Cultural Japanで集約したIIIFマニフェストをまとめたIIIFコレクションのご紹介 Cultural Japanで集約したIIIFマニフェストをまとめたIIIFコレクションのご紹介

Cultural Japanで集約したIIIFマニフェストをまとめたIIIFコレクションのご紹介

https://tech.ldas.jp/ja/posts/hatena-2021-07-23-003143/

#rdf #culturaljapan #iiif

Introduction to the IIIF Collection of IIIF Manifests Aggregated by Cultural Japan Introduction to the IIIF Collection of IIIF Manifests Aggregated by Cultural Japan

[EN] Introduction to the IIIF Collection of IIIF Manifests Aggregated by Cultural Japan

https://tech.ldas.jp/en/posts/hatena-2021-07-23-003143/

#rdf #culturaljapan #iiif

🚀 New releases from the Ontogen and RDF on Elixir ecosystem! 🧵

Two new projects and a major update to RDF.ex:

🔹 DCAT-R
🔹 Gno
🔹 RDF.ex 3.0

Many thanks to the NLnet foundation for the continued support! 🙏

ontogen.io/blog/dcat-r-...

#RDF #LinkedData #SemanticWeb #KnowledgeGraph #Elixir

The #Netflix and Apple #RDF train is busy trying to paint a picture the state of #F1 is fine. That what we see is fairy tales and hallucinations. When their arguments fail, they resort to making up things people say and making up what they're seeing. It's sad to see greed will ruin this sport.

My original Borderline Cases vinyl album by Radical Dance Faction or RDF, released in 1990

My original cassette tape album Wasteland by Radical Dance Faction or RDF, released in 1991

#DavidsMonthOfMusic

Day 17. Song by a band/artist whose name starts with R

Surplus People #RadicalDanceFaction

A #Punk #Dub #Ska band of the late 80s/mid 90s, I got into #RDF at Wolves University in 1992 & have 2 of their albums

m.youtube.com/watch?v=x0VD...

#MusicChallenge #90sMusic #80sMusic

SPARQL query of a local endpoint that uses http://schema.org vocabulary

SPARQL query of OSM Planet endpoint https://qlever.dev/api/osm-planet PREFIX geo: <http://www.opengis.net/ont/geosparql#> PREFIX osmkey: <https://www.openstreetmap.org/wiki/Key:> PREFIX ogc: <http://www.opengis.net/rdf#> PREFIX osmrel: <https://www.openstreetmap.org/relation/> SELECT ?osm_id ?hasgeometry WHERE { osmrel:1960198 ogc:sfContains ?osm_id . ?osm_id geo:hasGeometry/geo:asWKT ?hasgeometry . ?osm_id osmkey:building ?building . }

Messing about with #ClaudeCode, #RDF and #SPARQL, building an app that enables multiple queries in same window, geographic and network display, and exploring the "schema" implied by the entitity types and their connections. I want something like the apps we have to query SQL databases.

Original post on mastodon.social

RE: https://w3c.social/@w3c/116216070362563670

FWIW, I have been storing Linked Data (including ActivityPub) in an INI like format — because I find INI-like formats more human-friendly (to both read and write) than JSON.

YAML is probably better than JSON, too, in that respects. But I think […]

Rwanda🇷🇼 President Paul Kagame's government says it "regrets One-Sided Sanctions" after President Donald Trump's US 🇺🇸 government imposed sanctions against its military #RDF, and four top military officials over #M23 direct support.

US President Donald Trump's government has imposed sanctions against Rwanda's Defense Force #RDF and four top military officials "for their direct operational support to the March 23 Movement #M23 and its affiliates in eastern #DRC", a statement from the department of state reads.

Valueflows

Value Flows v1.0.0

by @lynnfoster & @bhaugen & associates.

https://www.valueflo.ws/

codeberg.org/valueflows/valueflows/re...

