Speaker 1: Welcome to our deep dive into ontologies using Python.
Speaker 2: Ontologies.
Speaker 1: Yeah, ontologies. You know, it's a way to describe knowledge
Speaker 1: in a way that machines can actually work with it.
Speaker 1: So we're using this book called ontologies with Python as
Speaker 1: our main source. It focuses on this Python module called
Speaker 1: owlready two hmm. And I think you'll be pretty surprised
Speaker 1: by what you can actually do with ontologies, like what
Speaker 1: you can build websites that like understand complex data wow,
Speaker 1: or even power AI that can like reason like a human. Hmm.
Speaker 2: That's that's pretty impressive, it is, right, So what is
Speaker 2: an ontology?
Speaker 1: Yeah? What is an ontology?
Speaker 2: Like, how is it different from just regular programming?
Speaker 1: Well, it's it's not just data, okay, like you would
Speaker 1: find in this breadsheet or a database. It's it's about
Speaker 1: meaning meaning okay, and relationships just between what between things? Ok?
Speaker 1: It's about like, can give me like a simple analogy?
Speaker 2: Sure, imagine a family tree. It's not just a list
Speaker 2: of names, right right, It shows how those people are related.
Speaker 2: So you have different categories like parents and children, which
Speaker 2: we call classes in ontology terms.
Speaker 1: Gotcha.
Speaker 2: And then you have characteristics like age or hair color,
Speaker 2: and we call those properties, and then each person in
Speaker 2: the tree is an individual.
Speaker 1: Okay, So an ontology lets us represent this kind of information, yes,
Speaker 1: in a way that a computer can understand and reason
Speaker 1: with and reason with. So instead of just seeing data points, yeah,
Speaker 1: the computer can actually understand that grandparent exactly is a
Speaker 1: meaningful category based on the relationships. That's pretty neat, Yeah
Speaker 1: it is.
Speaker 2: And this ability to capture meaning is really what makes
Speaker 2: ontologies powerful.
Speaker 1: Okay, so how does this owl ready to come into play?
Speaker 2: So a already too is like our toolkit for working
Speaker 2: with ontologies and Python, and it's surprisingly easy to use, really,
Speaker 2: even if you're not a Python xper.
Speaker 1: Ok.
Speaker 2: Lets us load existing ontologies, modify them, and even create
Speaker 2: entirely new ones from scratch.
Speaker 1: So it's our way to actually put these ontologies into
Speaker 1: action exact. Can you give me specific example?
Speaker 2: Yeah? Sure, The book uses this example of a bacteria ontology.
Speaker 1: Oka.
Speaker 2: It might sound simple, but it's a good illustration of
Speaker 2: how even a basic ontology can be powerful. So this
Speaker 2: ontology has classes like bacteria shape and grouping, and then
Speaker 2: it also has properties like gram positive to describe those bacteria,
Speaker 2: and then each specific type of bacteria is represented as
Speaker 2: an individual.
Speaker 1: So it's like we're creating like a digital fingerprint for
Speaker 1: each bacteria based on its characteristics exactly. Okay, And so
Speaker 1: this setup allows us to do some pretty cool things.
Speaker 2: Yeah, Like we could use this ontology to identify an
Speaker 2: unknown bacteria oh wow, based on its characteristics.
Speaker 1: So it's like we're giving a computer the ability to
Speaker 1: play bacterial detective.
Speaker 2: That's a great way to put it.
Speaker 1: Yeah, if we tell it the bacteria is Gram positive
Speaker 1: and Rod shaped, it can like use the ontology to
Speaker 1: narrow down the possibility exactly. That's pretty practical, it is, But.
Speaker 2: There's even more to it.
Speaker 1: Okay.
Speaker 2: One of the really amazing things is this concept of reasoners.
Speaker 1: Reasoners.
Speaker 2: Yeah, they're like superpowered logic engines.
Speaker 1: Okay, how do they work?
