#ontology — Public Fediverse posts

When the #semanticSpectrum was introduced 25 years ago, it was meant to be a conceptual tool to help programmers and architects understand the relative benefits of different levels of semantic effort.

Now, a couple of decades later, Jessica Talisman has repurposed the model as a way to organize the actual process of developing enterprise #knowledgeGraphs, which she shares in her new book, #Ontology Pipeline: A Framework for Knowledge Engineering.

https://knowledgegraphinsights.com/jessica-2/

Oh, nice! The Citation Counting and Context Characterization Ontology (#C4O) got a new documentation page some weeks ago: https://sparontologies.github.io/c4o/current/c4o.html

It's not clear to me though, if the ontology was changed.

@essepuntato Did I oversee release notes or version info?

#Ontology #OpenResearchInformation

Your existence doesn't substantiate your beliefs.

And criticizing your beliefs doesn't substantiate your existence.

Ignoring these logical heuristics removes you from the realm of intellectualism and into the realm of dogma.

#philosophy #theology #epistemology #ontology #existentialism #socialtheory #sociology #religion #theology

🌐❓Why #Reality is #Relations Not #Objects – Do We Need a New #Philosophy of the #NaturalSciences?

Modern #metaphysics does not need to hide behind the natural #sciences. Rather, it serves as a compass for a scientific-theoretical framework.

🎧 https://open.spotify.com/episode/665z013tMwIWapuuFHTxnb?si=TtTU5o68SmC25QDH93WTsw

#StructuralRealism #PhilosophyOfScience #Physics #AI #Cybernetics #MindAndMatter #ParadigmShift #Ontology #ScienceEthics #PhilosophyBlog #Podcast #philosophies_de

The Reality of Dreams

https://johnbronze.wordpress.com/2026/07/24/the-reality-of-dreams/

I critique and Dis–Integrate Alex O'Connor's Cartesian argument for Panpsychism.

https://philosophics.blog/2026/06/29/deconstructing-consciousness-without-panpsychism/?utm_source=masto&utm_medium=social

Many philosophers I respect support Panpsychism, but Homey don't play dat.

#philsophy #panpsychism #video #youtube #physics #emergence #consciousness #podcast #magic #blog #ontology #reductionism #deflationary #metaphysics #awareness #phenomena #experience #qualia #idealism #materialism

I critique and Dis–Integrate Alex O'Connor's Cartesian argument for Panpsychism.

https://philosophics.blog/2026/06/29/deconstructing-consciousness-without-panpsychism/?utm_source=masto&utm_medium=social

Many philosophers I respect support Panpsychism, but Homey don't play dat.

#philsophy #panpsychism #video #youtube #physics #emergence #consciousness #podcast #magic #blog #ontology #reductionism #deflationary #metaphysics #awareness #phenomena #experience #qualia #idealism #materialism

I critique and Dis–Integrate Alex O'Connor's Cartesian argument for Panpsychism.

https://philosophics.blog/2026/06/29/deconstructing-consciousness-without-panpsychism/?utm_source=masto&utm_medium=social

Many philosophers I respect support Panpsychism, but Homey don't play dat.

#philsophy #panpsychism #video #youtube #physics #emergence #consciousness #podcast #magic #blog #ontology #reductionism #deflationary #metaphysics #awareness #phenomena #experience #qualia #idealism #materialism

I critique and Dis–Integrate Alex O'Connor's Cartesian argument for Panpsychism.

https://philosophics.blog/2026/06/29/deconstructing-consciousness-without-panpsychism/?utm_source=masto&utm_medium=social

Many philosophers I respect support Panpsychism, but Homey don't play dat.

#philsophy #panpsychism #video #youtube #physics #emergence #consciousness #podcast #magic #blog #ontology #reductionism #deflationary #metaphysics #awareness #phenomena #experience #qualia #idealism #materialism

I critique and Dis–Integrate Alex O'Connor's Cartesian argument for Panpsychism.

https://philosophics.blog/2026/06/29/deconstructing-consciousness-without-panpsychism/?utm_source=masto&utm_medium=social

Many philosophers I respect support Panpsychism, but Homey don't play dat.

