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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Knowledge graph confidence-aware embedding for recommendation.

Chen Huang1, Fei Yu1, Zhiguo Wan1

  • 1Zhejiang Lab, Hangzhou, 311121, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 25, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel knowledge graph (KG) embedding technique that enhances recommendation systems by considering relation features and entity confidence. The method improves recommendation accuracy by addressing complex feature aggregation in KGs.

Keywords:
Confidence-aware embeddingKnowledge graph embeddingRecommendation systems

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • Knowledge graphs (KGs) are crucial for knowledge extraction and storage.
  • Existing KG recommendation methods often neglect relation features and complex aggregation processes.
  • Imbalanced feature aggregation in KGs poses a challenge for recommendation accuracy.

Purpose of the Study:

  • To propose a recommendation-oriented KG confidence-aware embedding technique.
  • To address the limitations of current KG embedding methods in recommendation systems.
  • To improve the precision of embeddings during information propagation and aggregation.

Main Methods:

  • Developed a confidence-aware embedding technique for recommendation-oriented KGs.
  • Introduced an information aggregation graph and a confidence feature aggregation mechanism.
  • Quantified entity confidence at both feature and category levels.

Main Results:

  • Achieved significant improvements over state-of-the-art KG embedding-based recommendation methods.
  • Demonstrated up to 6.20% increase in AUC and 8.46% increase in GAUC.
  • Validated the approach on four public KG datasets.

Conclusions:

  • The proposed KG confidence-aware embedding technique effectively enhances recommendation systems.
  • Addressing complex and imbalanced feature aggregation is key to improving KG-based recommendations.
  • Quantifying entity confidence leads to more precise embeddings and better recommendation performance.