#ontology — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #ontology, aggregated by home.social.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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?
-
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?
-
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?
-
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?
-
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?
-
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
-
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
-
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
-
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
-
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
-
I critique and Dis–Integrate Alex O'Connor's Cartesian argument for Panpsychism.
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
-
#DigitalIdentityOptimization as Articulated Revelation (#Claude #Opus 4.7 Search)
Epistemological #analysis by Claude as #analyst, #operator, and #theorist: #DIO represents #shift in approaching #digitalidentity.
Key #vectors:
• #Ontology of #LatentSpace
• Transdisciplinary Convergence
• #Operator role: Contingent or necessary?
DIO is not tactic, but a structural necessity dictated by the materialization of #KnowledgeGraphs, #AIOverviews, and #LLM citations.
-
#DigitalIdentityOptimization as Articulated Revelation (#Claude #Opus 4.7 Search)
Epistemological #analysis by Claude as #analyst, #operator, and #theorist: #DIO represents #shift in approaching #digitalidentity
Key #vectors:
• #Ontology of #LatentSpace
• Transdisciplinary Convergence
• #Operator role: Contingent or necessary?
DIO is not tactic, but a structural necessity dictated by the materialization of #KnowledgeGraphs, #AIOverviews, and #LLM citations.
-
I witter on about Qualified Subjectivity. Ponder with me.
https://philosophics.blog/2026/01/16/qualified-subjectivism/?utm_source=masto&utm_medium=social
Why objective reality is an anachronistic myth.
#philosophy #politics #society #culture #mediation #truth #anythinggoes #relativism #subjectivity #language #semantics #ontology #epistemology #kant #phenomena #noumena #history #intetrfaces #blog #podcast
-
Western philosophy made a mess of colour by pretending it lives either “in objects” or “in minds.”
In reality, colour happens in the encounter — mediated biologically, cognitively, linguistically, culturally.
I dismantle the old binaries and show why the apple was never “red in itself” to begin with.Full post, illustrations included:
https://philosophics.blog/2025/11/24/seeing-red-or-how-the-enlightenment-got-colour-blind/?utm_source=masto&utm_medium=social&utm_campaign=meow#philosophy #nexus #ontology #perception #philosophy #amwriting #essay #blog #podcast #language #culture #thought #enactivism #meow
-
🧬 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
-
Most Online Chaos Magick Is Naming Mnemonic Variables
No matter how intricate your sigil is, it’s still just a semiotic identifier—a signifier. You could call it x, and it would carry the same ontological weight. Sigils point to concepts, entities, or they act as a signature. At the end of the day, you’ve just created a decorative label. Sigils are glyphs, not alphabets or languages like Theban, Celestial, Enochian, or the Alphabet of Daggers.
Sigils made using Austin Spare’s method aren’t languages—they’re stylized abstractions. You’re not writing a sentence in some hidden tongue; you’re making a visual variable. A graphic tag with no linguistic scaffolding. There’s nothing to “read” in it—no syntax, no grammar, no pronunciation. Just an intentional scribble pointing toward a thought.
Compare that to Theban, Celestial, Enochian, or the Alphabet of Daggers. Those are actual alphabets. Magical languages. They follow consistent letter-for-letter substitutions. They can obscure meaning—because there’s real meaning to obscure. You can encode texts, chants, names. They operate like ciphers, because that’s exactly what they are.
Spare-style sigils don’t encrypt—they erase. When you compress a phrase into a symbol, you’re not hiding language, you’re letting it go. You’re collapsing intention into a single mark that doesn’t depend on literacy, just mental association.
Magical languages at least do something. They obscure. They encode. Run a sentence through Theban or Celestial, and you get something arcane-looking and unreadable—because it is, unless you know the key. That’s the point: symbolic misdirection. Encryption with intent.
Sigils, especially the Spare-style ones, don’t do that. You’re not concealing a message; you’re flattening it into a shape. It’s not code—it’s a label. A name tag. You take a phrase, compress it into a glyph, and suddenly it’s a “magickal symbol.” But it’s really just a symbolic pointer—a variable, like in programming. And just like with variables, the more convoluted the name, the more annoying it is to use. If your sigil looks like a spiky eldritch mandala, congrats: you’ve made a beautiful, unreadable label.