#philsophy #panpsychism #video #youtube #physics #emergence #consciousness #podcast #magic #blog #ontology #reductionism #deflationary #metaphysics #awareness #phenomena #experience #qualia #idealism #materialism

Your BlueSky Feed Is Porn You Didn’t Ask For Because Your Friends Are Gooners With a Severe Porn Addiction

A common complaint I see people make on Bluesky is: why am I being served so much porn or things I am not interested in? They will incorrectly believe that the algorithm is broken. It’s not broken. You didn’t know the people you knew as well as you thought you did. Porn addiction is a thing, and porn addiction is especially common with weebs. You’re seeing deranged shit because people you follow have porn addictions and are into deranged shit. So, though you may not be consuming porn, people in your network are. That activity kicks into your feeds.

The issue I have with that is that it essentially normalizes being sex pests in a space on the Internet. That sets the expectation that it is good—attractive, even—to act like that elsewhere. That expectation alienates relationships. Bluesky creates a cultural space that offers an unrealistic, bizarre representation of social relationships, which isolates and alienates the users who stay on there consuming erotica and porn like they do.

So, user repos in Bluesky have a property for likes. Bluesky’s underlying AT Protocol stores likes as first-class structured records in each user’s AT Protocol repository. In the AT Protocol lexicon, a like is an app.bsky.feed.like record type. Unlike a simple boolean flag on a post, it is its own record with a creation timestamp and a subject field that holds a strong reference to the liked record.

That strong reference is composed of an AT-URI and a CID. The AT-URI identifies the exact record in the network by DID, collection, and record key. The CID is a cryptographic content identifier that uniquely identifies the exact content of that liked record.

These like records exist under the app.bsky.feed.like namespace in the user’s repo. Bluesky’s repo model is built so that these repos are hosted on a user’s Personal Data Server and are publicly readable through the AT Protocol APIs. Because of that, the like record and its fields can be fetched, indexed, and used by any client or service that can query the protocol.

The protocol exposes operations like getLikes. This returns all of the like records tied to a particular subject’s AT-URI and CID. It also exposes getActorLikes. This returns all of the subject references a given actor has liked. Those API calls return structured like objects with timestamps and subject references directly from the public repository data.

Various feeds hosted by different PDSs use the likes property to construct the feeds that you see. Since the likes of people you follow are included in your social graph, along with your own likes, you’re going to get served the porn they are consuming. Because likes are public and anyone can write an algorithm to see everyone’s likes, you can clearly see just how much porn people are consuming.

Honestly, what started to turn my stomach about the people on Bluesky is how they behave across different contexts. If you look through the records of the posts they interact with, you’ll see them engaging with political posts in the replies like a normal person. Then, when you look through their AT Protocol records, you see hours and hours of them interacting with every kind of porn imaginable. I am not exaggerating. Hours of likes for porn posts within 1–10 minutes of each other. Am I sex-negative? A prude? No, this site is filled with furry, gay bara porn, lol. You can have a drink without being an alcoholic. The problem with these people is like people who can’t have one drink without drinking the whole fucking day; they can’t consume porn in healthy ways.

I think people assume that their feed is customized for them and based on their likes. No—feeds are generalized based on what everyone likes and then served to your subgraph. It’s not just about who you follow; it’s about who they follow. So if you follow someone who follows a lot of people with porn addictions, you will see porn. Bluesky isn’t weighting the algorithm to do this. Basically, it’s the people in your social network with furry, hentai, or trans porn addictions who are driving it.

BlueSky’s Solution To Moderating Is Moderating Without Moderating via Social Proximity

I have noticed a lot of people are confused about why some posts don’t show up on threads, though they are not labeled by the moderation layer. Bluesky has begun using what it calls social neighborhoods (or network proximity) as a ranking signal for replies in threads. Replies from people who are closer to you in the social graph, accounts you follow, interact with, or share mutual connections with, are prioritized and shown more prominently. Replies from accounts that are farther away in that network are down-ranked. They are pushed far down the thread or placed behind “hidden replies.”

