18 ms·

Apache Jena

140 points by pplonski86 8y ago

th0ma5 8y ago
Big fan of Jena. The command line tools alone for running SPARQL queries and converting between formats is great. Can anyone compare and contrast Jena in a Java project and/or operation against flat files with Neo4J or other graph systems?
- karimtr 8y ago
  Jena is an RDF triple store whereas Neo4J, Memgraph, or JanusGraph, for example, are property label graphs. Two different data models with distinct use-cases. I would compare Jena to other RDF engines like Stardog or Anzo graph. From a java project perspective, it doesn't really matter which one you choose as they all expose JDBC.
- eecc 8y ago
  What do you think of http://rdf4j.org http://rdf4j.org ?
  - karimtr 8y ago
    Interesting. I'll have a look. Thanks for sharing :D
MrBuddyCasino 8y ago
A blast from the past. Semantik Web used be a good buzzword to receive EU research grants, and RDF tripel have been useful in some areas. What else remains?
- EngineerBetter 8y ago
  Ha, my sole experience of RDF was on an EU-funded research project :)
  - maayank 8y ago
    Same here! :)
    - NewsAware 8y ago
      Mine on a non-funded research project. Must have done something wrong...
- salex89 8y ago
  I chuckled on the EU research grants part. My God, when I remember the monstrosities I was thinking up just to bodge in Semantic Web/Linked Data. Those guys are purely fueled by buzzwords. Maybe we met somewhere :) . "Semantic data is the future and always will be" - unknown author.
  - CeiII 8y ago
    "Semantic data is the future and always will be" - unknown author." - Pretty sure it was Peter Norvig
- kodz4 8y ago
  What do you think Google, Facebook, Siri and Alexa use for their underlying knowledge graphs?
  - pjc50 8y ago
    They have knowledge graphs? I thought it was all machine learning now. (That was a glib comment, but could we be a bit more specific about what's being repreented with RDF and other technologies, and for what purposes it's used and how?)
    - kodz4 8y ago
      Ya right machine learning. Machine learning over what? Comments on the internet? Donald Trump would automatically be classified as an Orange. The knowledge graph is a massive manual effort of encoding triples - https://en.wikipedia.org/wiki/Freebase https://en.wikipedia.org/wiki/Freebase.
      - mcguire 8y ago
        A dead effort?
      - riku_iki 8y ago
        Freebase wasn't built on RDF though, but had all it's own formats and query language.
        kodz4 8y ago
        https://developers.google.com/freebase/#freebase-rdf-dumps https://developers.google.com/freebase/#freebase-rdf-dumps https://arxiv.org/abs/1712.08707 https://arxiv.org/abs/1712.08707
        riku_iki 8y ago
        The fact that they exported their data in form of RDF for other's convenience doesn't mean they built it on RDF.
- nbaksalyar 8y ago
  > What else remains? SPARQL. It's useful beyond Semantic Web, as evidenced by the raise of projects such as GraphQL. It can replace proprietary REST APIs and make the Web more connected and open. For e.g. see this short talk by Ruben Verborgh [1] and projects like Solid [2]. [1] https://youtu.be/LUF7plExdv8 https://youtu.be/LUF7plExdv8 [2] https://solid.mit.edu/ https://solid.mit.edu/
- ungzd 8y ago
  Wikidata is built mostly on RDF ideas. For example, there's SPARQL query interface for it: https://query.wikidata.org/ https://query.wikidata.org/ Schema.org is heavily used by search engines and metadata in its format is found on many websites. It's an RDF-based format.
azatris 8y ago
What even is Semantic Web in modern terms?
- joshuabezaleel 8y ago
  Knowledge Graph.
  - diegoperini 8y ago
    And what is that exactly?
- jimmy_ruska 8y ago
  Things like json-ld, microdata, rdfa https://developers.google.com/search/docs/data-types/product https://developers.google.com/search/docs/data-types/product Wikidata also has data in rdf https://wikidata.org/wiki/Wikidata:Database_download#JSON_dumps_(recommended) https://wikidata.org/wiki/Wikidata:Database_download#JSON_du... I wonder what is the killer feature to be had. If you want highly connected data, you can use graph databases like neptune, neo4j, datomic. If you want logic programming, you still have swi-prolog, or something like picat, eclipse, mercury, which can easily model triples or custom ontology. There's also apache tinkerpop and similar which give querying a more object oriented feel. I see prolog can interop with Jena, but if it can, why not parse & query rdf/owl in prolog itself. Can't prolog do everything sparql can. What is the value offering on apache Jena?
  - solidasparagus 8y ago
    Not 100% certain, but I think the killer feature is the ability to manage your own data while exposing semantic representations that external apps can interact with. The guiding idea behind RDF was (at one point) that users manage their own data while apps they give permission to can use that data.
  - Boulth 8y ago
    Ha, yes, JSON-LD is RDF in JSON but no one admits that so it doesn't look ancient.
narrator 8y ago
I think the semantic web never worked because of seo spam. The closest it got to adoption in any form was the keywords meta tag. We know how that ended up.
- acdha 8y ago
  Less that and more the combination of bad tools, labyrinthine “standards” which were usually poorly implemented by said tools, and lack of concrete use-cases. Almost nobody had a case where implementing it showed a clear business value so most of the “you should do this” advocacy was along the lines of “this will become useful later” and that was a hard sell because it took many years before you could go to a webpage, read some decent instructions, mark something up, and run it through a validator or another client and get the expected data back out.
  - namedgraph 8y ago
    That is exactly what is happening: people and companies marking up their pages with JSON-LD and RDFa metadata because it impacts SEO: https://developers.google.com/search/docs/guides/intro-structured-data https://developers.google.com/search/docs/guides/intro-struc...
    - acdha 8y ago
      That was my point: to the extent that the semantic web has happened at all, it's because someone can go to their boss and say “If we do this, here's a non-hypothetical benefit we'll see immediately” and because Google invested in high-quality documentation and tools which make it easy to do correctly.
martinsbalodis 8y ago
How is semantic web data being used today? Has there been any research to build a general AI using this?
mark_l_watson 8y ago