Honestly, watching people crank these out like they’re reinventing the arcane wheel—when they’re just decorating the same psychological placeholder—it’s almost a relief. The more time someone spends designing a stylized faux ceremonial symbol symbol for “money now please,” the less likely they are to be doing anything dangerously effective. A lot of what gets called “chaos magick” online is really just overdesigned variable naming by people who think the aesthetic is the spell.
Sure, sigils, metaphors, and myths all operate symbolically—but they don’t hit the same. A sigil is a symbol boiled down to a label. A shortcut. It points to something. A mythology doesn’t just point—it breathes. It moves through story, layers, archetypes. That’s why mythic metaphors and ritual drama land harder: they’re not just signs, they’re immersive systems of meaning.
A myth doesn’t just say “this is like that.” It binds symbols into patterns. It creates tension, resonance, transformation. Allegory can carry cosmology. Sigils don’t do that. They’re more like programming variables—arbitrary labels you assign meaning to. Useful? Sure. But flat. A box with a name. Writing “dragon” on the lid doesn’t conjure the force of a real dragon narrative.
That’s why stories, dreams, and rituals move people—and sigils usually don’t, unless you’ve already decided they will. Metaphor speaks to the deep mind. It speaks with symbols, not just about them. Sigils? They’re like filing tabs. Metaphors are the stories inside the folders.
So yeah—they’re both symbols. But one is a placeholder. The other is a living structure.
Exactly—whether you sketch a swirling, ornate sigil or just scrawl an x, you’re still tagging the same conceptual box. The content doesn’t change just because the outside is fancier. That’s the point: the sigil isn’t the thing. It’s the pointer. It’s the tag that says, “this is where I stored that intention,” or “this represents that entity.” It doesn’t gain power because it looks more esoteric.
People get caught up in the design and forget the purpose. A sigil doesn’t summon anything by itself—it’s a reference. It’s not the payload. You’re not conjuring a spirit with the shape; you’re doing it with the meaning you’ve assigned. The symbol just helps you focus—like putting a label on a folder.
This is why making the sigil “look magickal” is mostly cosmetic. Dress up the label all you want—it still opens the same box. The real work isn’t in the glyph; it’s in what the glyph means and how your mind interacts with that meaning. So yes, swap the sigil for an x—the box still holds the demon. All you’ve changed is the font.
Fediverse Reactions
#Archetypes #ceremonialMagic #ceremonialMagick #chaosMagick #Crowley #demons #Discordians #egregore #egregores #exemplification #fascist #grimoire #grimoires #HermeticOrderOfTheGoldenDawn #hermeticism #hyperSigils #invocation #JungianArchetypes #magicalLanguages #magick #mythologies #mythology #myths #occult #occultism #occulture #Ontology #ostension #pagan #paganism #paranormal #paranormalCommunities #postmodernism #poststructuralism #Semiotics #sigil #sigilMagic #sigilMagick #sigils #sorcery #urbanMyth
-
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.
Vassily Kandinsky, 1913 – Composition VII (1913)
According to Kandinsky, this is the most complex piece he ever painted.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
- 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
- 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
- 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
- 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
- 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
- Kilicoglu, H., et al. (2024). Towards better understanding of biomedical knowledge graphs: A survey. arXiv preprint. https://arxiv.org/abs/2402.06098
- 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
- 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
- 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
-
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.
Vassily Kandinsky, 1913 – Composition VII (1913)
According to Kandinsky, this is the most complex piece he ever painted.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
- 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
- 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
- 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
- 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
- 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
- Kilicoglu, H., et al. (2024). Towards better understanding of biomedical knowledge graphs: A survey. arXiv preprint. https://arxiv.org/abs/2402.06098
- 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
- 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
- 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
-
#WillemDrees - The #Mystery of #Existence
https://www.youtube.com/watch?v=-9iEMOuG6LQ
#Philosophy #Metaphysics #Ontology #PhilosophyOfCosmology #Cosmology #PhilosophyOfScience #Science #Astrophysics #Logic #LawsOfPhysics #LawsOfNature #QM #QuantumMechanics #QuantumPhysics #Physics #QuantumFluctuation #Reality #God #Theology #Theism #Truth #Answer #Answers #CloserToTruth #RobertKuhn
-
Philosophy humor is the best humor: #philosophy #heidegger #germany #ontology #fascism #dasein #geworfenheit #Befindlichkeit #eigentlichkeit #uneigentlichkeit #wwii
-
Everybody can design an ontology. But, how to design better ontologies? This is one of the central questions of the next section of our free #kg2023 lecture.