Each person gets their own unique view of a thread based on their social graph. It creates the impression that replies from distant users simply don’t exist. This is true even though they’re still technically public and viewable if you expand the thread or adjust filters. Bluesky is explicitly using features of subgraphs to moderate without moderating. Their reasoning is that if you can’t see each other, you can’t harass each other. Ergo, there is nothing to moderate.

Bluesky mentions that here:

https://bsky.social/about/blog/10-31-2025-building-healthier-social-media-update

As a digression, I’m not going to lie: I really enjoyed working on software built on the AT protocol, but their fucking users are so goddamn weird. It’s sort of like enjoying building houses, but hating every single person who moves into them. But, you don’t have to deal with them because you’re just the contractor. That is how I feel about Bluesky. I hate the people. I really like the protocol and infrastructure.

I sort of am a sadist who does enjoy drama, so I do get schadenfreude from people with social media addictions and parasocial fixations who reply to random people on Bluesky, because they don’t realize their replies are disconnected from the author’s thread unless that person is within their network. They aren’t part of the conversation they think they are. They’re algorithmically isolated from everyone else. Their replies aren’t viewable from the author’s thread because of how Bluesky handles social neighborhoods.

Bluesky’s idea of social neighborhoods is about grouping users into overlapping clusters based on real interaction patterns rather than just the follow graph. Unlike Twitter, it does not treat the network as one big public square. Instead, it models networks of “social neighborhoods” made up of people you follow, people who follow you, people you frequently interact with, and people who are closely connected to those groups. They’re soft, probabilistic groupings rather than strict labels.

Everyone does not see the same replies. Bluesky is being a bit vague with “hidden.” Hidden means your reply is still anchored to the thread and can be expanded. There is another way Bluesky can handle this. Bluesky uses social neighborhoods to judge contextual relevance. Replies from people inside or near your social neighborhood are more likely to be shown inline with a thread, expanded by default, or served in feeds. Replies from outside your neighborhood are still public and still indexed, but they’re treated as lower-context contributions.

Basically, if you reply to a thread, you will see it anchored to the conversation, and everyone will see it in search results, as a hashtag, or from your profile, but it will not be accessible via the thread of the person you were replying to. It is like shadow-banning people from threads unless they are strongly networked.

Because people have not been working with the AT Protocol like I have, they assume they are shadow-banned across the entire Bluesky app view. No—everyone is automatically shadow-banned from everyone else unless they are within the same social neighborhood. In other words, you are not part of the conversation you think you are joining because you are not part of their social group.

Your replies will appear in profiles, hashtag feeds, or search results without being visually anchored to the full thread. Discovery impressions are neighborhood-agnostic: they serve content because it matches a query, tag, or activity stream. Once the reply is shown, the app then decides whether it’s worth pulling in the rest of the conversation for you. If the original author and most participants fall outside your neighborhood, Bluesky often chooses not to expand that context automatically.

Bluesky really is trying to avoid having to moderate, so this is their solution. Instead of banning or issuing takedown labels to DIDs, the system lets replies exist everywhere, but not in that particular instance of the thread.

I find this ironic because a large reason why many people are staying on Bluesky and not moving to the fediverse—thank God, because I do not want them there—is discoverability, virality, and engagement.

In case anyone is asking how I know so much about how these algorithms work: I was a consultant on a lot of these types of algorithms, so I certainly hope I’d know how they work, lol. No, you get no more details about the work I’ve done. I have no hand in the algorithm Bluesky is using, but I have proposed and implemented that type of algorithm before.

I have an interest in noetics and the noosphere. A large amount of my ontological work is an extension of my attempts to model domains that have no spatial or temporal coordinates. The question is how do you generalize a metric space that has no physically, spatial properties. I went to school to try to formalize those ideas. Turns out they’re rather useful for digital social networks, too. The ontological analog to spatial distance, when you have no space, is a graph of similarities.