Speaker 2: So imagine you have a detective who's really good at
Speaker 2: connecting the dots. Okay, that's what a reasoner does for
Speaker 2: our ontology. It can automatically classify individuals, uncover hidden relationships
Speaker 2: that weren't explicitly stated, wow, and even flag inconsistencies in
Speaker 2: our knowledge base.
Speaker 1: So it's like the reasoner takes everything we've defined exactly
Speaker 1: and starts making logical deductions and revealing insights that we
Speaker 1: might not even considered precisely. That's powerful.
Speaker 2: It's what truly allows ontologies to go beyond simple data storage, okay,
Speaker 2: and become tools for knowledge representation.
Speaker 1: So are these reasoners built into OWL ready too?
Speaker 2: That's the beauty of it. It has built in support
Speaker 2: for popular reasoners like Hermity and Pellet, And.
Speaker 1: To be clear, those are separate programs.
Speaker 2: Yes, both Hermity and Pellet are Java programs, gotcha, So
Speaker 2: you'll need to have Java installed on your system to
Speaker 2: use them. But don't worry. Already two makes the setup
Speaker 2: pretty straightforward.
Speaker 1: Okay. So once we have those set up, how do
Speaker 1: we use them?
Speaker 2: It's surprisingly simple, okay. Already two has this function called
Speaker 2: sink reasoner. You just run that and it's like hitting
Speaker 2: the go button on our logic engine.
Speaker 1: And by default, Alurity two uses the Hermite reasoner that's right, okay.
Speaker 2: But if you want to use Pellet there are specific
Speaker 2: functions for that as well, so it gives.
Speaker 1: You the flexibility.
Speaker 2: Yeah.
Speaker 1: So, no matter which one we choose, Already two provides
Speaker 1: a simple way to put them to work exactly. This
Speaker 1: is all starting to come.
Speaker 2: Together, it is, isn't it? Yeah, and as we dived,
Speaker 2: you'll see just how powerful this combination of ontologies already
Speaker 2: to and reasoners can be.
Speaker 1: Now, you mentioned something earlier about an open world assumption.
Speaker 1: Oh yeah, can you explain what that means?
Speaker 2: Sure? The open world assumption basically means that we acknowledge
Speaker 2: there might be information okay, that we don't know.
Speaker 1: So it's like saying, hey, we might be missing some
Speaker 1: pieces of a puzzle.
Speaker 2: It's like admitting that our knowledge is never truly complete,
Speaker 2: which contrasts with the closed world assumption, okay, where we
Speaker 2: assume that we know everything.
Speaker 1: God is to know. So this distinction is important, yes,
Speaker 1: very because it affects how the reasoners work exactly. Okay,
Speaker 1: So how does the open world assumption change the way
Speaker 1: they operate?
Speaker 2: Well? Under the open world assumption, a reasoner can't just
Speaker 2: assume something is false because it's not explicitly stated in
Speaker 2: the ontology. It has to consider the possibility that the
Speaker 2: information is simply missing.
Speaker 1: So it's a bit more cautious. Yes, it's knowing that
Speaker 1: there might be unknown factors at play. Exactly, That makes sense.
Speaker 1: So how does this work in practice?
Speaker 2: So already two has a function called close world that
Speaker 2: lets you temporarily close the world, either for specific parts
Speaker 2: of the ontology or for the whole thing.
Speaker 1: So we can basically tell the reasoner to focus on
Speaker 1: a particular area and treat it as if we have
Speaker 1: all the information. But why would we want to do that?
Speaker 2: Well, sometimes closing the world can be beneficial, Okay. It
Speaker 2: lets us make more specific deductions about a particular area
Speaker 2: of knowledge.
Speaker 1: So it's like saying, for this specific task, yeah, let's
Speaker 1: assume we have all the pieces of exactly puzzle.
Speaker 2: It can be helpful in situations where we need to
Speaker 2: make decisions Okay, based on a limited set of facts. Gotcha,
Speaker 2: But it's a trade off between precision and the ability
Speaker 2: to handle potential unknowns.
Speaker 1: Okay, that makes sense.
Speaker 2: It's like choosing the right tool for the job exactly.