#ValueFlows #LinkedData #RDF #JSON-LD #creation #distribution #exchange #ressources #REA #economic #network #planning

Use cases: transmitter locating, noise hunting, wildlife/asset tracking, SAR beacons & fox hunts—guided by Android turn-by-turn. 🦊📱
Learn more🔗 l.lab401.com/mOEiY
#lab401 #pentesting #RDF #FoxHunt #KrakenSDR

2.6 RDF Blank Nodes RDF blank nodes in Turtle are expressed as _: followed by a blank node label which is a series of name characters. The characters in the label are built upon PN_CHARS_BASE, liberalized as follows: • The characters _ and digits may appear anywhere in a blank node label. • The character . may appear anywhere except the first or last character. • The characters -, U+00B7, U+0300 to U+036F and U+203F to U+2040 are permitted anywhere except the first character.

I suspect that there is an error in the Turtle specification, in the section shown in the screen-shot.

(It relates to JSON-LD, which ActivityPub / ActivityStreams is built on.)

I suspect that "PN_CHARS_BASE" is an error.

Because other parts of other […]

[Original post on mastodon.social]

Original post on fosstodon.org

I have been reading This is for everyone by @timbl.

I have taken lots of interesting quotes from the book and I post one or two every couple of weeks.

Now on XML and RDF and on public and open data:
ambience.sk/reading-this-is-for-ever...
#xml #rdf #timbl #internet […]

Bath Day
#beardeddragon #restingdragonface #rdf

GitHub - morph-kgc/morph-kgc: Powerful RDF Knowledge Graph Generation with RML Mappings Powerful RDF Knowledge Graph Generation with RML Mappings - morph-kgc/morph-kgc

morph-kgc to map #SQL, CSV, XML, JSON into #RDF triples to make a virtual #knowledgegraph.
Apache 2.0 lic.
#GraphRAG #RAG #Graph #OpenSource

github.com/morph-kgc/mo...

Senior Software developer About Us The Swiss Personalized Health Network (SPHN) is a national network mandated by the State Secretariat for Education, Research and Innovation (SERI) to enable the secure reuse of health data for research and innovation. Its mission is to ensure that clinical information collected across Swiss hospitals can be made FAIR (Findable, Accessible, Interoperable, and Reusable) and comparable across institutions. SPHN fosters interoperability at two complementary levels: Semantic interoperability through common data models, ontologies, and terminologies, enabling health data to be linked, queried, and understood consistently across institutions. Computational interoperability through BioMedIT, a secure federated IT infrastructure connecting Swiss hospitals and research institutions, providing trusted environments for privacy-preserving data transfer, storage, and analysis. To strengthen its semantic layer, SPHN is seeking a Senior Software Developer with strong expertise in Semantic Web technologies, particularly SPARQL, and a solid understanding of data pipeline infrastructures and architectures. The successful candidate will contribute to the design and implementation of solutions that put semantic interoperability into practice across the Swiss health data ecosystem.Responsibility/TaskContribute to the design and implementation of ontologies, data models, and APIs supporting semantic interoperability of health data. Develop and maintain semantic data services, with a focus on SPARQL query optimization and integration with RDF triple stores (e.g., GraphDB, Virtuoso).Build tools and workflows that support the alignment of health datasets with international medical informatics standards (FHIR, SNOMED CT, LOINC, HealthDCAT). Collaborate with clinicians, researchers, and IT specialists to translate semantic interoperability requirements into robust technical solutions. Lead the development and on-premises implementation of ETLs and software such as the SPHN Connector and Einstein, enabling secure and standardized data exploration across Switzerland.

There is a cool job open at the #SPHN #DCC lots of #RDF #FAIR and helping medical researchers. https://apply.refline.ch/499599/0353/pub/1/index.html

in the required format: Bitcoin: $84,649.43 drops by 5.2% due to market volatility.

#Crypto #PIMCO #RDF #DJT

Who says using RDF is hard? The Linked Data ecosystem and RDF as its graph data model have been around for many years already. Even though there is an increasing interest in knowledge graphs, many developers...

Who says using #RDF is hard?

#web #semanticweb #blogpost

www.rubensworks.net/blog/2019/10/06/using-rd...