I have been a fan of RDF/RDFS/OWL for a long time. I started adding RDF in various forms to my main web site shortly after TBL, et al wrote the original Scientific American article. I have also written two semantic web/linked data books. The uptake for the semantic web has been spotty. I was hired as a contractor at Google to work with the Knowledge Graph and over the years I have had a lot of consulting work in related areas. That said, if I were building a custom Knowledge Graph for a company or organization today, I would likely use a graph database like Neo4J. Maybe not though - it would depend on the application.
- blablabla123 8y ago
  Really? Why not Jena or Marmotta with Postgres backend? I have little experience but just started setting up the latter, therefore I'm curious.
  - zwifi 8y ago
    I can't speak for OP, but in my humble opinion, they don't really serve the same purpose. Jena and Marmotta are implementation of standards, while Neo4J is more of a proprietary system, and the underlying paradigm is different between triple graphs (for Jena and in a lesser dimension Marmotta) and property graphs (for most graph databases, e.g Neo4J, Apache Tinkerpop, OrientDB...). To make a very short summary, RDF is more concerned with the possibility to link data, so each piece of information is identified by a dereferencable URI, and can be described with an explicit model called an ontology (fancy word for a vocabulary used to describe data). On the other hand, Neo4J is more concerned with performance, but does not consider linking data accross the Web, or using an explicit schema. And for Marmotta, it is in kind of a bad place right now, the development seems a bit stalled, and the standard it implements is quite complicated compared to the majority of the problem it solves. This might evolve however, since Linked Data Platform (said standard) is now promoted by SOLID (https://solid.inrupt.com/ https://solid.inrupt.com/), a new initiative by Tim Berners Lee et al. to enable a truly distributed Web.
    - solidasparagus 8y ago
ragerino 8y ago
I prefer rdf4j as Rdf-Api for clients and GraphDB from Ontotext as RDF store. I never liked programming with Jena.
anon1253 8y ago
A lot of "blast from the past" comments! But as a counter example, we still use Jena extensively (in combination with its server Fuseki) to deal with biomedical ontologies and taxonomies for use in Natural Language Processing (e.g. Human Phenotype Ontology, Gene Ontology, etc). We even recently made the step to add some PROLOG-ish inference rules [1]. I have nothing but love for the RDF ecosystem, in the sense that I love their ideas even if some of the implementations are a bit wonky. For example, the performance of property paths (i.e. the + and * operators in sparql) leave things to be desired sometimes. Not to mention the funny looks from some devs when you say you use RDF, but I take that as a badge of honor! It took me a while to get it, so I finally decided to write down what it all means a couple of years ago [2]. [1]: https://jena.apache.org/documentation/inference/index.html https://jena.apache.org/documentation/inference/index.html [2]: https://joelkuiper.eu/semantic-web https://joelkuiper.eu/semantic-web
dekken_ 8y ago
Wow what a waste of time.
tqkxzugoaupvwqr 8y ago
What is the origin of the name / What does it mean? Couldn’t find an answer on the website.
- evanb 8y ago
  Jena is a city in the eastern part of Germany. It is historically famous for its university and optics innovation, and as an epicenter for bauhaus architecture.
  - la_fayette 8y ago
    That is why people from AKSW Leipzig built a similar thing for PHP named Erfurt (https://github.com/AKSW/Erfurt https://github.com/AKSW/Erfurt) for their semantic wiki OntoWiki (https://github.com/AKSW/OntoWiki https://github.com/AKSW/OntoWiki).
- bwestergard 8y ago
  Probably a reference to Frege, a founder of modern logic. https://en.wikipedia.org/wiki/Gottlob_Frege https://en.wikipedia.org/wiki/Gottlob_Frege
pragmaticlurker 8y ago
why Java?
- karimtr 8y ago
  Easier to build something quicky but definitely not the best option for DBs. It's always about tradeoffs I guess.
  - pragmaticlurker 8y ago
    for quickly, I would have chosen a dynamic language (python, php)
- namedgraph 8y ago
  because it is an enterprise language? Jena originates from Hewlett-Packard labs.
openasocket 8y ago
I actually stumbled across this a couple weeks ago, while thinking about the design for a project. I'm working with threat intelligence/security data and I need a nice way for users to save/share that data with the team, as well as query it in a simple fashion. People are pushing for a graph database. So I did some research and learned about RDF, OWL, Jena, etc. I'm still not sure if this is the right road to go down. Just from reading papers it's hard to tell how much of this is hype and over-engineering and how much is solid. Anyone have some RDF/OWL/Jens/SPARQL stories they want to share?
- oever 8y ago
  EU is developing a vocabulary editor called VocBench [0]. UK is making something similar: registry-core [1]. Here in the Netherlands there an active group exchanging RDF user stories [2]. To get a good start with RDF etc, I recommend 'Semantic Web for the Working Ontologist' (second edition or later!) [3]. It explains how inferencing works by doing inferencing with SPARQL queries. RDF is a powerful technology, but takes time to get acquainted with. What I dislike is that there's not many libraries (that I know) for working with it. I started a Rust library (Rome) for working with Turtle files and would like to have time to make it into a SPARQL engine. [0] http://vocbench.uniroma2.it/ http://vocbench.uniroma2.it/ [1] https://github.com/UKGovLD/registry-core https://github.com/UKGovLD/registry-core [2] http://www.pilod.nl/wiki/Platform_Linked_Data_Nederland http://www.pilod.nl/wiki/Platform_Linked_Data_Nederland [3] http://workingontologist.org/ http://workingontologist.org/
- th0ma5 8y ago
