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The Sparnatural knowledge graph explorer leverages SHACL specifications to drive a user interface (UI) that allows end users to easily discover the content of an RDF graph. What is the best way to make this UI-oriented SHACL specification ? if a SHACL specification for the knowledge graph structure already exists, can it be used directly ? does it require customization ? or is the Sparnatural SHACL spec completely decoupled from an existing knowledge graph spec ? and what if no SHACL spec exists at all ?

We faced all these different situations while deploying Sparnatural, and used various approaches to produce a satisfying end-user oriented specification. In particular, the Nakala repository is one of the latest graph for which Sparnatural was deployed. Nakala is a data repository that aims to preserve and disseminate data produced by French research projects in the Humanities and Social Sciences, in compliance with the FAIR principles. Nakala is a service offered by Huma-Num, a research infrastructure dedicated to the digital humanities. The Nakala knowledge graph contains `dcterms` metadata provided by researchers to describe the resources they upload. Additional non-dcterms metadata can also be provided. The metadata varies in quality and quantity depending on the researcher. When exposed in a SPARQL endpoint, resources, collections of resources and agents are described using the Europeana Data Model (EDM).

As the EDM dissemination channel for Nakala was new, no SHACL specification existed for it. We could have designed one for Sparnatural from scratch, but the choice was make to generate it automatically, with no human intervention. This was for three reasons : ease of configuration, flexibility in maintenance over time, and pedagogical reason, as it was important to explain the structure of the graph to target users.

Sparnatural UI

Let’s first have a look at what the Sparnatural UI looks like on an example from Nakala:

Once you know that « ProvidedCHO » stands for « Provided Cultural Heritage Object », and that « asWKT » encodes the location of a Place, you will be able to understand that the query searches for all ProvidedCHO entries gathered into a certain collection (« Cartes Université Bordeaux Montaigne » – a collection of maps), and selects their location and an optional description (and yes, the results of this query are displayed on a map, but that’s out of scope).

SHACL is derived automatically

In this project we wanted the shortest path from the graph to the query UI. Hence we used a SHACL generation algorithm, available in SHACL Play. By issuing SPARQL queries on an RDF graph, the algorithm determines the NodeShapes (targeting the classes used as values of `rdf:type`), and PropertyShapes (from all predicates used on instances on each class) of the model, with their node kinds, datatypes, class range, and cardinalities. It generates `sh:or` constraints when multiple datatypes or ranges are found. Note that in the case of Nakala a large variety of ranges are used, since the data comes from very open user inputs : the same `dcterms` property can be either an IRI or a Literal, with varying datatypes.

In addition, the algorithm computes some statistics on the dataset : the number of targets of each NodeShapes, the number of occurrences and the number of distinct values for each property shapes. The statistics are expressed using the `void` vocabulary, and `dcterms:conformsTo` is used to link void partitions to the corresponding shapes.

The SHACL Play documentation tool was then used to generate a report of the generated SHACL combined with the statistics. A few errors were spotted in the exported data, and fixed. We also saw that around 70 properties were present only a few times out of 700.000+ ProvidedCHO records. These properties were applied by probably a single or very few researchers when describing their data. It was decided to filter them out to keep the final UI simple, with an extra filtering step : based on statistics, property shapes used less than 0.1% of the number of targets of their node shapes are removed.

Here is a screenshot of the report : the right column shows the number of distinct values, and the column before is the number of total occurrences; we can immediately see that `dct:isReplacedBy` occurs only once, and `dct:isRequiredBy` occurs 81 times. They will be filtered out.

Sparnatural reads SHACL

Sparnatural can then read the SPARQL specification, together with the dataset statistics. When designing a query, value selection widgets for literal properties are determined by looking at the `sh:datatype` constraint (for number, dates, boolean, or map widgets). For IRI properties, statistics are used to distinguish between list and autocomplete widgets. Predicates with less than 500 distinct values will use a dropdown list, and those with more will use an autocomplete search field. The range is determined by reading `sh:class` or `sh:node`. The label to show in dropdown lists or to search on autocomplete field is determined by looking at a `dash:propertyRole = dash:LabelRole` annotation.

