Adaptive Ontology Mapping
Adaptive Ontology Mapping - Related Videos
09:38
Adapting 3' Rapid Amplification of CDNA Ends to Map Transcripts in Cancer
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Cited by 3 •
2018
The two different 3' rapid amplification of cDNA ends (3' RACE) protocols described here make use of two different DNA polymerases to map sequences that include a segment of the open reading frame (ORF), the stop codon, and the entire 3' UTR of a transcript using RNA obtained from different cancer cell lines.
09:39
Mapping Dysfunctional Protein-Protein Interactions in Disease
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2025
Here, we present a protocol to enable the capture and identification of disease-specific protein-protein interactions from native cells and tissues using chemical probes and mass spectrometry. The resulting interaction datasets are analyzed through a dedicated web-based platform to reveal dynamic network dysfunctions and pathway alterations linked to disease.
06:58
Fate Mapping
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2023
Fate mapping is a technique used to understand how embryonic cells divide, differentiate, and migrate during development. In classic fate mapping experiments, cells in different areas of an embryo are labeled with a chemical dye and then tracked to determine which tissues or structures they form. Technological improvements now allow for individual cells to be marked and traced throughout embryonic development and adulthood. This video reviews the concepts behind fate mapping, and then details a...
08:30
X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
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Cited by 2 •
2011
We are developing a dynamic adaptive exposure technique using our scanning beam digital X-ray system. Rather than exposing an object uniformly, the exposure is adapted depending on the opacity of the object. Here we show an experiment on an anthropomorphic phantom that resulted in a dose saving of 30%.
11:03
Motor Maps
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California One principle of brain organization is the topographic mapping of information. Especially in sensory and motor cortices, adjacent regions of the brain tend to represent information from adjacent parts of the body, resulting in maps of the body expressed on the surface of the brain. The primary sensory and motor maps in the brain surround a prominent sulcus known as the central sulcus. The cortex...
FAQs
Correspondences are identified by comparing labels, definitions, relationships, and contextual patterns across source and target ontologies. A match can therefore reflect not only similar wording but also compatible meaning and linked concepts. This multi-feature comparison helps connect terms that differ syntactically while preserving their intended relationships during information integration.
Adaptation allows mappings to be updated when terminology changes, structures are reorganized, domain requirements shift, or new concepts appear. Existing correspondences can be reconsidered rather than treated as permanent. This is important for evolving engineering environments, where concepts and relationships may change across design, manufacturing, simulation, maintenance, and product lifecycle systems.
Rule-based reasoning can apply explicit correspondence conditions, while similarity measures evaluate how closely concepts or their descriptions align. Machine learning can support pattern-based identification of relationships. These approaches provide different ways to interpret ontology content, and their use allows mapping procedures to accommodate both stated rules and recurring patterns in the available data.
Similar labels do not necessarily indicate equivalent concepts. Contextual patterns, definitions, and relationships provide additional evidence about how a concept functions within its ontology. Considering these signals reduces semantic inconsistencies caused by relying on wording alone and supports more reliable information exchange when different engineering systems use overlapping or differently organized terminology.
A typical workflow compares the relevant source and target ontologies, examines labels, definitions, relationships, and context, and identifies candidate correspondences. Rules, similarity measures, or machine learning can then support the matching process. The resulting mappings are updated as concepts emerge, terminology changes, or ontology structures and domain requirements evolve.
The approach can connect information across computer-aided design, manufacturing, simulation, maintenance, and product lifecycle systems. These environments often represent related engineering concepts in different ways. Aligning their ontologies supports interoperability and more consistent data exchange, allowing information created in one part of an engineering workflow to remain useful in another.
By reducing semantic inconsistencies, the mappings support automated data integration and more reliable information exchange between systems. They can also provide a common basis for decision-making across interconnected workflows. In engineering, this is relevant when information must remain aligned as products, processes, terminology, and supporting lifecycle systems evolve.