  I had a project to map out a whole bunch of relationships between various software configuration parameters of a vendor product. This was a lot of XML but also some CSV and JSON data. There's probably a ton of ways to do it, but I just spent some time extracting "facts" from this data into RDF. I didn't know what any of it was or how it all fit together but there were a lot of UUIDs and such. Once I had "everything" in RDF, using SPARQL queries to nose around was a really great way to find arbitrary relationships. I then used GraphViz to map out everything and it was very helpful to see how everything fit together. Could've probably used a regular database, or a key value store, but working with just flat files transformations at the command line was nice. I've also since used it manage a knowledge base and generate documentation and presentations. I haven't even used much of OWL or higher levels of abstraction yet, but just from a hack up something standpoint I think it is a pretty nice set of ideas to get basic graph operations just about anywhere you may need it, in the shell, in the browser, or in a backend project. Large sets of data may require something else, but anything less than 1g of data is rather performant IMHO.
andrewstellman 8y ago
I'm seeing several people asking what RDF is useful for. If you're curious, I use it for basketball analytics: https://github.com/andrewstellman/pbprdf https://github.com/andrewstellman/pbprdf Here's an article about my system, pbprdf: https://www.zdnet.com/article/nba-analytics-and-rdf-graphs-game-data-and-metadata-evolution-and-occams-razor/ https://www.zdnet.com/article/nba-analytics-and-rdf-graphs-g... And an example of its use: https://gist.github.com/andrewstellman/4872dbb9dc7593e56abddbe8b998b509 https://gist.github.com/andrewstellman/4872dbb9dc7593e56abdd... Here's an example of what the RDF files generated by pbprdf look like: Here's the ontology, which defines the vocabulary it uses: https://github.com/andrewstellman/pbprdf/blob/master/generated/ontology.ttl https://github.com/andrewstellman/pbprdf/blob/master/generat... And this is what the data looks like: <pbprdf/games/2017-11-29_Warriors_at_Lakers/230> pbprdf:shotPoints "3"^^xsd:int ; pbprdf:shotAssistedBy <pbprdf/players/Klay_Thompson> ; pbprdf:shotType "26-foot three point jumper" ; pbprdf:shotMade "true"^^xsd:boolean ; a pbprdf:Shot ; pbprdf:shotBy <pbprdf/players/Stephen_Curry> ; a pbprdf:Play ; pbprdf:forTeam <pbprdf/teams/Warriors> ; pbprdf:inGame <pbprdf/games/2017-11-29_Warriors_at_Lakers> ; pbprdf:time "10:23" ; pbprdf:period "3"^^xsd:int ; a pbprdf:Event ; rdfs:label "Warriors: Stephen Curry makes 26-foot three point jumper (Klay Thompson assists)" ; pbprdf:secondsIntoGame "1537"^^xsd:int ; pbprdf:secondsLeftInPeriod "623"^^xsd:int .
- simo-climb 8y ago
  Hi Andrew Thanks for posting your project in full. Too much of RDF/linked data is in the abstract, to big to see the moving parts, or behind propriety doors. I'm at the beginning of the learning curve so it's much appreciated and quite a number of things about the data workflow clicked in - nice to see a graph and instance create process in rd4j. I'm wondering if you've come across an approach to push the outputs of quantitative sparql queries such as your shot points% to a visualization tool..but, I'm looking for a semantically aware approach. So as to be informative to this forum.. what do I mean... I'm not talking about a basic flow of the output a flat file (e.g. csv) and digest by a generic tool - take the pick of zillions of libraies here, but Power BI is my current bug bear where Microsoft has sold the promise of self serve BI but leaves everyone else to manaage the chaos of cleaved, chewed and duplicated data and fragile and disconnected calculation(DAX measure) code base. So what am I looking for ? Let's call them "measures", but in the rdf construct they are sparql queries as you've documented so well. The measure operates on data that meets constraints of it's type and cardinality amongst other things, but which has, if required been automatically changed as to the conforiming "pattern" using constraint rules. I then build my client application with visuals e.g. a chart or map that displays the sparql query results. The visual changes based on properties or constraints on that data. More over the acutal measure is stored with the data and encapsualted in the client application. Plus it has full provenance also included. I note here that general ontology and instance visualization tools abound, but not what you could call BI tools for charting etc. I know these have been concevied and prototyped before. See: https://composing-the-semantic-web.blogspot.com/search?q=chart https://composing-the-semantic-web.blogspot.com/search?q=cha... I have been building my skills and work flow in a team that's adopting shacl and spin rules to drive data ignestion through to interfaces in the Topbraid tool set. The space is coming along but for this use case of charting and visualizing seems to have stalled, with the above UISPIN work now deprecated and waiting... maybe for shacl and some shacl javascript mappings to come to the rescue. I've found some interesting new work using webcomponents (polymer/LitElement) that makes sense: https://blog.resc.info/reboot-of-using-interface-encapsulation-to-listen-to-linked-data-predicates/ https://blog.resc.info/reboot-of-using-interface-encapsulati... But it feels a long way away for me to tackle conceptually and skills wise with yet another code framework to get on top of. Hoping you've seen some potential paths mate. Cheers Simon
dominotw 8y ago
http://rya.incubator.apache.org/ http://rya.incubator.apache.org/ How does it compare to rya?
- kinow 8y ago
  Looks like rya is a triple store on top of accumulo. Jena has SDB (stores data in relational DB), TDB (single machine, single JVM, transaction-wise, on-disk store [with mmap, journal, cache, etc]). Jena API is also extensible, and I remember someone was talking in the mailing list about an example with HBase I believe, as the backend for the triple store. Both HBase and Accumulo can be scaled to multiple nodes. So you should have some pros and cons of using either (or rya), as well as pros and cons of using Jena. Finally, I believe Jena is not simply a triple store. It has a web layer via Fuseki, services for managing graphs, command lines, and other tools useful for data processing with semantic web technologies.
ktk 8y ago
