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

Ogive Graph01:07

Ogive Graph

5.6K
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...
5.6K
Bar Graph01:07

Bar Graph

16.4K
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...
16.4K
Multiple Bar Graph01:07

Multiple Bar Graph

5.1K
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...
5.1K
Self-Schemas02:16

Self-Schemas

31.1K
In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
31.1K
Self-Evaluation: Self-Enhancement and Self-Verification03:00

Self-Evaluation: Self-Enhancement and Self-Verification

5.2K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
5.2K
The Representativeness Heuristic02:13

The Representativeness Heuristic

15.8K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
15.8K

You might also read

Related Articles

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

Sort by
Same author

Crosstalk in the brain tumor microenvironment: mechanisms, therapeutic strategies, and clinical advances.

Military Medical Research·2026
Same author

Real-time dynamic monitoring of rockfalls with PTZ cameras.

Scientific reports·2026
Same author

Physics-Informed Neural Network-Based Elevator Degradation Diagnosis and Early Warning.

Sensors (Basel, Switzerland)·2026
Same author

Spontaneous gastrocutaneous fistula presenting as a long-standing abdominal wall nodule: a case report.

Frontiers in medicine·2026
Same author

ERO1A as a novel biomarker for risk stratification and immunotherapeutic guidance in early-stage lung adenocarcinoma.

PeerJ·2026
Same author

HyperG-PS: Voxel correlation modeling via hypergraph for LiDAR panoptic segmentation.

Fundamental research·2026
Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026
Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026
Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026
Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026
Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026
Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jun 27, 2025

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

1.6K

A novel customizing knowledge graph evaluation method for incorporating user needs.

Ying Zhang1, Gang Xiao2

  • 1Institute of Systems Engineering, Academy of Military Sciences (AMS), Beijing, 100107, China. yingzhang199608@foxmail.com.

Scientific Reports
|April 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel knowledge graph accuracy assessment method. It prioritizes user needs and employs efficient sampling to achieve cost-effective, practical, and instructive accuracy evaluations.

Keywords:
Accuracy assessmentKnowledge graph quality assessmentUser requirement

More Related Videos

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.1K
Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
12:55

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

8.4K

Related Experiment Videos

Last Updated: Jun 27, 2025

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

1.6K
Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.1K
Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
12:55

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

8.4K

Area of Science:

  • Computer Science
  • Data Science
  • Artificial Intelligence

Background:

  • Knowledge graphs are crucial for AI applications but often contain inaccuracies, reducing their utility.
  • Assessing knowledge graph accuracy is vital, but current methods lack user-centricity and "Fitness for Use" principles.
  • Evaluating large-scale knowledge graphs is labor-intensive and costly.

Purpose of the Study:

  • To develop a novel, cost-effective knowledge graph accuracy assessment method.
  • To align accuracy assessment with specific user requirements and "Fitness for Use".
  • To minimize labor costs while ensuring accurate and practical assessment results.

Main Methods:

  • Proposed a novel accuracy assessment method for knowledge graphs.
  • Designed an effective sampling strategy to focus on user requirements.
  • Implemented the method and evaluated its performance against real accuracy rates.

Main Results:

  • The proposed method yields accuracy rates closely matching real accuracy.
  • The method significantly minimizes the required sample size.
  • Achieved cost-saving and practical assessment results aligned with user needs.

Conclusions:

  • The novel method provides accurate and instructive knowledge graph quality assessment.
  • It effectively addresses the limitations of existing methods by incorporating user needs.
  • This approach offers a practical and efficient solution for assessing large-scale knowledge graphs.