Astroturfing Is Pretty Pointless When Social Subgraphs Are Fragmented (e.g., the Fediverse)

I am seeing astroturfing in the fediverse again, by AT Protocol developers implicitly trying to shill their products. I think it is stochastic behavior by developers with too much time on their hands. Honestly, I do not care. I like the people on ActivityPub more, but I like the AT Protocol better, and I have developed for both. Astroturfing on ActivityPub networks is fascinating to me because it is so pointless.

I am actually a Computational Biologist and Computer Scientist whose specialty is combinatorics, social graphs, graph theory, etc. Specifically, I use this to create epidemiological models for the memetic layer of human behaviors that act as vectors for diseases, using the SIRS model. I do not just study germs; I study human behaviors.

The models I construct extend into a “memetic layer,” in which beliefs, norms, and behaviors (such as risk-taking, compliance with public health measures, or susceptibility to misinformation) spread contagiously through social networks. These behaviors function as vectors that modulate biological transmission rates. As a result, the spread of ideas can accelerate, dampen, or reshape the spread of disease. By running computational simulations and agent-based models on these graphs, I study how network structure, influential nodes, clustering, and platform-specific dynamics affect behavioral contagion. I also examine how these factors influence epidemiological outcomes.

To say it very concisely, I study how the spread of bat-shit insane beliefs, shit posts, and memes influences whether or not there is a measles outbreak in Texas. Ironically, this is an evolution of my studying semiotics, memetics, and chaos magick in high school. I got a job where I can use occult, anarchist techniques professionally.

I think a large reason why I do not care about astroturfing in the fediverse is that it’s so pointless, lol. Astroturfing to manipulate the narrative would actually work better on Bluesky to keep people there than trying to recruit from the fediverse. Furthermore, big instances are relatively small. Some people on Bluesky have follower lists larger than an entire large instance in the fediverse.

Within ActivityPub networks, astroturfing rarely propagates far, because whether information spreads depends on properties of the social graph itself. Dense connectivity, short paths between communities, and a sufficient number of cross-cutting ties support diffusion. ActivityPub’s architecture tends to produce graphs that are fragmented and highly modular. This limits the reach of coordinated activity.

ActivityPub is a system where each instance maintains its own local user graph and exchanges activities through inboxes and outboxes. This makes it autonomous and decentralized. The network consists of loosely connected subgraphs. Cross-instance edges appear only through explicit follow relationships. The ActivityPub protocol does not provide a shared or complete view of the network. Measurements of the fediverse consistently show uneven connectivity between instances, clustering at the instance level, and relatively long effective path lengths across the network. Under these conditions, large cascades are uncommon.

Instance-level clustering means that in ActivityPub networks, users interact much more with others on the same server than with users on different servers. Because each instance has its own local timeline, culture, and moderation, connections form densely within instances and only sparsely across them through explicit follow relationships. This creates a network made up of tightly connected local communities linked by relatively few cross-instance ties, which slows the spread of information beyond its point of origin.

However, with the AT Protocol, global indexing and aggregation are explicitly supported. Relays and indexers can assemble near-complete views of the social graph. Applications built on top of this infrastructure operate over a graph that is denser and easier to traverse. There are fewer structural barriers between communities. The diffusion dynamics change substantially when content can move across the graph without relying on narrow federated paths.

Astroturfing depends on coordinated amplification, typically through tightly synchronized clusters of accounts intended to manufacture visibility. Work on coordinated inauthentic behavior shows that these tactics gain traction when they intersect highly connected regions of the graph or bridge otherwise separate communities. In networks with strong modularity, coordination remains local. ActivityPub’s federation model produces this kind of modularity by default. Coordinated clusters stand out clearly within instances. Their effects remain confined to those local neighborhoods.

Astroturfing on ActivityPub therefore tends to stall on its own because of the underlying graph topology. Without dense inter-instance connectivity or any form of global indexing, coordinated campaigns have a hard time moving beyond the immediate regions where they originate. Systems built on globally indexable social graphs, including those enabled by the AT Protocol, expose a much larger surface for viral spread. Network structure and connectivity account for the divergence where that is independent of moderation, cultural norms, ideology, or intent.