My comment is a repost that I did in another RDF related thread. It is still valid so here we go: I'm an engineer that used to do RDBs for a long time. One day a customer of a friend came with an issue that was in my opinion impossible to solve with relational DBs: He described data that is in flow all the time and there was no way we could come up with a schema that would fit his problem for more than one month after we finished it. Then I remembered that another friend once mentioned this graph model called RDF and its query language SPARQL and started digging into it. It's all W3C standards so it's very easy to read into it and there are competing implementations. It was a wild ride. At the time I started there was little to no tooling, only few SPARQL implementations and SPARQL 1.1 was not released yet. It was PITA to use it but it still stuck with me: I finally had an agile data model that allowed me and our customers to grow with the problem. I was quite sceptical if that would ever scale but I still didn't stop using it. Initially one can be overwhelmed by RDF: It is a very simple data model but at the same time it's a technology stack that allows you to do a lot of crazy stuff. You can describe semantics of the data in vocabularies and ontologies, which you should share and re-use, you can traverse the graph with its query language SPARQL and you have additional layers like reasoning that can figure out hidden gems in your data and make life easier when you consume or validate it. And most recently people started integrating machine learning toolkits into the stack so you can directly train models based on your RDF knowledge graph. If you want to solve a small problem RDF might not be the most logical choice at first. But then you start thinking about it again and you figure out that this is probably not the end of it. Sure, maybe you would be faster by using the latest and greatest key/value DB and hack some stuff in fancy web frameworks. But then again there is a fair chance the customer wants you to add stuff in the future and you are quite certain that at one point it will blow up because the technology could not handle it anymore. That will not happen with RDF. You will have to invest more time at first, you will talk about things like semantics of your customers data and you will spend quite some time figuring out how to create identifiers (URIs in RDF) that are still valid in years from now. You will have a look at existing vocabularies and just refine things that are really necessary for the particular use case. You will think about integrating data from relational systems, Excel files or JSON APIs by mapping them to RDF, which again is all defined in W3C standards. You will mock-up some data in a text editor written in your favourite serialization of RDF. Yes, there are many serializations available and you should most definitely throw away and book/text that starts with RDF/XML, use Turtle or JSON-LD instead, whatever fits you best. After that you start automating everything, you write some glue-code that interprets the DSL you just built on top of RDF and appropriate vocabularies and you start to adjust everything to your customer's needs. Once you go live it will look and feel like any other solution you built before but unlike those, you can extend it easily and increase its complexity once you need it. And at that point you realize that this is all worth is and you will most likely not touch any other technology stack anymore. At least that's what I did. I could go on for a long time, in fact I teach this stack in companies and gov-organizations during several days and I can only scratch the surface of what you can do with it. It does scale, I'm convinced by that by now and the tooling is getting better and better. If you are interested start having a look at the Creative Commons course/slides we started building. There is still lots of content that should be added but I had to start somewhere: http://linked-data-training.zazuko.com/ http://linked-data-training.zazuko.com/ Also have a look at Wikipedia for a list of SPARQL implementations: https://en.wikipedia.org/wiki/Comparison_of_triplestores https://en.wikipedia.org/wiki/Comparison_of_triplestores Would I use other graph databases? Definitely not. The great thing about RDF is that it's open, you can cross-reference data across silos/domains and profit from work others did. If I create another silo in a proprietary graph model, why would I bother? Let me finish with a quote from Dan Brickly (Googles schema.org) and Libby Miller (BBC) in a recent book about RDF validation: > People think RDF is a pain because it is complicated. The truth is even worse. RDF is painfully simplistic, but it allows you to work with real-world data and problems that are horribly complicated. While you can avoid RDF, it is harder to avoid complicated data and complicated computer problems. Source: http://book.validatingrdf.com/bookHtml005.html http://book.validatingrdf.com/bookHtml005.html I could not have come up with a better conclusion.
amirouche 8y ago
I think Resource Description Framework (RDF) is overkill. The underlying idea that is triple store (reminiscence of Entity-Attribute-Value model) is a good idea because it allows to model data with less overhead than a graph database will do over the same problem. Think of a list of items attached to a node or hypergraphs. All that is easier to do in a triple store. Actually, I think that triple stores are not given enough buzz. Most of RDF buzz is around ontologies (aka. standard vocabulary for describing things). Datomic prooves that triple store is a great idea in itself. Datomic is RDF in disguise. That is, it implements a versioned triple store and a language similar to SPARQL (based on core.logic (aka. clojure's minikanren))). When you think about it, a versioned database is a gem when it comes to debugging. Versioning a database is next to the best idea of the decade and that would not have been possible with another model than the triple store model. The idea of database versioning or more generally versioning of structured data, especially versioning ala git is making its way through academia (see https://project-hobbit.eu/ https://project-hobbit.eu/) and outside academia (cf. https://qri.io/ https://qri.io/) to help with the vast amount of data that is flowing.
- jacques_chester 8y ago
  > When you think about it, a versioned database is a gem when it comes to debugging. Versioning a database is next to the best idea of the decade and that would not have been possible with another model than the triple store model. This is a pretty bold claim. Why isn't it possible with another model?
  - amirouche 8y ago
    Well, you are correct. It is possible to implement historisation / audit trail in other database models. Sorry. Maybe I should blog about it :)
rilut 8y ago
- Are there any examples of using Apache Jena for reasoning/inference CMU's NELL/RTW [0] data? - Are there any tools for ontology inference like Apache Jena, but simpler and preferably built with js or python? [0] http://rtw.ml.cmu.edu/rtw/ http://rtw.ml.cmu.edu/rtw/