Speaker 2: Now that we've laid the groundwork with reasoners and the
Speaker 2: open world assumption, yeah, let's dive into some of the
Speaker 2: things that we can actually do. Okay with these reasoners
Speaker 2: in OWL, ready to what.
Speaker 1: Kind of tasks can they help us with.
Speaker 2: One of the most common and useful applications is consistency checking.
Speaker 1: Okay, consistency check.
Speaker 2: It's like having a built in quality control system.
Speaker 1: Okay, but what exactly does consistency mean?
Speaker 2: So a consistent ontology is one where there are no
Speaker 2: logical contradictions. Okay, think of it this way. If you
Speaker 2: told me that squares are a type of circle, that
Speaker 2: would be a contradiction.
Speaker 1: Right.
Speaker 2: Reasoners can help us find these kind of errors in
Speaker 2: our ontologies before they cause problems.
Speaker 1: Ah. So it's like making sure all the pieces of
Speaker 1: our knowledge why framework fit together logically. I can see
Speaker 1: how that would be crucial. Yeah, especially if we're building
Speaker 1: complex systems based on this.
Speaker 2: Right, inconsistencies can lead to all sorts of unexpected and
Speaker 2: incorrect results.
Speaker 1: Okay, so we've got consistency checking. Else.
Speaker 2: Another powerful application is automatic classification. Reasoners can automatically categorize
Speaker 2: individuals into the correct classes based on their properties and
Speaker 2: the relationships defined in the ontology.
Speaker 1: So it's like having their reasoner act as an expert librarian.
Speaker 2: Yeah, that's a good analogy.
Speaker 1: Who can perfectly organize all the books based on their
Speaker 1: content and genre.
Speaker 2: Let's go back to our bacteria ontology. If we define
Speaker 2: a new bacteria with certain characteristics, the reason I can
Speaker 2: automatically figure out which class.
Speaker 1: It belongs to, saving us a lot of manual.
Speaker 2: Effort, exactly, And I imagine this becomes even more valuable
Speaker 2: as our ontology grows and becomes more complex.
Speaker 1: Okay, so we can use reasoners for consistency checking, automatic classification.
Speaker 1: What other tricks do they have?
Speaker 2: They can also help us uncover implicit relationships. Implicit relationships
Speaker 2: are explicitly stated in the ontology.
Speaker 1: Can you give me an example.
Speaker 2: Sure. Let's say our ontology describes family relationships. Okay, we
Speaker 2: know that John is the father of Mary and Mary
Speaker 2: is the mother of Peter.
Speaker 1: So we have two direct relationships.
Speaker 2: There, right, But there's an implied relationship that's not explicitly stated.
Speaker 2: John is the grandfather of Peter. Right. A reasoner can
Speaker 2: figure this out based on the existing information about parent
Speaker 2: child relationships.
Speaker 1: I see. So it's like the reasoner is filling in
Speaker 1: the gaps in our knowledge based on logical deductions.
Speaker 2: Exactly.
Speaker 1: That's pretty impressive, it.
Speaker 2: Is, And this ability to uncover implicit relationships can be
Speaker 2: incredibly valuable in many real world scenarios. What imagine using
Speaker 2: this in a medical ontology to identify potential drug interactions, huh,
Speaker 2: or predict disease risks based on complex relationships between gene
Speaker 2: symptoms and treatments.
Speaker 1: Okay, so we're giving the computer the ability to make connections. Yes,
Speaker 1: that might not be obvious to us.
Speaker 2: At first glance, precisely, and this is where the true
Speaker 2: power of ontologies and reasoners really shines. They help us
Speaker 2: move beyond simply storing data to actually understanding the knowledge
Speaker 2: that's encoded in that data.
Speaker 1: This is all fascinating, But before we get too carried away,
Speaker 1: I want to go back to something you mentioned earlier, annotations.
Speaker 1: Can you remind me what those are?
Speaker 2: Sure? So, think of annotations as extra information that we
Speaker 2: can attach to different parts of our ontology. They're like
Speaker 2: sticky notes that provide additional context, explanations, or even links
Speaker 2: to external resources.
Speaker 1: So they're like adding helpful notes to our knowledge map.