OGRAG by #Microsoft
#RDF #GraphRAG with hypergraphs to incorporate #ontology grounded knowledge.
MIT license
#OpenSource #GraphDB #RAG #AI #LLM
Link in the first comment💬👇

RE: https://openbiblio.social/@awinkler/115982827467465444

from the link GitHub issue: "Here is a demo of QLever handling one trillion triples on a single commodity PC: https://qlever.dev/one-trillion . That is more than fifty times larger than the current Wikidata" #sparql #rdf #opensource

Announcing Ontology Support, Improved Data Model Visualization, Entra ID auth Support & More

Dive into this month's G.V() release: gdotv.com/blog/gdotv-r...

#RDF #ontologies #graphdatabases #dataviz

There are many benefits when the inference rules are included in the knowledge graph. One benefit is the decoupling of data and application. It lowers the cost of integration and the cost of change. Another is the improved data governance.

A third one is the gain in flexibility 👇📜

#RDF #SHACL

There are many benefits when the inference rules are included in the knowledge graph. One benefit is the decoupling of data and application. It lowers the cost of integration and the cost of change. Another is the improved data governance.

A third one is the gain in flexibility. 👇📜

#RDF #SHACL

Challenges with Semantic Mappings There are many challenges associated with the curation, publication, acquisition, and usage of semantic mappings. This post examines their philosophical, technical, and practical implications, highlights existing solutions, and describes opportunities for next steps for the community of curators, semantic engineers, software developers, and data scientists who make and use semantic mappings. ### Proliferation of Formats The first major challenge with semantic mappings is the variety of forms they can take. This both includes different data models and serializations of those models. Let’s start with a lightning review (please let me know if I missed something): Simple Knowledge Organization System (SKOS) is a data model for RDF to represent controlled vocabularies, taxonomies, dictionaries, thesauri, and other semantic artifacts. It defines several semantic mapping predicates including for broad matches, narrow matches, close matches, related matches, and exact matches. JSKOS (JSON for Knowledge Organization Systems), a JSON-based extension of the SKOS data model. I recently wrote a post about converting between SSSOM and JSKOS. Web Ontology Language (OWL) is primarily used for ontologies. It has first-class language support for encoding equivalences between classes, properties, or individuals. Other semantic mappings can be encoded as annotation properties on classes, properties, or individuals, e.g., using SKOS predicates. The OBO Flat File Format is a simplified version of OWL with macros most useful for curating biomedical ontologies. It has the same abilities as OWL, but also the `xref` macro which corresponds to `oboInOwl:hasDbXref` relations, which are by nature imprecise and therefore used in a variety of ways. The Simple Standard for Sharing Ontological Mappings (SSSOM) is a fit-for-purpose format for semantic mappings between classes, properties, or individuals. SSSOM guides curators towards inputting key metadata that are typically missing from other formalisms and is gaining wider community adoption. Importantly, SSSOM integrates into ontology curation workflows, especially for Ontology Development Kit (ODK) users. The Expressive and Declarative Ontology Alignment Language (EDOAL) lives in a similar space to SSSOM, but IMO was much less approachable (c.f. XML + Java), and has not seen a lot of traction in the biomedical space. OntoPortal has its own data model for semantic mappings that has low metadata precision. I recently wrote a post on converting OntoPortal to SSSOM. OntoPortal would also like to invest more in SSSOM infrastructure if it can organize funding and human resources. Wikidata has its own data model for semantic mappings that include higher precision metadata. I recently wrote a post on mapping between the data models from SSSOM and Wikidata. Finally, there’s a long tail of mappings that live in poorly annotated CSV, TSV, Excel, and other formats. Similarly, mappings can live in plain RDF files, e.g., encoded with SKOS predicates, but without high precision metadata. ### Scattered, Partially Overlapping, and Incomplete Semantic mappings are not centralized, meaning that multiple sources of semantic mappings often need to be integrated to map between two semantic spaces. Even then, these integrated mappings are often incomplete. Using Medical Subject Headings (MeSH) and the Human Phenotype Ontology (HPO) as an example, we can see the following: 1. MeSH doesn’t maintain any mappings to HPO. 2. HPO maintains some mappings as primary mappings. 3. The Unified Medical Language System (UMLS) maintains some mappings as secondary mappings. HPO suggests using UMLS as a supplementary mapping resource. 4. Biomappings maintains some community-curated mappings as secondary mappings. This actually might not be the best example - it would have been better to show a pair of resources that both partially map to the other. When I first made this chart, I had to engineer the UMLS inference by hand. Eventually, the need to generalize this workflow led to the development of the Semantic Mapping Reasoner and Assembler (SeMRA) Python package which does this automatically and at scale. The fact that there were missing mappings that even UMLS inference couldn’t retrieve led to establishing the Biomappings project for prediction and semi-automated curation of semantic mappings. The underlying technology stack from Biomappings eventually got spun out to SSSOM Curator and is now fully domain-agnostic. ### Different Precision or Conflicts Another challenge with semantic mappings is when different resources have different level of precision. In the example