The handful of tools I use for development around the semantic web stack (RDF/SPARQL).

https://github.com/justin2004/weblog/tree/master/iDE#readme

Hey all, looking for some quick guide on practical ontology development. I'm familiar with Semantic Web and have worked already a little with ontology management, SHACL, and some integrations in OWL.

But I would like to revisit some core concepts over the weekend, in modelling and in assessing existing ontologies. I was thinking of starting with Proteges Ontology Development 101, but wondering if anyone has any other useful resource on this.

Thanks in advance!

I am a searching about the semantic web and ontologies and reading about how it can help to integrante data but it was imposible to find some source with a simple example about it.

How can I use it? Let's say I have two systems with data that have some common points. How can I make something like a SPARQL endpoint to query it? Wichtig programming language is the best for the task? Where do I deploy it?

Hello! I have a semantic web project for college that I need help with. Could anyone assist me so I can pass my exam?

Hi everyone! I have recently started getting into RDF and SPARQL for a university class and I am currently in the process of trying to make some simple queries to test/improve my knowledge. I decided to write a query to get a list of all Pokemon from the first generation from DBpedia, which looks like this:

PREFIX dbo: http://dbpedia.org/ontology/

PREFIX dbp: http://dbpedia.org/property/

PREFIX dbr: http://dbpedia.org/resource/

SELECT DISTINCT ?pokemonName

WHERE {

?pokemon dbo:wikiPageWikiLink dbr:List_of_Generation_I_Pokémon .

?pokemon dbp:name ?pokemonName .

FILTER(lang(?pokemonName) = 'en')

}

The query, however, returns an empty table. I have been trying to get it to work for the past 2 hours, even asking ChatGPT to "fix" my query, but I have been completely unable to do so. I would appreciate it if someone who is more experienced with SPARQL/DBpedia could tell me what I am doing wrong.

I have been using Protege for building an ontology for my research study but I added a lot of instances so it took considerable amount of time when I was executing SPARQL queries. So, I moved to GraphDB but apparently its reasoner is different and might not give new insights like HermiT does? I have been suggested to move to Stardog now but just wanted to ask this community, what really is the difference between all these reasoners and is HermiT really the best?

I recently came across a platform called sCompute that I thought might be of interest to the semantic web community. sCompute is a decentralized marketplace that connects data providers with data consumers, enabling the exchange of high-quality datasets across various domains.

What caught my attention is the potential for sCompute to facilitate semantic data integration and enhance machine learning applications. I wrote an article exploring these aspects:

• Decentralized approach to data sharing and monetization
• Emphasis on data quality, integrity, and ethical sourcing
• Potential for integrating semantic web technologies to improve data discoverability and interoperability
• Implications for building more accurate and reliable ML models using semantically enriched data

While the article primarily focuses on the ML applications of sCompute, I believe the platform's decentralized nature and focus on data quality align well with the principles of the semantic web.

Article link

I'm curious to hear your thoughts on the potential synergies between platforms like sCompute and semantic web technologies. How could semantic web standards and practices be incorporated into decentralized data marketplaces to enhance data integration and discoverability? What role could semantically enriched datasets play in improving the performance of ML models?

I'd love to start a discussion on how we can leverage the strengths of both decentralized data sharing and semantic web technologies to create more powerful and interoperable data ecosystems.

Please share your insights and experiences related to semantic data integration and its applications in machine learning and beyond.

Hi, please I have just started with it, so maybe it exists and I cannot find it.

What I want to do are filter with "turtle" input, something like:

Set<Statement> s1;

Set<Statement> s2 = Filter.filterByPredicate(s1, "my:predicateA");

or:

Set<Statement> s2 = Filter.filterByObject(s1, "my:objectB");

does something like that exists? .. or, do people do this at all?

Thank you

Dear fellow Redditors,

What work has been done on constructing a universal semantic dictionary of sorts that has an entry for every unique concept that humans are capable of conceiving of? I'm referring to a list in which no two entries would have the exact same meaning. I also wonder if you could possibly decompose each entry into a list of +/- parameters (e.g. woman = +noun +female +adult +human), although I assume this would be more difficult or even impossible for abstract terms.

In any case, can someone kindly give me some information about what kind of semantic project or line of inquiry deals with such a thing?

Thanks!

I am currently working for my master thesis with topic on ontology alignment.

I have developed a tool to be used for the alignment of ontologies. However I lack from available datasets that contain ground truth, for example a reference.rdf that contains the possible matchings between classes.

Are there any available online? I have tried from the OAEI but still, are there any available from somewhere else?

Thank you in advance!

I'm working on a project to catalogue events and actors of a certain political uprising, along with [digital] artefacts thereof.

