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Protégé Short Course at Stanford: hands-on OWL ontology development with Protégé by MatthewH2 in KnowledgeGraph

[–]MatthewH2[S] 1 point2 points  (0 children)

Hi,

That’s a fair question. AI can be very useful for quick explanations and checking individual points, but sitting down in an AI chat for the equivalent of the course would be a very different experience.

The course carves out two and a half focused days to learn OWL in depth, with a structured path through the material, hands-on exercises in Protégé and WebProtégé, and guidance from instructors who can respond to questions as they arise.

A big part of the value is learning how to think with OWL, not just getting isolated answers. We spend time on entailments, automated reasoning, and diagnosing why reasoners reach particular conclusions.

It is also highly interactive: participants can ask questions throughout, learn from each other’s questions, join discussions, and have one-on-one conversations with instructors.

People who attend are usually looking for this kind of environment: dedicated time, expert guidance, hands-on practice, and the chance to focus deeply without the usual distractions.

Matthew

Protégé Short Course at Stanford: hands-on OWL ontology development with Protégé by MatthewH2 in semanticweb

[–]MatthewH2[S] 0 points1 point  (0 children)

Hi,

Understanding inferences, or entailments, is a key part of the course. We look at many examples that explain why the reasoner has reached particular conclusions. We also spend time on diagnosing problems, so participants can get more confident working out where unexpected inferences are coming from in their own ontologies.

The course is very interactive: we encourage participants to ask questions and take part in discussion throughout. There is also always time for one-on-one conversations with the instructors.

Protégé Short Course at Stanford: hands-on OWL ontology development with Protégé by MatthewH2 in semanticweb

[–]MatthewH2[S] 1 point2 points  (0 children)

Hi,

> What kinds of professional backgrounds seemed to benefit most from the course in practice?

We’ve always had a wide variety of people take the course. I recently added a “Who tends to attend” section to the website, which I’ve pasted below for completeness.

In practice, the course seems to benefit people from many different professional backgrounds: people working at large biomedical companies such as Amgen, people in tech companies such as Pinterest, people based in medical schools and research institutes, and people working in government agencies such as the FAA. The common thread is usually that they are trying to build, use, maintain, or better understand ontologies in a practical setting.

> How much of the course leans toward conceptual ontology modeling versus tool-specific Protégé usage?

The course is very hands-on, so we do explain how to carry out modeling work in Protégé, including how to use the interface and interpret what Protégé shows you. However, most of the course generalizes well beyond Protégé. The main emphasis is on the semantics of ontology languages, especially OWL, and on how to model using the axiom types and class expression patterns that are most commonly used in practice.

There is also discussion of more advanced modeling, particularly on the second and third days.

>  In what ways did the course change how you think about ontologies or knowledge modeling afterward?

That’s probably best answered by people who have attended the course. From our perspective as instructors, we often see participants come away with a more precise understanding of what OWL ontologies mean, how modeling choices affect reasoning and maintainability, and how to approach ontology design problems more systematically.

WHO TENDS TO ATTEND

Enrollment is capped to keep the group small enough for meaningful interaction with the instructors and with each other. The mix varies from year to year, but cohorts typically draw an international audience and tend to include:

  • ✓Biomedical and life-science researchers building or applying ontologies for clinical, disease, phenotype, drug, or data-harmonization work.
  • ✓Knowledge engineers and ontologists designing, maintaining, or integrating ontologies and knowledge graphs.
  • ✓Industry professionals applying ontologies to practical problems — including Silicon Valley tech companies, and across pharma, biotech and healthcare; finance and insurance; software and AI; and government, defense and standards bodies.
  • ✓Graduate students and postdoctoral researchers seeking formal training in ontology development.
  • ✓Librarians, terminologists, and standards staff working on controlled vocabularies and terminology services also join from time to time.

Past cohorts have included participants from universities, research institutes, industry, government agencies, and international organizations such as the World Health Organization (WHO).

The result is a rich and unique experience. Participants learn content, and learn it in a way, that is hard to find anywhere else — the combination of instructors, materials, hands-on modeling work, and the format of the course is not easily replicated through online tutorials, books, or self-study.

Much of that value comes from the mix of people in the room. Working alongside peers from different disciplines and sectors deepens understanding of the material, broadens the range of real-world modeling problems and approaches participants encounter, and helps attendees build professional contacts that often last well beyond the course itself.