below, OrphaNet uses low-precision mapping predicates (i.e., `oboInOwl:hasDbXref`) while MONDO uses high-precision mapping predicates (i.e., `skos:exactMatch`). It makes sense to take the highest quality mapping in this situation, but having a coherent software stack to do this at scale was the big challenge (solved by SeMRA). This can get a bit dicier when there might be conflicting information, for example, if one resource says exact match and another says broader match. In SeMRA, I devised a confidence assessment scheme (which should get its own post later). ### Common Conflations There are three flavors of conflations that make curating and reviewing mappings difficult that I want to highlight. #### Different Ontology Encodings Classes, instances, and properties are mutually exclusive by design. This means that any semantic mappings between them are nonsense, but there are many situations where these mappings might get produced by an automated system or by a curator who is less knowledgable about the ontology aspect of semantic mappings. There’s also a much more subtle discussion about classes, instances, and metaclasses ( see this discussion) that I would set aside. As a concrete example, the Information Artifact Ontology (IAO) has a class that represents the section of a document that contains its abstract: abstract (IAO:0000315). Schema.org has an annotation property whose range is a creative work and whose domain is the text of the abstract itself: schema:abstract. These both have the same label `abstract`, which means that it’s possible to conflate (i.e., accidentally map them). #### Different Entity Types The second kind of conflation is even more subtle, when two classes, instances, or properties come from similar but distinct hierarchies. For example, there’s a subtle difference between what is a phenotype and what is a disease. Ontologies are highly apt at encoding this subtlety with _axioms_ that can then be used by reasoners. This can become a problem for curating and reviewing semantic mappings because some diseases are named after the phenotype that it presents or that causes it. Using MeSH’s disease hierarchy and HPO’s phenotype hierarchy as an example, we can see that Staghorn Calculi (mesh:D000069856) and Staghorn calculus (HP:0033591) should not get mapped. Many more examples can be produced (which also show there are even more subtleties here) using SSSOM Curator with the command: `sssom_curator predict lexical doid hp`. See the SSSOM Curator documentation for more information on the lexical matching workflow. #### Different Senses The basic formal ontology (BFO) is an upper-level ontology that is used by many ontologies, including almost the entire Open Biomedical Ontologies (OBO) Foundry. However, as Chris Mungall described in his blog post, Shadow Concepts Considered Harmful, there are many different senses in which an entity can be described, each falling under a different, mutually exclusive branch of BFO. The figure below, from Chris’s post, represents different senses in which a human heart can be described: This problem is particularly bad in disease modeling. Here are only a few examples (of many more) that illustrate this: * the Ontology for General Medical Science (OGMS) term for disease (OGMS:0000031), the Experimental Factor Ontology (EFO) term for disease (EFO:0000408), Monarch Disease Ontology (MONDO) term for disease (MONDO:0000001) is a disposition (BFO:0000016) * the Gender, Sex, and Sexual Orientation Ontology (GSSO) term for disease (GSSO:000486) is a process (BFO:0000015) * the Human Disease Ontology (DOID) informally mentions that a disease is a disposition, but doesn’t make an ontological commitment to BFO * many more controlled vocabularies including NCIT, SNOMED-CT, and MI have their own terms for diseases but don’t use BFO as an upper-level ontology nor are constructed in a way conducive towards integration with other ontologies Schultz _et al._ (2011) proposed a way to formalize the connections between the various senses for diseases in Scalable representations of diseases in biomedical ontologies. However, the OBO community has yet to resolve the long and taxing discussion on how to standardize disease modeling practices. For semantic mappings, this becomes a problem because a reasoner will explode if diseases under two different BFO branches get marked as equivalent, because the BFO upper level terms are marked as disjoint - this is a feature, not a bug. However, while useful for creating carefully constructed, logically (self-)consistent descriptions of diseases, these modeling choices can be confusing when curating or reviewing mappings. These modeling choices might not be so important in downstream applications, such as assembling a knowledge graph to support graph machine learning, where many different knowledge sources with lower levels of accuracy and precision must be merged. In practice, I have merged triples using conflicting senses for diseases in a useful way, without issue. ### Interpretation is Important While the last few examples were cautionary tales for when things (probably) shouldn’t be mapped, the next examples are about when things (probably) should be mapped. #### Definitions Here are three vocabularies’ terms for proteins and their textual definitions (though, many more contain their own term for proteins): Entity | Label | Description ---|---|--- wikidata:Q8054 | protein | biomolecule or biomolecule complex largely consisting of chains of amino acid residues SIO:010043 | protein | A protein is an organic polymer that is composed of one or more linear polymers of amino acids. PR:000000001 | protein | An amino acid chain that is canonically produced _de novo_ by ribosome-mediated translation of a genetically-encoded mRNA, and any derivatives thereof. As semantic mapping curator, we have two options: 1. We can reasonably assume that the intent from all three