It’s just really funny to me how these stochastic techbro groups waste so many resources. I personally don’t want to go viral, which is why I avoid platforms where I can. The fact that it’s harder to achieve high virality on ActivityPub is exactly why I prefer the fediverse over the Atmosphere. One way to think about it is that you can change the ‘genetics’ of a system with a retrovirus, where memetic entities act as cultural retroviruses to reprogram the cultural loci of a space. That is their end goal. They are trying to hijack cultures memetically. You see this a lot with culture jamming.

Basically, the astroturfing on ActivityPub networks is designed to jam and subvert the culture. But, as I have already said, the topological structure makes memetic virality stall. They cannot achieve that kind of viral spread in the fediverse, which is why I cannot understand why they do this every year.

Wo das Meer trägt und widerspricht – Über Hyperkomplettheit und die Grenzen ontologischer Kohärenz
👉 https://whisper7.substack.com/p/wo-das-meer-tragt-und-widerspricht

#Philosophie #Wissenschaft #Sprache #Erkenntnis #Metapher #Ontologie

Where the Sea Carries and Contradicts – On Hypercompleteness and the Limits of Ontological Coherence
👉 https://whisper7.substack.com/p/where-the-sea-carries-and-contradicts

#Philosophy #Science #Language #Knowledge #Metaphor #Ontology

It's happening again.

👉 https://semweb.pro/conference/2025/

Nous serons présents jeudi 27 novembre prochain à la conférence @semwebpro qui aura lieu au FIAP Paris !

#semwebpro #semweb #websem #opendata #linkeddata #linkedopendata #knowledgegraph #thesaurus #ontology #RDF #SPARQL #SHACL #OWL #JSONLD

"Translating and Formalizing the MIRAGE Guidelines to a Prototype MIRAGE Ontology and DCAT3 Extension Vocabulary for Glycomics Data Management" https://doi.org/10.37044/osf.io/wj8bz_v1

"We present the first comprehensive semantic formalization of MIRAGE guidelines through an integrated RDF ontology framework comprising the MIRAGE Ontology and MIRAGE-DCAT3 vocabulary. The MIRAGE Ontology models glycan structures, biological specimens, analytical instruments, and experimental processes with formal OWL semantics and SHACL validation constraints." https://index.biohackrxiv.org//2025/09/30/wj8bz.html

#biohackathon #BH25JP #shacl #ontology

"[V]alue is the product of wage labour in a capitalist system, and all the things that this entails. Value is not an ontologically distinct, ‘abstract’ substance, existing in some other plane of reality, birthed into that plane by the demonic machinations of capital. Value – like gravity – is a conceptual representation of a complex process that is difficult for all of us to comprehend.

To repeat: value is not an object, but a process. Many have said this, but the implication I emphasise here is this: if value is not an object, but a process (or set of processes) it cannot be a ‘real abstraction’, insofar as this term is commonly understood to refer to something immaterial. Everything that creates value happens here, in this world, and is explainable within it, without need for reference to a wicked Narnia where a white queen of value coordinates our realm through her interdimensional spyglass."

https://www.radicalphilosophy.com/article/on-the-bourgeois-concept-of-real-abstraction

#Marxism #CriticalTheory #RealAbstraction #Ontology #WertKritik

Merci à tous pour vos présentations !

Nous avons reçu cette année 17 propositions pour SemWeb.pro 2025 !

Retrouvons-nous le 15 juillet pour découvrir le programme de cette édition.

📆 : 27 novembre 2025
📌 @Fiap Paris

#semweb #websem #opendata #linkeddata #linkedopendata
#knowledgegraph #thesaurus #ontology #RDF #SPARQL #SHACL #OWL #JSONLD

🧬 Can AI fix the chaos in biological sample data?

🔗 Annotation of biological samples data to standard ontologies with support from large language models. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2025.05.020

📚 CSBJ: https://www.csbj.org/

#AIinScience #LLMs #Bioinformatics #DataAnnotation #GPT4 #BiomedicalAI #OpenScience #FAIRData #Ontology #AIinBiology #DataInteroperability

📣 Appel à candidatures #SemWebPro !
Le comité de programme souhaite intégrer de nouveaux membres ! Vous souhaitez nous aider à faire connaître cet évènement ?

