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Quality Assurance

Quality Assurance

Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...

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Quality Control

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Related Experiment Video

Updated: May 22, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Towards valid and reusable reference alignments - ten basic quality checks for ontology alignments and their

Elena Beisswanger¹, Udo Hahn

¹Jena University Language and Information Engineering (JULIE) Lab, Friedrich-Schiller-Universität Jena, Jena, Germany. elena.beisswanger@uni-jena.de.

Journal of Biomedical Semantics

|May 1, 2012

Summary

This study introduces quality checks for ontology alignments, ensuring their validity and reusability. These checks are crucial for evaluating automated alignment systems and improving data integration across scientific domains.

Related Experiment Videos

Last Updated: May 22, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

Bioinformatics
Data Science
Ontology Engineering

Background:

Ontology alignment identifies relationships between entities in different ontologies.
Manual or automated systems create alignments, with automated systems using diverse relation detection methods.
Reference alignments, typically manually created, are used to evaluate these systems.

Purpose of the Study:

To derive requirements for valid and reusable reference alignments based on practical experience.
To develop and apply simple quality checks to assess the validity and reusability of reference alignments.
To evaluate standard and recent cross-domain reference alignments using the proposed quality checks.

Main Methods:

Analysis of existing reference alignments to identify common issues and derive quality requirements.
Development of a set of quality checks based on the derived requirements.
Application of these quality checks to a biomedical reference alignment (OAEI Anatomy track) and two multi-domain datasets.

Main Results:

The proposed quality checks provide a systematic approach to assess reference alignment quality.
The evaluation revealed potential issues in validity and reusability in the tested reference alignments.
The checks are applicable across different domains, including anatomy and biology.

Conclusions:

Implementing these quality checks enhances the reliability of reference alignments used for evaluating ontology alignment systems.
Ensuring alignment validity and reusability is critical for accurate system comparison and effective data integration.
The developed quality checks contribute to the standardization and improvement of ontology alignment evaluation methodologies.