resources was to represent the same thing, despite the definitions being quite different. This assumption can be built on our prior knowledge about what a protein is, why Wikidata, SIO, and PR exist, and then infer the intent of the term’s definition’s author 2. We can make a very literal reading of the definition and conclude that these three terms represent very different things I think that the latter is really unconstructive for several reasons, but I have worked with colleagues, especially from the linguistics background, who take this approach. First, this is unconstructive because it means you’ll probably never map anything. Second, if you want to be rigorous, use an ontology formalism with proper logical definitions. For example, the Cell Ontology (CL) exhaustively defines its cells using appropriate logical axioms. However, this also has a caveat, that to make mappings based on logical definitions, then the different modelers have to agree on the same axioms and same modeling paradigm. As far as I know, there aren’t any groups out there that use the same modeling paradigm that haven’t just combine forces to work on the same resource. So we’re stuck back at option 1 either way :) #### Context Sometimes Matters In contrast to the discussion about mapping phenotypes and diseases, there are context-dependent reasons to make semantic mappings, which can be illustrated in biomedicine using genes and proteins. Let’s start with some definitions: 1. SO:0000704 A gene is a region of a chromosome that encodes a transcript 2. PR:000000001 A protein is a chain of amino acids The biomedical literature often uses gene symbols to discuss the proteins they encode. While this isn’t precise, it’s still useful in many cases. Therefore, when reading the COVID-19 literature, you will likely see discussion of the IL6-STAT cascade, where IL6 is the HGNC gene symbol for the Interleukin 6 protein. Most of the time, the HGNC approved gene symbol is an initialism or other abbreviation of the protein, but this isn’t always the case. Similar to the literature, many pathway databases that accumulate knowledge about the processes and reactions in which proteins take part actually use gene symbols (or other gene identifiers) to curate proteins. The take-home message here is that genes and proteins are indeed not the same thing, but in some contexts, it’s useful to map between them. There’s also a compromise - the Relation Ontology (RO) has a predicate has gene product (RO:0002205) that explicitly models the relationship between IL6 and Interleukin 6, which can then be automatically inferred to mean a less precise mapping for certain scenarios (SeMRA implements this). Outside of biomedicine, I have also heard that context-specific mappings are very important in the digital humanities. As I’m better understanding the use cases of colleagues in other NFDI Consortia that focus on the digital humanities, I will try and update this section to have alternate perspectives. ### Evidence A key challenged that motivated the development of SSSOM as a standard was to associate high-quality metadata with semantic mappings, such as the reason the mapping was produced (e.g., manual curation, lexical matching, structural matching), who produced it (e.g., a person, algorithm, agent), when, how, and more. We developed the Semantic Mapping Vocabulary (semapv) to encode different kinds of evidence such as for manual curation of mappings, lexical matching, structural matching, and others. SSSOM is well-suited towards capturing simple evidences (blue). #### Provenance for Inferences The purple evidence from the figure in the last section requires a more detailed data model to represent provenance for inferred semantic mappings that simply doesn’t fit in the SSSOM paradigm (and it shouldn’t be hacked in, either). I proposed a more detailed data model for capturing how inference is done in Assembly and reasoning over semantic mappings at scale for biomedical data integration and provided a reference implementation in the Semantic Reasoning Mapper and Reasoner (SeMRA) Python software package. Here’s what that data model looks like, which also has a Neo4j counterpart: ### Negative Semantic Mappings SSSOM also has first-class support for encoding _negative_ relationships, meaning that the following can be represented: This means that SSSOM curators can keep track of non-trivial negative mappings, e.g., when curating the results of semantic mapping prediction or automated inference. In a semi-automated curation loop, this allows us to avoid re-reviewing zombie mappings over and over again. High quality, non-trivial negative mappings also enable more accurate machine learning, as opposed to using negative sampling. For example, we have been working on developing graph machine learning-based ontology matching and merging using PyKEEN (a graph machine learning package I helped develop and maintain). An open challenge is that we neither have support from data modeling formalisms (e.g., ontologies in OWL, knowledge graphs in RDF or Neo4j) to encode negative knowledge (in this case negative mappings) nor tooling support. This means that when we output SSSOM to RDF, we use our own formalism, which won’t be correctly recognized by any other tooling that wasn’t developed with SSSOM in mind. I’m keeping notes about this in a separate post about negative knowledge that I update periodically. * * * Despite the challenges, I think that the mapping world is actually getting quite mature. I am currently working with NFDI and RDA colleagues to further unify the SSSOM and JSKOS worlds, especially given that the Cocoda mapping curation tool solved many of these problems (from the digital humanities perspective) many years ago, and we simply were unaware of it. I hope this post can continue as a living document - if I missed something, please let me know and I will update the post to include it!