140 points by pplonski86 8y ago

Big fan of Jena. The command line tools alone for running SPARQL queries and converting between formats is great. Can anyone compare and contrast Jena in a Java project and/or operation against flat files with Neo4J or other graph systems?

karimtr 8y ago
Jena is an RDF triple store whereas Neo4J, Memgraph, or JanusGraph, for example, are property label graphs. Two different data models with distinct use-cases. I would compare Jena to other RDF engines like Stardog or Anzo graph. From a java project perspective, it doesn't really matter which one you choose as they all expose JDBC.
eecc 8y ago
What do you think of http://rdf4j.org http://rdf4j.org ?
- karimtr 8y ago
  Interesting. I'll have a look. Thanks for sharing :D

MrBuddyCasino 8y ago

A blast from the past. Semantik Web used be a good buzzword to receive EU research grants, and RDF tripel have been useful in some areas. What else remains?

EngineerBetter 8y ago
Ha, my sole experience of RDF was on an EU-funded research project :)
- maayank 8y ago
  Same here! :)
  - NewsAware 8y ago
    Mine on a non-funded research project. Must have done something wrong...
salex89 8y ago
I chuckled on the EU research grants part. My God, when I remember the monstrosities I was thinking up just to bodge in Semantic Web/Linked Data. Those guys are purely fueled by buzzwords. Maybe we met somewhere :) . "Semantic data is the future and always will be" - unknown author.
- CeiII 8y ago
  "Semantic data is the future and always will be" - unknown author." - Pretty sure it was Peter Norvig
kodz4 8y ago
What do you think Google, Facebook, Siri and Alexa use for their underlying knowledge graphs?
- pjc50 8y ago
  They have knowledge graphs? I thought it was all machine learning now. (That was a glib comment, but could we be a bit more specific about what's being repreented with RDF and other technologies, and for what purposes it's used and how?)
  - kodz4 8y ago
    Ya right machine learning. Machine learning over what? Comments on the internet? Donald Trump would automatically be classified as an Orange. The knowledge graph is a massive manual effort of encoding triples - https://en.wikipedia.org/wiki/Freebase https://en.wikipedia.org/wiki/Freebase.
    - mcguire 8y ago
      A dead effort?
    - riku_iki 8y ago
      Freebase wasn't built on RDF though, but had all it's own formats and query language.
      - kodz4 8y ago
        https://developers.google.com/freebase/#freebase-rdf-dumps https://developers.google.com/freebase/#freebase-rdf-dumps https://arxiv.org/abs/1712.08707 https://arxiv.org/abs/1712.08707
        riku_iki 8y ago
        The fact that they exported their data in form of RDF for other's convenience doesn't mean they built it on RDF.
nbaksalyar 8y ago
> What else remains? SPARQL. It's useful beyond Semantic Web, as evidenced by the raise of projects such as GraphQL. It can replace proprietary REST APIs and make the Web more connected and open. For e.g. see this short talk by Ruben Verborgh [1] and projects like Solid [2]. [1] https://youtu.be/LUF7plExdv8 https://youtu.be/LUF7plExdv8 [2] https://solid.mit.edu/ https://solid.mit.edu/
ungzd 8y ago
Wikidata is built mostly on RDF ideas. For example, there's SPARQL query interface for it: https://query.wikidata.org/ https://query.wikidata.org/ Schema.org is heavily used by search engines and metadata in its format is found on many websites. It's an RDF-based format.

azatris 8y ago

What even is Semantic Web in modern terms?

joshuabezaleel 8y ago
Knowledge Graph.
- diegoperini 8y ago
  And what is that exactly?
jimmy_ruska 8y ago
Things like json-ld, microdata, rdfa https://developers.google.com/search/docs/data-types/product https://developers.google.com/search/docs/data-types/product Wikidata also has data in rdf https://wikidata.org/wiki/Wikidata:Database_download#JSON_dumps_(recommended) https://wikidata.org/wiki/Wikidata:Database_download#JSON_du... I wonder what is the killer feature to be had. If you want highly connected data, you can use graph databases like neptune, neo4j, datomic. If you want logic programming, you still have swi-prolog, or something like picat, eclipse, mercury, which can easily model triples or custom ontology. There's also apache tinkerpop and similar which give querying a more object oriented feel. I see prolog can interop with Jena, but if it can, why not parse & query rdf/owl in prolog itself. Can't prolog do everything sparql can. What is the value offering on apache Jena?
- solidasparagus 8y ago
  Not 100% certain, but I think the killer feature is the ability to manage your own data while exposing semantic representations that external apps can interact with. The guiding idea behind RDF was (at one point) that users manage their own data while apps they give permission to can use that data.
- Boulth 8y ago
  Ha, yes, JSON-LD is RDF in JSON but no one admits that so it doesn't look ancient.

narrator 8y ago

I think the semantic web never worked because of seo spam. The closest it got to adoption in any form was the keywords meta tag. We know how that ended up.

acdha 8y ago
Less that and more the combination of bad tools, labyrinthine “standards” which were usually poorly implemented by said tools, and lack of concrete use-cases. Almost nobody had a case where implementing it showed a clear business value so most of the “you should do this” advocacy was along the lines of “this will become useful later” and that was a hard sell because it took many years before you could go to a webpage, read some decent instructions, mark something up, and run it through a validator or another client and get the expected data back out.
- namedgraph 8y ago
  That is exactly what is happening: people and companies marking up their pages with JSON-LD and RDFa metadata because it impacts SEO: https://developers.google.com/search/docs/guides/intro-structured-data https://developers.google.com/search/docs/guides/intro-struc...
  - acdha 8y ago
    That was my point: to the extent that the semantic web has happened at all, it's because someone can go to their boss and say “If we do this, here's a non-hypothetical benefit we'll see immediately” and because Google invested in high-quality documentation and tools which make it easy to do correctly.

