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Related Concept Videos

Natural and Artificial Concepts01:24

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint Vincent in...
Schemata01:17

Schemata

A schema is a mental construct that organizes related concepts, allowing the brain to process information efficiently. Upon activation, schemata facilitate assumptions about people or objects.
Two types of schemata are:
Impact of Schemas01:30

Impact of Schemas

Schemas are cognitive structures that provide a framework for interpreting and organizing social information. They help individuals navigate complex environments by offering expectations about people, events, and behaviors. Schemas influence attention, encoding, and retrieval processes, thereby shaping the entire trajectory of information processing in social contexts.Attention and Cognitive LoadDuring initial attention, schemas function as filters that prioritize schema-consistent information,...
Methods of Documentation I: Source-Oriented Records01:18

Methods of Documentation I: Source-Oriented Records

Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
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Synteny and Evolution02:31

Synteny and Evolution

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Schemas01:42

Schemas

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Related Experiment Video

Updated: May 25, 2026

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

CODEX: exploration of semantic changes between ontology versions.

Michael Hartung1, Anika Gross, Erhard Rahm

  • 1Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany. hartung@izbi.uni-leipzig.de

Bioinformatics (Oxford, England)
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

Life science ontologies evolve, necessitating tracking changes between versions. CODEX (Complex Ontology Diff Explorer) offers a tool to identify and analyze these semantic differences effectively.

Related Experiment Videos

Last Updated: May 25, 2026

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

Area of Science:

  • Bioinformatics
  • Ontology Engineering
  • Life Sciences

Background:

  • Life science ontologies are dynamic and frequently updated to incorporate new knowledge and user needs.
  • Tracking modifications between ontology versions is crucial for maintaining data consistency and understanding scientific advancements.

Purpose of the Study:

  • To present CODEX (Complex Ontology Diff Explorer), a novel tool for identifying and analyzing semantic changes between two ontology versions.
  • To provide users with an interactive platform for understanding ontology evolution.

Main Methods:

  • Developing a computational approach to detect differences between successive ontology versions.
  • Implementing an interactive exploration interface for visualizing and analyzing identified semantic changes.

Main Results:

  • CODEX successfully determines semantic modifications between ontology versions.
  • The tool enables users to interactively explore these differences through multiple analytical views.

Conclusions:

  • CODEX facilitates a deeper understanding of ontology evolution in the life sciences.
  • The tool enhances the usability and interpretability of ontology version comparisons.