Alors, rejoignez-nous !

Candidature avant le 30 avril à
[email protected] 📧

#semweb #websem #opendata #linkeddata #linkedopendata #knowledgegraph #thesaurus #ontology #RDF #SPARQL #SHACL #OWL #JSONLD

@logilab

Domain Ontologies: Indispensable for Knowledge Graph Construction

AI slop is all around and increasingly extraction of useful information will face difficulties as we start to feed more noise into the already noisy world of knowledge. We are in an era of unprecedented data abundance, yet this deluge of information often lacks the structure necessary to derive meaningful insights. Knowledge graphs (KGs), with their ability to represent entities and their relationships as interconnected nodes and edges, have emerged as a powerful tool for managing and leveraging complex data. However, the efficacy of a KG is critically dependent on the underlying structure provided by domain ontologies. These ontologies, which are formal, machine-readable conceptualizations of a specific field of knowledge, are not merely useful, but essential for the creation of robust and insightful KGs. Let’s explore the role that domain ontologies play in scaffolding KG construction, drawing on various fields such as AI, healthcare, and cultural heritage, to illuminate their importance.

At its core, an ontology is a formal representation of knowledge within a specific domain, providing a structured vocabulary and defining the semantic relationships between concepts. In the context of KGs, ontologies serve as the blueprint that defines the types of nodes (entities) and edges (relationships) that can exist within the graph. Without this foundational structure, a KG would be a mere collection of isolated data points with limited utility. The ontology ensures that the KG’s data is not only interconnected but also semantically interoperable. For example, in the biomedical domain, an ontology like the Chemical Entities of Biological Interest (ChEBI) provides a standardized way of representing molecules and their relationships, which is essential for building biomedical KGs. Similarly, in the cultural domain, an ontology provides a controlled vocabulary to define the entities, such as artworks, artists, and historical events, and their relationships, thus creating a consistent representation of cultural heritage information.

One of the primary reasons domain ontologies are crucial for KGs is their role in ensuring data consistency and interoperability. Ontologies provide unique identifiers and clear definitions for each concept, which helps in aligning data from different sources and avoiding ambiguities. Consider, for example, a healthcare KG that integrates data from various clinical trials, patient records, and research publications. Without a shared ontology, terms like “cancer” or “hypertension” may be interpreted differently across these data sets. The use of ontologies standardizes the representation of these concepts, thus allowing for effective integration and analysis. This not only enhances the accuracy of the KG but also makes the information more accessible and reusable. Furthermore, using ontologies that follow the FAIR (Findable, Accessible, Interoperable, Reusable) principles facilitates data integration, unification, and information sharing, essential for building robust KGs.

Moreover, ontologies facilitate the application of advanced AI methods to unlock new knowledge. They support both deductive reasoning to infer new knowledge and provide structured background knowledge for machine learning. In the context of drug discovery, for instance, a KG built on a biomedical ontology can help identify potential drug targets by connecting genes, proteins, and diseases through clearly defined relationships. This structured approach to data also enables the development of explainable AI models, which are critical in fields like medicine where the decision-making process must be transparent and interpretable. The ontology-grounded KGs can then be used to generate hypotheses that can be validated through manual review, in vitro experiments, or clinical studies, highlighting the utility of ontologies in translating complex data into actionable knowledge.

Despite their many advantages, domain ontologies are not without their challenges. One major hurdle is the lack of direct integration between data and ontologies, meaning that most ontologies are abstract knowledge models not designed to contain or integrate data. This necessitates the use of (semi-)automated approaches to integrate data with the ontological knowledge model, which can be complex and resource-intensive. Additionally, the existence of multiple ontologies within a domain can lead to semantic inconsistencies that impede the construction of holistic KGs. Integrating different ontologies with overlapping information may result in semantic irreconcilability, making it difficult to reuse the ontologies for the purpose of KG construction. Careful planning is therefore required when choosing or building an ontology.