Speaker 1: Can you give me some examples.
Speaker 2: Absolutely, we can use them to provide easy to understand
Speaker 2: descriptions for complex terms, add comments or explanations for ourselves
Speaker 2: or other developers, keep track of where we got the
Speaker 2: information for a specific concept, link to relevant websites, research papers,
Speaker 2: or database, or even track different versions of the ontology.
Speaker 1: So it's all about adding more layers of meaning exactly,
Speaker 1: and making our ontology richer and more informative.
Speaker 2: Yeah, and already too, makes working with annotations really straightforward.
Speaker 2: You can add, access, and modify annotations using a simple syntax,
Speaker 2: just like you would with any other property.
Speaker 1: Now, you mentioned something about annotations being helpful for full
Speaker 1: text search. Yeah, can you explain how that works?
Speaker 2: So imagine you're searching for information within a vast library. Okay,
Speaker 2: the more detailed the index and cataloging system, the easier
Speaker 2: it is to find what you're looking for. Right, That's
Speaker 2: what annotations do for ontology.
Speaker 1: So the more annotations we add, the easier becomes to
Speaker 1: search exactly for specific information within our ontology.
Speaker 2: Precisely, search engines can index the text content of our annotations,
Speaker 2: making it much easier to find relevant concepts, even if
Speaker 2: they're buried deep within the ontology.
Speaker 1: So it's like providing extra keywords and clues to help
Speaker 1: the search engine understand and navigate our ontology.
Speaker 2: Yeah, and this becomes especially important as our ontologies grow
Speaker 2: larger and more complex. Annotations help ensure that our knowledge
Speaker 2: remains accessible and discoverable.
Speaker 1: Now, shifting gears a bit, I'm curious about something you
Speaker 1: mentioned earlier. Okay, worlds an owlready two. This sounds almost
Speaker 1: like science fiction.
Speaker 2: It's not quite science fiction, but it's a powerful concept.
Speaker 1: Okay, So what are they think of.
Speaker 2: A world as a self contained environment within owl ready to? Okay,
Speaker 2: it's like having a separate sandbox.
Speaker 1: Okay, I like this out of a sandbox. Yeah. It
Speaker 1: makes me feel like I can explore and experiment without
Speaker 1: breaking anything.
Speaker 2: Yeah.
Speaker 1: So why would we want to use these separate worlds?
Speaker 2: There are several reasons why worlds can be incredibly useful experimentation.
Speaker 2: You can test out different changes or additions to your
Speaker 2: ontology without worrying about messing up the main version and
Speaker 2: scenario analysis. You can create different worlds to model various
Speaker 2: hypothetical situations or explore different perspectives on the same data. Gotcha,
Speaker 2: data separation. If you're working with data from multiple sources
Speaker 2: or domains, you can keep them separate in different worlds
Speaker 2: for easier management.
Speaker 1: So it's like having different versions of our ontology exactly,
Speaker 1: and we can switch between or even merge together if needed.
Speaker 2: Precisely. It's a powerful way to manage complexity and explore
Speaker 2: different possibilities without risk.
Speaker 1: So how do we actually create and work with these worlds?
Speaker 2: Already too makes it quite intuitive.
Speaker 1: Okay.
Speaker 2: You use the world class to create and manage your world.
Speaker 2: It's like having a world building toolkit.
Speaker 1: Okay, So we can create these separate worlds. Yeah, but
Speaker 1: can we also interact with them? That's a great question,
Speaker 1: or even share information between them?
Speaker 2: So, while worlds are designed to be isolated environments okay,
Speaker 2: already too, does provide some mechanisms for communication between them.
Speaker 1: How would we go about transferring information between them?
Speaker 2: One way is to use the import from function. You
Speaker 2: can think of it like copying and pasting elements okay,
Speaker 2: from one world into another.
Speaker 1: So we can reuse parts of our ontology in different
Speaker 1: contexts or scenarios. Yeah, that's pretty handy. Are there any
Speaker 1: other ways?
