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Related Concept Videos

Fault Types01:18

Fault Types

When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...

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Related Experiment Videos

Interactive ontology debugging: Two query strategies for efficient fault localization.

Kostyantyn Shchekotykhin1, Gerhard Friedrich, Philipp Fleiss

  • 1Alpen-Adria Universität, Universitätsstrasse 65-67, 9020 Klagenfurt, Austria.

Web Semantics (Online)
|April 2, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an entropy-based strategy for debugging ontologies, significantly reducing the number of queries needed to identify faulty terms. This method proves superior to simpler strategies, even without prior knowledge of common user errors.

Keywords:
Description logicModel-based diagnosisOntology debuggingQuery selection

Related Experiment Videos

Area of Science:

  • Ontology engineering
  • Knowledge representation
  • Artificial intelligence

Background:

  • Ontologies are crucial for knowledge bases, especially on the Semantic Web.
  • User-generated content can lead to faulty ontologies due to differing domain views and complexities in logical descriptions.
  • Current debugging methods often overwhelm users with numerous potential diagnoses.

Purpose of the Study:

  • To develop an efficient method for identifying the specific cause of ontology faults.
  • To reduce the burden on users for fault localization in ontology debugging.
  • To propose and evaluate query selection strategies for ontology debugging.

Main Methods:

  • Proposed two query selection strategies: "split-in-half" and entropy-based.
  • Utilized an oracle to perform observations by querying entailments of the target ontology.
  • Evaluated strategies based on the number of queries required for fault localization.

Main Results:

  • The entropy-based strategy significantly reduced the number of queries compared to the "split-in-half" approach.
  • The superiority of the entropy-based method was demonstrated across various user error probability distributions.
  • This method is effective even when no prior information about typical user errors is available.

Conclusions:

  • Entropy-based query selection is a highly effective strategy for ontology debugging.
  • This approach minimizes the number of queries needed, making ontology maintenance more efficient.
  • The proposed method enhances the usability and reliability of ontologies in real-world applications.