As we move forward, the development of integrated, holistic solutions will be crucial to unlocking the full potential of domain ontologies in KG construction. This means creating methods for integrating multiple ontologies, ensuring data quality and credibility, and focusing on semantic expansion techniques to leverage existing resources. Furthermore, there needs to be a greater emphasis on creating ontologies with the explicit purpose of instantiating them, and storing data directly in graph databases. The integration of expert knowledge into KG learning systems, by using ontological rules, is crucial to ensure that KGs not only capture data, but also the logical patterns, inferences, and analytic approaches of a specific domain.

Domain ontologies will prove to be the key to building robust and useful KGs. They provide the necessary structure, consistency, and interpretability that enables AI systems to extract valuable insights from complex data. By understanding and addressing the challenges associated with ontology design and implementation, we can harness the power of KGs to solve complex problems across diverse domains, from healthcare and science to culture and beyond. The future of knowledge management lies not just in the accumulation of data but in the development of intelligent, ontologically-grounded systems that can bridge the gap between information and meaningful understanding.

References

1. Al-Moslmi, T., El Alaoui, I., Tsokos, C.P., & Janjua, N. (2021). Knowledge graph construction approaches: A survey of recent research works. arXiv preprint. https://arxiv.org/abs/2011.00235
2. Chandak, P., Huang, K., & Zitnik, M. (2023). PrimeKG: A multimodal knowledge graph for precision medicine. Scientific Data. https://www.nature.com/articles/s41597-023-01960-3
3. Gilbert, S., & others. (2024). Augmented non-hallucinating large language models using ontologies and knowledge graphs in biomedicine. npj Digital Medicine. https://www.nature.com/articles/s41746-024-01081-0
4. Guzmán, A.L., et al. (2022). Applications of Ontologies and Knowledge Graphs in Cancer Research: A Systematic Review. Cancers, 14(8), 1906. https://www.mdpi.com/2072-6694/14/8/1906
5. Hura, A., & Janjua, N. (2024). Constructing domain-specific knowledge graphs from text: A case study on subprime mortgage crisis. Semantic Web Journal. https://www.semantic-web-journal.net/content/constructing-domain-specific-knowledge-graphs-text-case-study-subprime-mortgage-crisis
6. Kilicoglu, H., et al. (2024). Towards better understanding of biomedical knowledge graphs: A survey. arXiv preprint. https://arxiv.org/abs/2402.06098
7. Noy, N.F., & McGuinness, D.L. (2001). Ontology Development 101: A Guide to Creating Your First Ontology. Semantic Scholar. https://www.semanticscholar.org/paper/Ontology-Development-101%3A-A-Guide-to-Creating-Your-Noy/c15cf32df98969af5eaf85ae3098df6d2180b637
8. Taneja, S.B., et al. (2023). NP-KG: A knowledge graph for pharmacokinetic natural product-drug interaction discovery. Journal of Biomedical Informatics. https://www.sciencedirect.com/science/article/pii/S153204642300062X
9. Zhao, X., & Han, Y. (2023). Architecture of Knowledge Graph Construction. Semantic Scholar. https://www.semanticscholar.org/paper/Architecture-of-Knowledge-Graph-Construction-Zhao-Han/dcd600619962d5c1f1cfa08a85d0be43a626b301

#AIInHealthcare #ArtificialIntelligence #BiomedicalOntologies #CulturalHeritageData #DataIntegration #DataInteroperability #DomainOntologies #DrugDiscovery #ExplainableAI #FAIRPrinciples #GraphDatabases #KnowledgeGraphs #KnowledgeManagement #LLMs #Ontology #OntologyDesign #OntologyDevelopment #OntologyDrivenAI #SemanticRelationships #SemanticWeb

#TimMaudlin - #Realism vs. #Antirealism

https://www.youtube.com/watch?v=wiZI91ggRNo

#Philosophy #Metaphysics #Epistemology #Ontology #Perception #Consciousness #PhilosophyOfConsciousness #Mind #PhilosophyOfMind #Math #Maths #Mathematics #Model #Models #Physics #PhilosophyOfPhysics #PhilosophyOfScience #Science #CloserToTruth #RobertKuhn