Speaker 2: You can also work with RDF triples directly? Remember those
Speaker 2: are like the building blocks of ontologies already too. Lets
Speaker 2: you manipulate these triples which can be used to transfer
Speaker 2: information between worlds if you need more fine grain control.
Speaker 1: Okay, so we have a few different options for interworld communication.
Speaker 1: But is there anything we need to be cautious about.
Speaker 2: Yeah, it's important to remember that changes made in one
Speaker 2: world do not automatically affect the others.
Speaker 1: Ah, so if I make a change in my experimental world,
Speaker 1: it won't mess up the main world unless I explicitly
Speaker 1: those changes.
Speaker 2: Over precisely, each world maintains its own independent state.
Speaker 1: Okay.
Speaker 2: It's a powerful feature, but it's important to be aware
Speaker 2: of the separation when working with multiple worlds.
Speaker 1: This has been an incredible exploration of the capabilities of OWL.
Speaker 1: Ready to uh huh, I'm starting to feel like I
Speaker 1: can build and manipulate. That's great these ontologies with a
Speaker 1: lot more confidence.
Speaker 2: That's great to hear. And now that we have a
Speaker 2: good grasp of the fundamentals, I think it's time to
Speaker 2: move on to some real world applications.
Speaker 1: Okay, I'm ready to see how these concepts are being
Speaker 1: used in practice.
Speaker 2: Great, let's start with a field that's near and dear
Speaker 2: to my heart, bioinformatics.
Speaker 1: Bioinformatics, Now that's a field where I can imagine ontologies.
Speaker 1: You're absolutely right being incredibly useful. There's so much complex
Speaker 1: data to organize and make sense of.
Speaker 2: And one of the most widely used ontologies in bioinformatics
Speaker 2: is the geneontology.
Speaker 1: Geneontology or go for sure go. I've definitely heard that
Speaker 1: term before, and I'm not entirely sure what it is
Speaker 1: of course.
Speaker 2: So the gene ontology is a hierarchical classification system that
Speaker 2: describes the functions of genes and proteins.
Speaker 1: So it's like a giant dictionary or encyclopedia. That's a
Speaker 1: great analogy for genes and proteins.
Speaker 2: GGO provides a standardized vocabulary and structure that helps us
Speaker 2: describe what genes and proteins do in a consistent way.
Speaker 1: But WEO is more than just a list of definitions.
Speaker 2: Right, How is it actually structured? GGO is organized into
Speaker 2: three main branches or some ontologies. Molecular function This branch
Speaker 2: focuses on what a gene product actually does at the
Speaker 2: molecular level, think of things like catalytic activity or binding.
Speaker 2: Biological process. This branch describes the larger processes or pathways
Speaker 2: that a gene product is involved in. Examples would be
Speaker 2: things like cell growth or signal transduction. And then cellular component. Okay,
Speaker 2: this branch describes where a gene product is located within
Speaker 2: a cell, like the nucleus or membrane.
Speaker 1: So it's like a three dimensional map.
Speaker 2: You got it.
Speaker 1: A gene and protein function.
Speaker 2: Joe provides a comprehensive framework for understanding how genes and
Speaker 2: proteins contribute to the complexity of life.
Speaker 1: This is fascinating, but how is this actually used in research?
Speaker 2: So, show go is used in a wide range of
Speaker 2: bioinformatics applications. For example, it can help us predict the
Speaker 2: function of genes that haven't been fully studied, analyze groups
Speaker 2: of genes that are activated in certain diseases or conditions,
Speaker 2: map genes and proteins to known biological pathways, or integrate
Speaker 2: data from various sources.
Speaker 1: So it sounds like JOE is a fundamental tool. It
Speaker 1: is for anyone working with gene and protein data. Yes,
Speaker 1: but where does already two fit into all of this?
Speaker 2: Already two has excellent support for JOE. You can easily
Speaker 2: load the entire gene ontology into already two as an
Speaker 2: ontology and then use all of the features we've discussed
Speaker 2: to analyze and explore the data.
Speaker 1: So we could use owl ready too to navigate the
Speaker 1: GROW hierarchy, yes, search for specific terms and even infer
Speaker 1: relationships between different GROW concepts.