martinsbalodis 8y ago

How is semantic web data being used today? Has there been any research to build a general AI using this?

mark_l_watson 8y ago

I have been a fan of RDF/RDFS/OWL for a long time. I started adding RDF in various forms to my main web site shortly after TBL, et al wrote the original Scientific American article. I have also written two semantic web/linked data books. The uptake for the semantic web has been spotty. I was hired as a contractor at Google to work with the Knowledge Graph and over the years I have had a lot of consulting work in related areas. That said, if I were building a custom Knowledge Graph for a company or organization today, I would likely use a graph database like Neo4J. Maybe not though - it would depend on the application.

blablabla123 8y ago
Really? Why not Jena or Marmotta with Postgres backend? I have little experience but just started setting up the latter, therefore I'm curious.
- zwifi 8y ago
  I can't speak for OP, but in my humble opinion, they don't really serve the same purpose. Jena and Marmotta are implementation of standards, while Neo4J is more of a proprietary system, and the underlying paradigm is different between triple graphs (for Jena and in a lesser dimension Marmotta) and property graphs (for most graph databases, e.g Neo4J, Apache Tinkerpop, OrientDB...). To make a very short summary, RDF is more concerned with the possibility to link data, so each piece of information is identified by a dereferencable URI, and can be described with an explicit model called an ontology (fancy word for a vocabulary used to describe data). On the other hand, Neo4J is more concerned with performance, but does not consider linking data accross the Web, or using an explicit schema. And for Marmotta, it is in kind of a bad place right now, the development seems a bit stalled, and the standard it implements is quite complicated compared to the majority of the problem it solves. This might evolve however, since Linked Data Platform (said standard) is now promoted by SOLID (https://solid.inrupt.com/ https://solid.inrupt.com/), a new initiative by Tim Berners Lee et al. to enable a truly distributed Web.
  - solidasparagus 8y ago

ragerino 8y ago

I prefer rdf4j as Rdf-Api for clients and GraphDB from Ontotext as RDF store. I never liked programming with Jena.

anon1253 8y ago

A lot of "blast from the past" comments! But as a counter example, we still use Jena extensively (in combination with its server Fuseki) to deal with biomedical ontologies and taxonomies for use in Natural Language Processing (e.g. Human Phenotype Ontology, Gene Ontology, etc). We even recently made the step to add some PROLOG-ish inference rules [1]. I have nothing but love for the RDF ecosystem, in the sense that I love their ideas even if some of the implementations are a bit wonky. For example, the performance of property paths (i.e. the + and * operators in sparql) leave things to be desired sometimes. Not to mention the funny looks from some devs when you say you use RDF, but I take that as a badge of honor! It took me a while to get it, so I finally decided to write down what it all means a couple of years ago [2]. [1]: https://jena.apache.org/documentation/inference/index.html https://jena.apache.org/documentation/inference/index.html [2]: https://joelkuiper.eu/semantic-web https://joelkuiper.eu/semantic-web

dekken_ 8y ago

Wow what a waste of time.

tqkxzugoaupvwqr 8y ago

What is the origin of the name / What does it mean? Couldn’t find an answer on the website.

evanb 8y ago
Jena is a city in the eastern part of Germany. It is historically famous for its university and optics innovation, and as an epicenter for bauhaus architecture.
- la_fayette 8y ago
  That is why people from AKSW Leipzig built a similar thing for PHP named Erfurt (https://github.com/AKSW/Erfurt https://github.com/AKSW/Erfurt) for their semantic wiki OntoWiki (https://github.com/AKSW/OntoWiki https://github.com/AKSW/OntoWiki).
bwestergard 8y ago
Probably a reference to Frege, a founder of modern logic. https://en.wikipedia.org/wiki/Gottlob_Frege https://en.wikipedia.org/wiki/Gottlob_Frege

pragmaticlurker 8y ago

why Java?

karimtr 8y ago
Easier to build something quicky but definitely not the best option for DBs. It's always about tradeoffs I guess.
- pragmaticlurker 8y ago
  for quickly, I would have chosen a dynamic language (python, php)
namedgraph 8y ago
because it is an enterprise language? Jena originates from Hewlett-Packard labs.

openasocket 8y ago

I actually stumbled across this a couple weeks ago, while thinking about the design for a project. I'm working with threat intelligence/security data and I need a nice way for users to save/share that data with the team, as well as query it in a simple fashion. People are pushing for a graph database. So I did some research and learned about RDF, OWL, Jena, etc. I'm still not sure if this is the right road to go down. Just from reading papers it's hard to tell how much of this is hype and over-engineering and how much is solid. Anyone have some RDF/OWL/Jens/SPARQL stories they want to share?

