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#184 Ontologies Don't Have to Be Scary: An Ontology Primer - Interview w/ Neda Abolhassani, PhD
Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/
Please Rate and Review us on your podcast app of choice!
If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here
Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.
Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.
Neda's LinkedIn: https://www.linkedin.com/in/neda-abolhassani-ph-d-61354329/
OSDU Ontology: https://github.com/Accenture/OSDU-Ontology
In this episode, Scott interviewed Neda Abolhassani PhD, R&D Manager at Accenture Labs. To be clear, she was only representing her own views in this episode.
There's some very specific language about ontology in this episode but I think it's quite approachable for most people as a good understanding of ontology, the difference with taxonomies, and some specific insight into developing and applying an ontology.
Some key takeaways/thoughts from Neda's point of view:
- When starting developing an ontology, it's best to start from the business questions you want to answer. It is okay to choose bottom up or top down, but the business applicability is the main point.
- You can convince people ontologies and knowledge graphs aren't scary or that hard to learn and leverage with a small demo of what they do and how to use them.
- Look for open ontologies that have already been created around your domain or area you are trying to model. They can usually be easily augmented and extended but there's no reason to reinvent the wheel.
- Data people need to learn enough about the domain to build the right ontologies and data models but data people learning domain knowledge can "discombobulate" them :) Get the data people with the subject matter experts to learn what's necessary.
- Try to keep your ontology as generic as possible but still encapsulate what you need; that way it is much easier to apply the ontology to other...
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Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/
Please Rate and Review us on your podcast app of choice!
If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here
Episode list and links to all available episode transcripts here.
Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.
Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.
Neda's LinkedIn: https://www.linkedin.com/in/neda-abolhassani-ph-d-61354329/
OSDU Ontology: https://github.com/Accenture/OSDU-Ontology
In this episode, Scott interviewed Neda Abolhassani PhD, R&D Manager at Accenture Labs. To be clear, she was only representing her own views in this episode.
There's some very specific language about ontology in this episode but I think it's quite approachable for most people as a good understanding of ontology, the difference with taxonomies, and some specific insight into developing and applying an ontology.
Some key takeaways/thoughts from Neda's point of view:
- When starting developing an ontology, it's best to start from the business questions you want to answer. It is okay to choose bottom up or top down, but the business applicability is the main point.
- You can convince people ontologies and knowledge graphs aren't scary or that hard to learn and leverage with a small demo of what they do and how to use them.
- Look for open ontologies that have already been created around your domain or area you are trying to model. They can usually be easily augmented and extended but there's no reason to reinvent the wheel.
- Data people need to learn enough about the domain to build the right ontologies and data models but data people learning domain knowledge can "discombobulate" them :) Get the data people with the subject matter experts to learn what's necessary.
- Try to keep your ontology as generic as possible but still encapsulate what you need; that way it is much easier to apply the ontology to other...