Speaker 2: Exactly, it's like having a powerful search engine and analysis
Speaker 2: toolk specifically designed for working with GROW data.
Speaker 1: Can you give me a specific example of how they
Speaker 1: might be used together in research?
Speaker 2: Sure, let's talk about protein interactions.
Speaker 1: Proteins. They're like the tiny machinescisely that make.
Speaker 2: Life happen, and proteins rarely work in isolation. They interact
Speaker 2: with each other in complex ways to carry out their functions.
Speaker 1: So understanding these interactions is key to understanding how life.
Speaker 2: Works exactly, and GROW can help us with that.
Speaker 1: Okay.
Speaker 2: Let's say we have a list of proteins that are
Speaker 2: known to interact. We can use ow already two to
Speaker 2: reach retrieve the grog terms associated with each protein, essentially
Speaker 2: annotating them with their functional roles.
Speaker 1: So we're giving each protein a label that tells us
Speaker 1: what it does.
Speaker 2: Right. Then we can use already two's reasoning capabilities okay
Speaker 2: to analyze those labels, looking for patterns or connections between
Speaker 2: the functions of the interacting proteins.
Speaker 1: Oh, so we might find that a group of interacting
Speaker 1: proteins is involved in the same biological process exactly, even
Speaker 1: if we didn't know that before.
Speaker 2: This kind of analysis can be incredibly valuable for generating hypotheses,
Speaker 2: understanding disease mechanisms, and even identifying potential drug targets.
Speaker 1: It shows how already two and Joe can be used
Speaker 1: to make sense of complex biological data.
Speaker 2: It's just one example of how ontologies are being used
Speaker 2: to advance our understanding of the complexities of life.
Speaker 1: That's right, we've only just scratched the surface of real
Speaker 1: world applications.
Speaker 2: Where should we explore next gears and talk about healthcare? Healthcare?
Speaker 1: I imagine ontologies could be really valuable for organizing and
Speaker 1: making sense of all the medical knowledge.
Speaker 2: Absolutely, healthcare is a field that's ripe for ontology driven solutions.
Speaker 2: Think about all the different terms and concepts and procedures
Speaker 2: in medicine. It's a complex web of information, and.
Speaker 1: With electronic health records becoming more common. Uh huh, there's
Speaker 1: an even greater need to standardize and make sense of
Speaker 1: all that data exactly.
Speaker 2: Ontologies can help us do just that. They can be
Speaker 2: used to create a structured representation of medical knowledge that
Speaker 2: can be understood by both humans and computers.
Speaker 1: So how are ontologies actually being used in healthcare today?
Speaker 2: So one area where ontologies are making a big impact
Speaker 2: is in electronic health records or EHRs.
Speaker 1: EHRs.
Speaker 2: Okay, they can help standardize the way patient information is recorded,
Speaker 2: making it easier to share data between different healthcare providers.
Speaker 1: That makes sense. It's like having a common language precise
Speaker 1: for health care data, so everyone's on the same page.
Speaker 2: And this can have a huge impact on patient care.
Speaker 2: For example, if a patient goes to a new doctor, Yeah,
Speaker 2: the doctor can quickly access their complete medical history from
Speaker 2: their EHR, even if it was created by a different
Speaker 2: healthcare provider.
Speaker 1: That could definitely help prevent misdiagnoses and ensure that patients
Speaker 1: get the right treatment exactly.
Speaker 2: Another area where ontologies are being used is in clinical
Speaker 2: Decisions support systems or cdss's. These systems help clinicians make
Speaker 2: more informed decisions about patient care.
Speaker 1: So how do ontologies fit into these systems.
Speaker 2: Ontologies can be used to encode medical guidelines and best practices.
Speaker 2: This allows cdssses to analyze patient data and provide clinicians
Speaker 2: with viance based recommendations.
Speaker 1: So it's like having a team of expert advisors.
Speaker 2: That's a great way to think about it.
Speaker 1: Working behind the scenes, Yeah, to help doctors make the
Speaker 1: best decisions.
