Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Deductive Reasoning01:16

Deductive Reasoning

57.2K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
57.2K
Parseval's Theorem01:18

Parseval's Theorem

614
Parseval's theorem is a fundamental concept in signal processing and harmonic analysis. It asserts that for a periodic function, the average power of the signal over one period equals the sum of the squared magnitudes of all its complex Fourier coefficients. This theorem, named after Marc-Antoine Parseval, provides a powerful tool for analyzing the energy distribution in signals.
Interestingly, Parseval's theorem also holds for the trigonometric form of the Fourier series, which...
614
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

946
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
946
Cognitivism01:17

Cognitivism

1.6K
Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
Previously dominated by behaviorism, which prioritized observable behaviors and largely ignored mental processes, psychology transformed in the 1950s. Cognitive psychologists argue that understanding how we think and process...
1.6K
State Space Representation01:27

State Space Representation

269
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
269
Associative Learning01:27

Associative Learning

525
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
525

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning.

Computational intelligence and neuroscience·2022
Same author

Modelling the Latent Semantics of Diffusion Sources in Information Cascade Prediction.

Computational intelligence and neuroscience·2021
Same author

An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images.

PloS one·2020
Same author

Laser energy absorption prediction of silicon substrate surface from a mid- and high-spatial frequency error.

Optics express·2020
Same author

The CXCL11-CXCR3A axis influences the infiltration of CD274 and IDO1 in oral squamous cell carcinoma.

Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology·2020
Same author

CFD investigation on gas-solid two-phase flow of dust removal characteristics for cartridge filter: a case study.

Environmental science and pollution research international·2020
Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
See all related articles

Related Experiment Video

Updated: Aug 26, 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

527

Representation Learning Method with Semantic Propagation on Text-Augmented Knowledge Graphs.

Ling Wang1, Jicang Lu1, Gang Zhou1

  • 1State Key Laboratory of Mathematical Engineering and Advanced Computing, Information Engineering University, Zhengzhou 450001, China.

Computational Intelligence and Neuroscience
|October 7, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces SP-TAG, a novel method for knowledge graph representation learning. SP-TAG enhances accuracy by semantically integrating text data, improving performance especially with limited training data.

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

673
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K

Related Experiment Videos

Last Updated: Aug 26, 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

527
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

673
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K

Area of Science:

  • Artificial Intelligence
  • Data Science
  • Computer Science

Background:

  • Knowledge graph representation learning is crucial for intelligent systems like question answering and recommendation engines.
  • Existing methods often struggle with accuracy due to limited use of auxiliary information.
  • Independent encoding of external data (text, type, time) hinders full semantic integration with knowledge graphs.

Purpose of the Study:

  • To propose a novel method, SP-TAG, for semantic propagation on text-augmented knowledge graphs.
  • To address the challenge of fully integrating external information with knowledge graphs at a semantic level.
  • To improve the accuracy and performance of knowledge graph representation learning.

Main Methods:

  • Constructing a text-augmented knowledge graph by extracting and linking named entities from text descriptions.
  • Employing a graph convolutional network for semantic information propagation between existing and newly extracted entities.
  • Ensuring full integration of text and triple structure through semantic propagation.

Main Results:

  • SP-TAG achieves competitive performance across multiple benchmark datasets.
  • The method demonstrates robust high performance even with a limited number of training samples.
  • Experimental results validate the effectiveness of text augmentation and semantic propagation.

Conclusions:

  • SP-TAG effectively integrates text information with knowledge graphs through semantic propagation.
  • The proposed approach enhances representation learning accuracy, particularly in data-scarce scenarios.
  • Text augmentation and semantic propagation are vital for advancing knowledge graph representation learning.