i wrote about some of the challenges with semantic mappings 🗺️

cthoyt.com/2026/01/20/semantic-mapp...

#ontology #owl #rdf #sssom #semanticweb #knowledgegraphs

[Proposal] Protocol-agnostic PDS Hi folks, Dorian here. Some of you may know me, and an even smaller subset may know that I’m working on an application server called Intertwingler, whose purpose is to create a durable substrate for ...

as such i'm keenly interested in seeing if #atproto #lexicon can be represented in #RDF to take advantage of this capability, as well as define a mapping that will make it possible to use existing RDF vocabs in atproto

that's what this thread is about: discourse.atprotocol.community/t/proposal-p...

in may (?) this will be my 20th year working with #RDF

RDF unambiguously has degrees of freedom that take getting used to

it also constraints that take getting used to

there are also benefits that only RDF can confer, which is why i have stuck with it for two decades

Kigali stand-off continues as Copperbelt booms The lack of enforcement mechanisms means no one is pressured to respect the US and Qatari accords

A #UN report suggests that the #M23militia, with support from the #RDF, is seeking ‘to position itself as an alternative governance authority to the Congolese state’. Those aims are unlikely to change in 2026. #DRC

africa-conf.com/kigali-stand...

aéPiot and the Future of Enterprise SaaS: Blueprint for Monetizing a 15.3M Organically-Acquired User Base. A Comprehensive Strategic Analysis of Freemium-to-Enterprise Transition. aéPiot and the Future of Enterprise SaaS: Blueprint for Monetizing a 15.3M Organically-Acquired User Base A Comprehensive Strategic Analy...

better-experience.blogspot.com/2026/01/aepi...

#RDF #QUERY #LANGUAGE
headlines-world.com/advanced-sea...
#ABSINT
multi-search-tag-explorer.allgraph.ro/advanced-sea...
#FUTURE #IRANIAN #GOVERNMENT
multi-search-tag-explorer.allgraph.ro/advanced-sea...
allgraph.ro

HyperMind is a #SymbolicAI reasoning stack & #agents with #KnowledgeGraph & build-in #RDF GraphDB. Transforms questions into verified, traceable answers.

#SPARQL 1.1, #Datalog rules, #OWL2 inference, GraphFrame analytics, & motif detection—unified interface.
#AI #OpenSource
Link in the 💬👇