7 ms·

What would one use an ontology editor for?

riku_iki 3y ago
for editing ontology..
wbazant 3y ago
Improving an ontology over time! A use case I've seen is to maintain a consistent tree-like display across different pages of a science website, microbiomedb.org. You'd load up an ontology as an .xml, use the UI to browse the nodes and a hierarchy you already have, decide how to handle the new thing you want to achive and add a new leaf somewhere or rejiggle the tree somehow, and at the end you save the changed .xml.
the-alchemist 3y ago
There's a lot of Protege tutorials out there, but here's what an ontology (in the OWL sense of the word, let's you do). The "pizza ontology" is the Hello World of the ontology world. * imagine a graph of things you wanna organize ("model"), say world religions, or the plant and animal kingdom * you can tell the system that anything that's a plant can never be an animal. or viruses can't be bacteria * or lions and zebras are both mammals * you can define what mammals are, vertebra, heart, brain, etc. The interesting part is ontology _validation_ or querying. Is it internally consistent? Maybe you specified viruses and bacteria and said they are never the same thing, but the way you modeled it, they are identical! Hmm, you'll have to update your definition of bacteria, or viruses, or both. Next, you try to put fungi in the system, but there's an error because fungi do not belong to the plant or animal kingdom: they are their own thing. So this is a fairly simplistic use case, but scale this up to hundreds if not thousands of entities and you can start to see the value. Imagine sticking the human genome in there, and which drugs act on which chromosomes, etc. It's a niche, for sure, if you need something like reasoning it's the way to go.
- agubelu 3y ago
  The main issue with ontologies, and the reason why they're not popular besides a few niche cases, is that they try to solve a fundamentally unsolvable problem: getting (a large amount of) humans to agree on a "correct" modelling of something non-trivial. When you narrow down the domain to something where a consensus on representation can be reached, then sure, reasoning is a plausible use case... except for the fact that it scales very poorly, and making it work on a set of data large enough to be interesting requires a disproportionate amount of computing power.
  - karencarits 3y ago
    But wikidata is trying
    - xtagon 3y ago
      Those who say it can't be done should not interrupt the people doing it, I suppose!
  - p_l 3y ago
    Ontologies are behind some of the systems that help mapping between different models used by different groups for things in the same space (for example, mapping between different ways of interpreting and communicating medical data through HL7 messages). An Ontology doesn't mean it has to decide on single correct model - in fact, I'd say such ontology is particularly poor and a technology that limits to that is too limited to be used in ontology field.
  - hobofan 3y ago
    Yes, consensus in ontology building has traditionally been a huge drag for the adoption of ontologies. While it's not necessarily required, having consensus about ontology can obviously increase their utility. At the same time I think it's important to have explicit dissent (differing world views) and give both a room to grow, rather than trying to create the "one true" view on the world. However, I don't think the core issue is consensus itself, but instead that the prevalent form of consensus in the ontology authoring space is consensus by committee rather than consensus by usage (as is usual in the open source software space). That's why I've in the past been involved in creating Plow[0], a package manager for ontologies, with the aim of bringing the same "grassroots" nature and network effects that you find in other open source ecosystem to ontology engineering. [0]: https://plow.pm/ https://plow.pm/
- zozbot234 3y ago
  It's generally more interesting to apply validation to data than ontologies themselves. OWL makes this harder, because it rejects two assumptions that are commonly used in real-world modeling: (1) Unique Name Assumption; every object in the domain is described by a single entry in your data model. By contrast, OWL will always try to conflate different entries in order to solve logical consistency issues that arise from your model; (2) Closed-World assumption on relations. OWL rejects this and assumes that your data about the relations or properties in any given model is always incomplete. Its reaction to issues that crop up with your modeling is to enforce logical consistency by adding "inferred" property instances to your data, as opposed to simply flagging the issue for validation. Real-world technologies like SHACL and ShEx work on very similar logical principles, such as description logic https://news.ycombinator.com/item?id=31890041 https://news.ycombinator.com/item?id=31890041 but avoid these pitfalls.
jstarfish 3y ago
If you played the game Scribblenauts, its "database that contains literally every object imaginable" is (as best I can tell) probably a bespoke ontology. Everything you try to stump it with references a factory of classes you can spawn instances of. The properties of a near-infinite number of classes need to be edited and managed somehow. Doing it in an IDE where every class is discrete is a pain in the ass. It's easier in a database- or filesystem-like interface. Hence, ontology editors.
galaxyLogic 3y ago
When you code an application that deals with people and products and services and payments (like most applications do) your code must implicitly assume some kind of model and interrelationships of those concepts. Ontology and ontology-editors allow you to make such assumptions about concept-relationships explicit. You can then review and correct those assumptions now that they are explicitly written down. If your company has multiple applications it helps that they all use the same ontology, or if they don't to know where they differ. How well that works in practice I don't know but I think I get the idea. Of course ontologies are not much in the zeitgeist now that AI will solve all the problems. But surely, AI could create great ontologies with ease. Can we ask it?
- cyanydeez 3y ago
  id assume for AIs to both be safe full and useful, they need to start with an ontology. of course, once that's boostraped, then potentially they could make derivative and novel ontologies.

for editing ontology..