Speaker 2: And this can be incredibly valuable for improving patient safety
Speaker 2: and outcomes.
Speaker 1: It seems like ontologies are already making a tangible difference
Speaker 1: in healthcare.
Speaker 2: They are, but healthcare isn't the only field where ontologies
Speaker 2: are making a difference.
Speaker 1: Are there any other fields where they're being used effectively?
Speaker 2: Absolutely, ontologies are finding applications in a wide range of domains,
Speaker 2: engineering and manufacturing to business and finance.
Speaker 1: Wow, that's a pretty diverse range. Could you give me
Speaker 1: an example of how ontologies might be used in engineering? Sure?
Speaker 2: Think about the design and development of a complex product
Speaker 2: like an airplane. Okay, there are so many different components,
Speaker 2: systems and subsystems involved.
Speaker 1: Right, and it's critical that all those pieces work together flawlessly.
Speaker 2: Exactly, and ontologies can help engineers manage this complexity. They
Speaker 2: can be used to create a detailed model of the airplane,
Speaker 2: representing all its parts and how they interact.
Speaker 1: So it's like having a digital blueprint of the airplane
Speaker 1: decisely that captures all the engineering knowledge and relationships.
Speaker 2: And this model can be used for a variety of persons,
Speaker 2: from simulation and testing to maintenance and troubleshooting.
Speaker 1: I can see how that would be incredibly valuable for
Speaker 1: ensuring the safety and reliability of such a complex system.
Speaker 2: Absolutely, ontologies are also being used in manufacturing to optimize
Speaker 2: production processes, manage supply chains, and improve product quality.
Speaker 1: So they're helping to make engineering and manufacturing more efficient.
Speaker 2: And effective exactly. And these are just a few examples
Speaker 2: of how ontologies are being used in the real world.
Speaker 2: The possibilities are truly vast, and as the field of
Speaker 2: ontology engineering continues to evolve, we can expect to see
Speaker 2: even more innovative applications emerge in the years to come.
Speaker 1: This has been an incredible journey through the world of
Speaker 1: ontology has I feel like I've gained a whole new
Speaker 1: understanding of their power and potential.
Speaker 2: It's been a pleasure exploring these concepts with you.
Speaker 1: The pleasure's all might and I hope.
Speaker 2: You've found this dive into ontologies as fascinating as I have.
Speaker 1: Absolutely, so as we wrap up, what are some key
Speaker 1: takeaways that our listeners should keep in mind.
Speaker 2: Well, I think the most important takeaway is that ontologies
Speaker 2: are not just abstract concepts. They're practical tools that are
Speaker 2: already being used to solve real world problems and create
Speaker 2: innovative solutions in a wide range of domains.
Speaker 1: And we've seen examples of that in healthcare, bioinformatics, engineering,
Speaker 1: just to.
Speaker 2: Name a few exactly. Another key takeaway is that Already
Speaker 2: two is a powerful and accessible library. Yeah, that makes
Speaker 2: it easy to work with ontologies and Python.
Speaker 1: Okay.
Speaker 2: Whether you're a researcher, developer, or just someone who's curious
Speaker 2: about ontologies, Already too provides a great starting point for
Speaker 2: exploring this fascinating field.
Speaker 1: So if you're intrigued by the possibilities of ontologies, don't
Speaker 1: be intimidated. Yeah, there are plenty of resources available to
Speaker 1: help you get started.
Speaker 2: I remember, ontologies are all about capturing and organizing knowledge, right,
Speaker 2: So whether you're working with medical data, engineering, designs, or
Speaker 2: even just trying to make sense of your own personal
Speaker 2: knowledge base, ontologies can provide a valuable framework for structuring
Speaker 2: and understanding information.
Speaker 1: This has been an incredible deep dive, and I want
Speaker 1: to thank you for sharing your expertise with us.
Speaker 2: It's been my pleasure, and to our listeners.
Speaker 1: I encourage you to keep exploring the world of ontologies. Yes,
Speaker 1: who knows, you might just discover the next groundbreaking application.
Speaker 2: You never know