I've been reviewing the literature for suitable vocabularies/ontologies to use for capturing events and actors, and I find the POWER and HISTO ontologies to be good candidates, but I cannot find an RDFS representation of either of them. I wrote to the respective researches, but I don't expect replies in a reasonable time, if at all.

My questions:

1. Does anyone know where I can find such RDFSs?
2. Any recommendations for other candidates?

Hi, the sub academiclibrarians is not functional, so may I ask here (where else, not sure).

We have created (what I think today) an ontology. Purpose is: to collect all perceived knowledge of a not-yet entirely empirically researched area. In the form of ttl with self-created relations.

It's intended to give an overview about the field, to see whether the concepts are meaningfully connected (some hierarchical, a lot increasing/inhibiting). / So, that is to: talk about with other people about it, improve it. It is in the sense of knowledge management, describing a knowledge area.

also, there are of course resources. So why not take this ontology and index them, after the labels of the ontology.

.. Is that done? Or may we create a thesaurus out of the ontology, that meant: transform all relations that are not-associative (and not hierarchical), basically all our own definitions, into associative ones?

Thank you

This is a replacement post.

What improvements would you like to see in SHACL? In general and considering RDF* & SPARQL*.

TIA

I am looking to import Dublin Core into Protégé, but Dublin Core has exploded from the 15 terms into this huge (though seemingly very useful) collection of entities. It would be GREAT if I could use the definitions in Dublin Core for further developing my ontologies with the predefined annotations, but I can't figure out where the RDF files actually are on dublincore.org, how many of them there are (e.g. dc, dcterms, dcmitype), etc.

Does anyone know where those files might be so that I can import them locally and see all their definitions within the editor?

(Bonus: anyone know where a set of MIME type definitions might be in RDF?)

Please forgive the total noob question. After years looking for tools to help me what I have always thought of as Hierarchical data management. I speculatively searched for Taxonomy, found Protege and now my head is spinning at the sheer amount of cool stuff I didn't know existed.

Before I fully go down this rabbit hole though I was hoping to get a steer from you all as to whether I am headed in the right direction.

My principle challenge is the management of multiple interconnecting Hierarchies and a collection of 'tags' that map a given record to those Hierarchies.

For example I have a record that has 3 attributes which are used to map to a key that in turn is the lowest level of grain of a seperate hierarchy. In my case three values relating to the description of a healthcare service that results in a key that links to a geographical hierarchy of where those services are delivered. There are a lot of these mappings and several people contribute to the definitions of the mapping and hierarchy so being a able to validate and track is important.

From what I can see this looks like something achievable in tools like Protege but I wasn't sure I can see that I could define for example Countries and Counties and express them as related concepts but could not see if I could limit counties to their relevant countries. I also envisage a challenge of having to merge Hierarchies that are similar but different reflecting differences in how organisations are structured from a delivery, finance and workforce perspective.

I hope that makes sense apologies if not, I only found out this all exists 24 hours ago and have been managing with bespoke development for years. If I have hit on the right tools for this kind of work I will dig in.

What are people's interpretation of https://www.w3.org/TR/rdf-schema/#ch_domainrange ?

I feel like most literature only mentions the inferencing implication.

I need to do semantic searches on a large dataset (millions of texts), where each text is max 250 characters long. This will be on the backend of a web application. For example, look at how Stackoverflow pulls up suggestions of existing similar topics when you enter the title of a new topic.

What system would you suggest for this? Not looking to create my own, but to use an existing system.

I've seen that MySQL can be enhanced with vector embeddings, and that MSSQL offers a semantic search option. I'm new to semantic search, so I have no idea if these are good or not.

Hello everyone,

I'm new to semantic languages and currently I'm testing different SPARQL queries to learn more. Recently, while experimenting with querying, I stumbled upon an intriguing observation that I'd like to discuss and seek insights into.

​

In the results of my SPARQL queries, I noticed there are properties used to describe similar concepts across different resources. Specifically, there are instances where properties with identical or similar meanings have different labels, both within the same resource and across different resources.

​

Let me illustrate this with an example:

Within a single resource representing a building, there might be two properties indicating the architect responsible for its design, labeled as [dbo:architect] and [dbp:architect]. Additionally, when comparing multiple resources representing different buildings, a third property such as [dbp:otherDesigners] might emerge. In all cases, the value is the same.

​

My questions are why does this variation in property labels occur, and is there a way to standardize and enhance the representation of these properties across different resources?

​

I'd love to hear your thoughts and insights on this topic.

x-posted in r/librarians

I'm close to graduating from library school and since I've been interested in linked data/RDF/SPARQL stuff lately, I thought it might be cool to have a project related to it in my portfolio. The problem I'm running into is when I search for this on Google, I get a lot of Data Science portfolio tips articles but nothing library-related or even primarily metadata-focused.