oever 8y ago
EU is developing a vocabulary editor called VocBench [0]. UK is making something similar: registry-core [1]. Here in the Netherlands there an active group exchanging RDF user stories [2]. To get a good start with RDF etc, I recommend 'Semantic Web for the Working Ontologist' (second edition or later!) [3]. It explains how inferencing works by doing inferencing with SPARQL queries. RDF is a powerful technology, but takes time to get acquainted with. What I dislike is that there's not many libraries (that I know) for working with it. I started a Rust library (Rome) for working with Turtle files and would like to have time to make it into a SPARQL engine. [0] http://vocbench.uniroma2.it/ http://vocbench.uniroma2.it/ [1] https://github.com/UKGovLD/registry-core https://github.com/UKGovLD/registry-core [2] http://www.pilod.nl/wiki/Platform_Linked_Data_Nederland http://www.pilod.nl/wiki/Platform_Linked_Data_Nederland [3] http://workingontologist.org/ http://workingontologist.org/
th0ma5 8y ago
I had a project to map out a whole bunch of relationships between various software configuration parameters of a vendor product. This was a lot of XML but also some CSV and JSON data. There's probably a ton of ways to do it, but I just spent some time extracting "facts" from this data into RDF. I didn't know what any of it was or how it all fit together but there were a lot of UUIDs and such. Once I had "everything" in RDF, using SPARQL queries to nose around was a really great way to find arbitrary relationships. I then used GraphViz to map out everything and it was very helpful to see how everything fit together. Could've probably used a regular database, or a key value store, but working with just flat files transformations at the command line was nice. I've also since used it manage a knowledge base and generate documentation and presentations. I haven't even used much of OWL or higher levels of abstraction yet, but just from a hack up something standpoint I think it is a pretty nice set of ideas to get basic graph operations just about anywhere you may need it, in the shell, in the browser, or in a backend project. Large sets of data may require something else, but anything less than 1g of data is rather performant IMHO.

andrewstellman 8y ago

I'm seeing several people asking what RDF is useful for. If you're curious, I use it for basketball analytics: https://github.com/andrewstellman/pbprdf https://github.com/andrewstellman/pbprdf Here's an article about my system, pbprdf: https://www.zdnet.com/article/nba-analytics-and-rdf-graphs-game-data-and-metadata-evolution-and-occams-razor/ https://www.zdnet.com/article/nba-analytics-and-rdf-graphs-g... And an example of its use: https://gist.github.com/andrewstellman/4872dbb9dc7593e56abddbe8b998b509 https://gist.github.com/andrewstellman/4872dbb9dc7593e56abdd... Here's an example of what the RDF files generated by pbprdf look like: Here's the ontology, which defines the vocabulary it uses: https://github.com/andrewstellman/pbprdf/blob/master/generated/ontology.ttl https://github.com/andrewstellman/pbprdf/blob/master/generat... And this is what the data looks like: <pbprdf/games/2017-11-29_Warriors_at_Lakers/230> pbprdf:shotPoints "3"^^xsd:int ; pbprdf:shotAssistedBy <pbprdf/players/Klay_Thompson> ; pbprdf:shotType "26-foot three point jumper" ; pbprdf:shotMade "true"^^xsd:boolean ; a pbprdf:Shot ; pbprdf:shotBy <pbprdf/players/Stephen_Curry> ; a pbprdf:Play ; pbprdf:forTeam <pbprdf/teams/Warriors> ; pbprdf:inGame <pbprdf/games/2017-11-29_Warriors_at_Lakers> ; pbprdf:time "10:23" ; pbprdf:period "3"^^xsd:int ; a pbprdf:Event ; rdfs:label "Warriors: Stephen Curry makes 26-foot three point jumper (Klay Thompson assists)" ; pbprdf:secondsIntoGame "1537"^^xsd:int ; pbprdf:secondsLeftInPeriod "623"^^xsd:int .

simo-climb 8y ago
Hi Andrew Thanks for posting your project in full. Too much of RDF/linked data is in the abstract, to big to see the moving parts, or behind propriety doors. I'm at the beginning of the learning curve so it's much appreciated and quite a number of things about the data workflow clicked in - nice to see a graph and instance create process in rd4j. I'm wondering if you've come across an approach to push the outputs of quantitative sparql queries such as your shot points% to a visualization tool..but, I'm looking for a semantically aware approach. So as to be informative to this forum.. what do I mean... I'm not talking about a basic flow of the output a flat file (e.g. csv) and digest by a generic tool - take the pick of zillions of libraies here, but Power BI is my current bug bear where Microsoft has sold the promise of self serve BI but leaves everyone else to manaage the chaos of cleaved, chewed and duplicated data and fragile and disconnected calculation(DAX measure) code base. So what am I looking for ? Let's call them "measures", but in the rdf construct they are sparql queries as you've documented so well. The measure operates on data that meets constraints of it's type and cardinality amongst other things, but which has, if required been automatically changed as to the conforiming "pattern" using constraint rules. I then build my client application with visuals e.g. a chart or map that displays the sparql query results. The visual changes based on properties or constraints on that data. More over the acutal measure is stored with the data and encapsualted in the client application. Plus it has full provenance also included. I note here that general ontology and instance visualization tools abound, but not what you could call BI tools for charting etc. I know these have been concevied and prototyped before. See: https://composing-the-semantic-web.blogspot.com/search?q=chart https://composing-the-semantic-web.blogspot.com/search?q=cha... I have been building my skills and work flow in a team that's adopting shacl and spin rules to drive data ignestion through to interfaces in the Topbraid tool set. The space is coming along but for this use case of charting and visualizing seems to have stalled, with the above UISPIN work now deprecated and waiting... maybe for shacl and some shacl javascript mappings to come to the rescue. I've found some interesting new work using webcomponents (polymer/LitElement) that makes sense: https://blog.resc.info/reboot-of-using-interface-encapsulation-to-listen-to-linked-data-predicates/ https://blog.resc.info/reboot-of-using-interface-encapsulati... But it feels a long way away for me to tackle conceptually and skills wise with yet another code framework to get on top of. Hoping you've seen some potential paths mate. Cheers Simon

dominotw 8y ago

http://rya.incubator.apache.org/ http://rya.incubator.apache.org/ How does it compare to rya?

kinow 8y ago
Looks like rya is a triple store on top of accumulo. Jena has SDB (stores data in relational DB), TDB (single machine, single JVM, transaction-wise, on-disk store [with mmap, journal, cache, etc]). Jena API is also extensible, and I remember someone was talking in the mailing list about an example with HBase I believe, as the backend for the triple store. Both HBase and Accumulo can be scaled to multiple nodes. So you should have some pros and cons of using either (or rya), as well as pros and cons of using Jena. Finally, I believe Jena is not simply a triple store. It has a web layer via Fuseki, services for managing graphs, command lines, and other tools useful for data processing with semantic web technologies.