Improving an ontology over time! A use case I've seen is to maintain a consistent tree-like display across different pages of a science website, microbiomedb.org. You'd load up an ontology as an .xml, use the UI to browse the nodes and a hierarchy you already have, decide how to handle the new thing you want to achive and add a new leaf somewhere or rejiggle the tree somehow, and at the end you save the changed .xml.

the-alchemist 3y ago

There's a lot of Protege tutorials out there, but here's what an ontology (in the OWL sense of the word, let's you do). The "pizza ontology" is the Hello World of the ontology world. * imagine a graph of things you wanna organize ("model"), say world religions, or the plant and animal kingdom * you can tell the system that anything that's a plant can never be an animal. or viruses can't be bacteria * or lions and zebras are both mammals * you can define what mammals are, vertebra, heart, brain, etc. The interesting part is ontology _validation_ or querying. Is it internally consistent? Maybe you specified viruses and bacteria and said they are never the same thing, but the way you modeled it, they are identical! Hmm, you'll have to update your definition of bacteria, or viruses, or both. Next, you try to put fungi in the system, but there's an error because fungi do not belong to the plant or animal kingdom: they are their own thing. So this is a fairly simplistic use case, but scale this up to hundreds if not thousands of entities and you can start to see the value. Imagine sticking the human genome in there, and which drugs act on which chromosomes, etc. It's a niche, for sure, if you need something like reasoning it's the way to go.

agubelu 3y ago
The main issue with ontologies, and the reason why they're not popular besides a few niche cases, is that they try to solve a fundamentally unsolvable problem: getting (a large amount of) humans to agree on a "correct" modelling of something non-trivial. When you narrow down the domain to something where a consensus on representation can be reached, then sure, reasoning is a plausible use case... except for the fact that it scales very poorly, and making it work on a set of data large enough to be interesting requires a disproportionate amount of computing power.
- karencarits 3y ago
  But wikidata is trying
  - xtagon 3y ago
    Those who say it can't be done should not interrupt the people doing it, I suppose!
- p_l 3y ago
  Ontologies are behind some of the systems that help mapping between different models used by different groups for things in the same space (for example, mapping between different ways of interpreting and communicating medical data through HL7 messages). An Ontology doesn't mean it has to decide on single correct model - in fact, I'd say such ontology is particularly poor and a technology that limits to that is too limited to be used in ontology field.
- hobofan 3y ago
  Yes, consensus in ontology building has traditionally been a huge drag for the adoption of ontologies. While it's not necessarily required, having consensus about ontology can obviously increase their utility. At the same time I think it's important to have explicit dissent (differing world views) and give both a room to grow, rather than trying to create the "one true" view on the world. However, I don't think the core issue is consensus itself, but instead that the prevalent form of consensus in the ontology authoring space is consensus by committee rather than consensus by usage (as is usual in the open source software space). That's why I've in the past been involved in creating Plow[0], a package manager for ontologies, with the aim of bringing the same "grassroots" nature and network effects that you find in other open source ecosystem to ontology engineering. [0]: https://plow.pm/ https://plow.pm/
zozbot234 3y ago
It's generally more interesting to apply validation to data than ontologies themselves. OWL makes this harder, because it rejects two assumptions that are commonly used in real-world modeling: (1) Unique Name Assumption; every object in the domain is described by a single entry in your data model. By contrast, OWL will always try to conflate different entries in order to solve logical consistency issues that arise from your model; (2) Closed-World assumption on relations. OWL rejects this and assumes that your data about the relations or properties in any given model is always incomplete. Its reaction to issues that crop up with your modeling is to enforce logical consistency by adding "inferred" property instances to your data, as opposed to simply flagging the issue for validation. Real-world technologies like SHACL and ShEx work on very similar logical principles, such as description logic https://news.ycombinator.com/item?id=31890041 https://news.ycombinator.com/item?id=31890041 but avoid these pitfalls.

jstarfish 3y ago

If you played the game Scribblenauts, its "database that contains literally every object imaginable" is (as best I can tell) probably a bespoke ontology. Everything you try to stump it with references a factory of classes you can spawn instances of. The properties of a near-infinite number of classes need to be edited and managed somehow. Doing it in an IDE where every class is discrete is a pain in the ass. It's easier in a database- or filesystem-like interface. Hence, ontology editors.

galaxyLogic 3y ago

When you code an application that deals with people and products and services and payments (like most applications do) your code must implicitly assume some kind of model and interrelationships of those concepts. Ontology and ontology-editors allow you to make such assumptions about concept-relationships explicit. You can then review and correct those assumptions now that they are explicitly written down. If your company has multiple applications it helps that they all use the same ontology, or if they don't to know where they differ. How well that works in practice I don't know but I think I get the idea. Of course ontologies are not much in the zeitgeist now that AI will solve all the problems. But surely, AI could create great ontologies with ease. Can we ask it?

cyanydeez 3y ago
id assume for AIs to both be safe full and useful, they need to start with an ontology. of course, once that's boostraped, then potentially they could make derivative and novel ontologies.