I have a metadata application profile I created for a class to put on my portfolio, but I'd like to show my coding ability (e.g. XML), too. I just have no clue how to come up with a good sample code project for linked data! Any tips?

Hey everyone,

I'm currently working on an exercise involving SPARQL queries in the DBpedia SPARQL Query Editor, and I'm a bit stuck. The task is to write a query that returns the URIs of cultural content objects that meet specific criteria:

1. They must be of the "Historic Building" type (dbo:HistoricBuilding).
2. They must also be of type "Architectural Structure" (dbo:ArchitecturalStructure).
3. Additionally, their architectural style should be "Classical Architecture" (dbr:Classical_architecture).

I've written a query, but I'm not entirely confident if it's correct.

Could someone please review this query and let me know if it's accurate? Or if there's any room for improvement? I'd greatly appreciate any help or suggestions!

Thanks in advance!

Here's what I have:

SELECT ?cultureURI

WHERE {

?cultureURI rdf:type dbo:HistoricBuilding ;

rdf:type dbo:ArchitecturalStructure ;

dbo:architecturalStyle dbr:Classical_architecture .

}

​

[edited the query because I pasted the wrong text]

Hi all, I try to get data. Yes I also try to get them from wikidata, and probably will generate my own. However, the WarSampo project is what I would like to "have", but for Center and Western Europe. Do you know about similar projects, that are also ok to use? - I was looking through the academic literature but could not find such. Thank you

Is there an existing way to say a web page or product is about sewing thread, a home automation standard, or a programming concept?

Similarly is there a way to say a link or discussion is about a Python module ‘Ice Cream’ and not a dairy product?

And do any search engines today have the capacity to do anything with such expressions?

Hi everyone! I'm interested in understanding how statistical analysis can be applied to knowledge graphs before using embedding techniques for entity alignment. Are there any specific statistical features or methods that have shown promising results? I'm particularly interested in capturing semantic context through statistical analysis, and how this context can be leveraged to improve the representation of entities in knowledge graphs before entity alignment. Any recommended research papers or projects in this area would be greatly appreciated. Thanks!

Hi, please,

we did a database in the form of semantics (turtle format) for the reason that it is very high interlinked (question here: is there another more suitable format to represent highly interlinked data?)

.. so I am doing sparql-queries onto them (with Jena arq.sh) and it works, great.

My question: it is not web-related anyhow. there is not any URL (only to use the pre-defined schemas of skos, rdfs, dc .. etc. but even then, only shortened)

.. i am kind of super-super-super annoyed for just any comparison (=, !=) where the result results into a string .. or not ... (then I have to stringify it with str() ..) having to: regex the results .. several times ..

(maybe the case of local storage without web link is not the usual case? however I have seen _a lot_ of triple stores without that?)

question: how can I just keep the sparql engine working with only the short-codes. not any url.

is there a "switch"? .. and if not, where can I add a rec/bug-report .. whatever to the sparkle-w3c ? (or better, add my comment to something that was seemingly considered useless for that there is a voice for its usefulness)? / thank you

Hey feel free to remove if its not relevant. I was given a pretty ambiguous task a few weeks ago. It was to create a data model for a recommender system that is going to be developed soon. Most of my data modeling experience is with dashboards and relational databases, but I realized pretty quickly that the recommender software needs a lot of very specific domain knowledge. I started looking at OWL, SCHACL, and SWRL to capture the complexity of the data needed for the inference engine. From my understanding OWL can structure the relationships/properties of data entities, SCHACL can be used for ensuring data integrity and SWRL for the actual inference rules. My question is, if I use a library such as owlready2 or rdflib, could I theoretically just ignore SWRL and use python functions instead, or does this defeat the point of creating a data model that others can use?

​

Hi,

maybe please you know that repo. Some weeks ago I was looking for ideas how to keep track of changes of a rdf (turtle) file. - An idea was to keep track of the changes.

Idea was: keep each entire file of a date (or date-time) _without_ annotation about modification. Then compare the last 2 files, and as a result, a change history was created (in HTML, I think)

.. I cannot find that repo any more. Does anybody of you maybe know what I am thinking of?

Thank you very much!

​

Edit: found what I was looking for. It is Joachim Neuberts skos-history repo https://github.com/jneubert/skos-history and the idea explained here https://www.dublincore.org/webinars/2016/skos_in_two_parts_-_part_1_change_tracking_in_knowledge_organization_systems_with_skos-history/slides.pdf, basically tracking the deltas (semantic differences) between 2 files. - Output formatted as HTML.

.. finding the deltas by doing a query for each property .. hell, such, he is doing this for the prefLabel and altLabel only.

​

Edit2: I honestly did not consider that a difficult topic but it is, entirely. well say I am surprised.