ktk 8y ago

My comment is a repost that I did in another RDF related thread. It is still valid so here we go: I'm an engineer that used to do RDBs for a long time. One day a customer of a friend came with an issue that was in my opinion impossible to solve with relational DBs: He described data that is in flow all the time and there was no way we could come up with a schema that would fit his problem for more than one month after we finished it. Then I remembered that another friend once mentioned this graph model called RDF and its query language SPARQL and started digging into it. It's all W3C standards so it's very easy to read into it and there are competing implementations. It was a wild ride. At the time I started there was little to no tooling, only few SPARQL implementations and SPARQL 1.1 was not released yet. It was PITA to use it but it still stuck with me: I finally had an agile data model that allowed me and our customers to grow with the problem. I was quite sceptical if that would ever scale but I still didn't stop using it. Initially one can be overwhelmed by RDF: It is a very simple data model but at the same time it's a technology stack that allows you to do a lot of crazy stuff. You can describe semantics of the data in vocabularies and ontologies, which you should share and re-use, you can traverse the graph with its query language SPARQL and you have additional layers like reasoning that can figure out hidden gems in your data and make life easier when you consume or validate it. And most recently people started integrating machine learning toolkits into the stack so you can directly train models based on your RDF knowledge graph. If you want to solve a small problem RDF might not be the most logical choice at first. But then you start thinking about it again and you figure out that this is probably not the end of it. Sure, maybe you would be faster by using the latest and greatest key/value DB and hack some stuff in fancy web frameworks. But then again there is a fair chance the customer wants you to add stuff in the future and you are quite certain that at one point it will blow up because the technology could not handle it anymore. That will not happen with RDF. You will have to invest more time at first, you will talk about things like semantics of your customers data and you will spend quite some time figuring out how to create identifiers (URIs in RDF) that are still valid in years from now. You will have a look at existing vocabularies and just refine things that are really necessary for the particular use case. You will think about integrating data from relational systems, Excel files or JSON APIs by mapping them to RDF, which again is all defined in W3C standards. You will mock-up some data in a text editor written in your favourite serialization of RDF. Yes, there are many serializations available and you should most definitely throw away and book/text that starts with RDF/XML, use Turtle or JSON-LD instead, whatever fits you best. After that you start automating everything, you write some glue-code that interprets the DSL you just built on top of RDF and appropriate vocabularies and you start to adjust everything to your customer's needs. Once you go live it will look and feel like any other solution you built before but unlike those, you can extend it easily and increase its complexity once you need it. And at that point you realize that this is all worth is and you will most likely not touch any other technology stack anymore. At least that's what I did. I could go on for a long time, in fact I teach this stack in companies and gov-organizations during several days and I can only scratch the surface of what you can do with it. It does scale, I'm convinced by that by now and the tooling is getting better and better. If you are interested start having a look at the Creative Commons course/slides we started building. There is still lots of content that should be added but I had to start somewhere: http://linked-data-training.zazuko.com/ http://linked-data-training.zazuko.com/ Also have a look at Wikipedia for a list of SPARQL implementations: https://en.wikipedia.org/wiki/Comparison_of_triplestores https://en.wikipedia.org/wiki/Comparison_of_triplestores Would I use other graph databases? Definitely not. The great thing about RDF is that it's open, you can cross-reference data across silos/domains and profit from work others did. If I create another silo in a proprietary graph model, why would I bother? Let me finish with a quote from Dan Brickly (Googles schema.org) and Libby Miller (BBC) in a recent book about RDF validation: > People think RDF is a pain because it is complicated. The truth is even worse. RDF is painfully simplistic, but it allows you to work with real-world data and problems that are horribly complicated. While you can avoid RDF, it is harder to avoid complicated data and complicated computer problems. Source: http://book.validatingrdf.com/bookHtml005.html http://book.validatingrdf.com/bookHtml005.html I could not have come up with a better conclusion.

amirouche 8y ago

I think Resource Description Framework (RDF) is overkill. The underlying idea that is triple store (reminiscence of Entity-Attribute-Value model) is a good idea because it allows to model data with less overhead than a graph database will do over the same problem. Think of a list of items attached to a node or hypergraphs. All that is easier to do in a triple store. Actually, I think that triple stores are not given enough buzz. Most of RDF buzz is around ontologies (aka. standard vocabulary for describing things). Datomic prooves that triple store is a great idea in itself. Datomic is RDF in disguise. That is, it implements a versioned triple store and a language similar to SPARQL (based on core.logic (aka. clojure's minikanren))). When you think about it, a versioned database is a gem when it comes to debugging. Versioning a database is next to the best idea of the decade and that would not have been possible with another model than the triple store model. The idea of database versioning or more generally versioning of structured data, especially versioning ala git is making its way through academia (see https://project-hobbit.eu/ https://project-hobbit.eu/) and outside academia (cf. https://qri.io/ https://qri.io/) to help with the vast amount of data that is flowing.

jacques_chester 8y ago
> When you think about it, a versioned database is a gem when it comes to debugging. Versioning a database is next to the best idea of the decade and that would not have been possible with another model than the triple store model. This is a pretty bold claim. Why isn't it possible with another model?
- amirouche 8y ago
  Well, you are correct. It is possible to implement historisation / audit trail in other database models. Sorry. Maybe I should blog about it :)

rilut 8y ago

- Are there any examples of using Apache Jena for reasoning/inference CMU's NELL/RTW [0] data? - Are there any tools for ontology inference like Apache Jena, but simpler and preferably built with js or python? [0] http://rtw.ml.cmu.edu/rtw/ http://rtw.ml.cmu.edu/rtw/