updated: Dec 25, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Information Retrieval

Background:

  • The Internet's complexity poses challenges for accurate information retrieval.
  • Current keyword-based search engines struggle with semantic understanding of content.
  • Existing semantic web approaches require exact matches between RDF content and queries, lacking probabilistic reasoning.

Purpose of the Study:

  • To propose a novel deep learning-based approach for enhanced searching of Resource Description Framework (RDF) graphs.
  • To address the limitations of exact-match retrieval in semantic web technologies.
  • To improve the accuracy and semantic understanding in RDF graph querying.

Main Methods:

  • RDF graphs were preprocessed into N-Triples format.
  • A combination of bag-of-words (BOW) and word2vec techniques was used for deep representation of RDF graphs.
  • A convolutional neural network (CNN) with an attention mechanism was trained for classification and retrieval.

Main Results:

  • The proposed deep learning approach achieved high accuracy (up to 97.12%) in RDF graph retrieval.
  • Evaluation using 10-fold cross-validation demonstrated superior performance over state-of-the-art methods.
  • Key performance metrics included average precision (98.17%), recall (95.56%), and f-measure (96.85%).

Conclusions:

  • The deep learning approach effectively enhances semantic understanding and retrieval accuracy in RDF graphs.
  • This method overcomes the limitations of exact-match querying in semantic web data.
  • The proposed technique offers a robust solution for complex information retrieval challenges on the internet.