#OpenHPI video: https://open.hpi.de/courses/knowledgegraphs2023/items/1kpaqjHwxUI4HZxm1Wb9ft
youtube video: https://www.youtube.com/watch?v=RHjoCpNj3KQ
slides: https://zenodo.org/records/10135481
@tabea @sashabruns @fiz_karlsruhe @fizise #knowledgegraphs #semanticweb #ontologies #ontology #ontologydesign -
Everybody can design an ontology. But, how to design better ontologies? This is one of the central questions of the next section of our free #kg2023 lecture.
#OpenHPI video: https://open.hpi.de/courses/knowledgegraphs2023/items/1kpaqjHwxUI4HZxm1Wb9ft
youtube video: https://www.youtube.com/watch?v=RHjoCpNj3KQ
slides: https://zenodo.org/records/10135481
@tabea @sashabruns @fiz_karlsruhe @fizise #knowledgegraphs #semanticweb #ontologies #ontology #ontologydesign -
Everybody can design an ontology. But, how to design better ontologies? This is one of the central questions of the next section of our free #kg2023 lecture.
#OpenHPI video: https://open.hpi.de/courses/knowledgegraphs2023/items/1kpaqjHwxUI4HZxm1Wb9ft
youtube video: https://www.youtube.com/watch?v=RHjoCpNj3KQ
slides: https://zenodo.org/records/10135481
@tabea @sashabruns @fiz_karlsruhe @fizise #knowledgegraphs #semanticweb #ontologies #ontology #ontologydesign -
Everybody can design an ontology. But, how to design better ontologies? This is one of the central questions of the next section of our free #kg2023 lecture.
#OpenHPI video: https://open.hpi.de/courses/knowledgegraphs2023/items/1kpaqjHwxUI4HZxm1Wb9ft
youtube video: https://www.youtube.com/watch?v=RHjoCpNj3KQ
slides: https://zenodo.org/records/10135481
@tabea @sashabruns @fiz_karlsruhe @fizise #knowledgegraphs #semanticweb #ontologies #ontology #ontologydesign -
Everybody can design an ontology. But, how to design better ontologies? This is one of the central questions of the next section of our free #kg2023 lecture.
#OpenHPI video: https://open.hpi.de/courses/knowledgegraphs2023/items/1kpaqjHwxUI4HZxm1Wb9ft
youtube video: https://www.youtube.com/watch?v=RHjoCpNj3KQ
slides: https://zenodo.org/records/10135481
@tabea @sashabruns @fiz_karlsruhe @fizise #knowledgegraphs #semanticweb #ontologies #ontology #ontologydesign -
#Kant - 'Transcendental Idealism'
"Are Space and Time all in your Mind?"
https://www.youtube.com/watch?v=JZEhrABp2wQ&ab_channel=PhilosophyVibe
#ImmanuelKant #Space #Time #SpaceTime #TranscendentalIdealism #Metaphysics #Idealism #Realism #IndirectRealism #Mind #TheMind #Peerception #Ontology #Noumenon #Noumena #Noumenal #Phenomenon #Phenomena #Phenomenal #Phenomenology
-
Branch raises $50M to offer bundled auto & home insurance via an API - Branch Insurance, a startup offering bundled home and auto insurance, has raised $... - http://feedproxy.google.com/~r/Techcrunch/~3/_TOzhQHkxEI/ #americanfamilyventures #foundationcapital #productmanagement #fundings&exits #venturecapital #rocketmortgage #ruthfoxeblader #recentfunding #anthemisgroup #autoinsurance #carinsurance #unitedstates #insurance #insurtech #startups #allstate #columbus #ontology #finance #ohio