How about labels ? Sparnatural can read them from classes and properties of the original OWL file, if provided with it. Otherwise local names of target classes or predicates are used.

Other configuration techniques

Other Sparnatural deployments, such as the DBPedia demo are designed in SHACL from scratch, using spreadsheets. This requires more manual work, but has the advantage of tailoring the UI to exactly what needs to be shown, including user-oriented labels/tooltips/icons, hiding some properties, taking shortcuts or declaring inverses using property paths, etc. In the case of DBPedia, no SHACL spec exists, and deriving it automatically for the entire graph would probably not make a lot of sense, hence the necessity for a manual design.

For other projects we are working on a third configuration technique : a SHACL spec that describes the exact content of the graph is first built. It is used to publish the documentation of the model and to validate the data. A separate shapes file containing a Sparnatural-specific configuration layer is then added on top of it. That layer can hide shapes by applying an `sh:deactivated` annotation on them, can specify the UI widgets to use, add additional `dash:LabelRole` flags, add shortcut or inverse properties, etc.

The 3 configuration paths are shown in the following diagram:

Your query UI in minutes

We combined 4 tools (all open-source) : an algorithm to generate a « profile » in SHACL of an RDF dataset, a statistical report generator, a SHACL filter based on statistics, and the Sparnatural query UI. The ability to generate the SHACL profile and review it in the report provided a way to understand the structure of the data in a matter of minutes, while hours would have been necessary with SPARQL queries, without a guarantee of completeness. The provision of the query UI was made by dropping the SHACL file and the statistics to Sparnatural, without manual intervention. This shows the pivotal role of SHACL for data quality and model-driven approaches for knowledge graphs projects.

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We look forward to reading Veronika’s book, and you ?

Cet article Nakala : from an RDF dataset to a query UI in minutes – SHACL automated generation and Sparnatural est apparu en premier sur Sparna Blog.

To make it short, Sparnatural is a client-side component that allows non-expert users to explore an RDF Knowledge Graph by building SPARQL queries with little effort. 

Fully configurable – and customizable – it can be plugged to any existing SPARQL endpoint, without additional server required to adapt to your knowledge graph ontology. 

Innovative and intuitive, it aims at bringing your knowledge graph to your end-users in a visual way that « gamifies » the knowledge graph experience. 

Nb : Sparnatural is open source, under a LGPL-3.0 license.

So far, the configuration was made through an OWL ontology…

Sparnatural in SHACL ! 

… until now,

But the times they are a-changin’ …

You can now configure Sparnatural starting from a SHACL configuration spreadsheet !

SHACL in a nutshell 

Defined by a W3C Working Group, SHACL, as for « Shapes Constraint Language », is « a language for validating RDF graphs against a set of conditions. These conditions are provided as shapes and other constructs expressed in the form of an RDF graph. » 

First published in 2017, it has become a widely used standard to :

• describe structural constraints on data graphs ;
• validate that data graphs satisfy a set of conditions ;
• but also build user interface, generate code and integrate data !

The latter we will leverage for our brand new Sparnatural SHACL configuration.

Yes, in a spreadsheet !

SHACL may be quite unfamiliar for our users.

A good thing is you don’t need to be a SHACL expert to build your SHACL-shaped Sparnatural configuration.

Indeed ! the entire configuration is done via a spreadsheet whose columns correspond to the SHACL model.

Still, you can observe that all the Sparnatural features are here :

• the nodes & the edges of the knowledge graph, of course ;
• its labels and literal attributes (different kind of notes) ;
• but also the Sparnatural search widgets, icons, etc.

Give it a try ! 

Go to the DBpedia Museums demo :

Navigate the graph 

Start with picking up a class from the list and navigate through the properties to another class of the graph, search for a value…

… then click on the arrow to launch the query ▶️

Click on « Toggle SPARQL editor » below the query builder to display the corresponding SPARQL query :

… no need to say that you can create even more elaborate queries, just by adding new parameters when navigating through the knowledge graph !

Sample queries 

To give you a quick overview of it, you can also try to launch one of the sample queries we added to the demo.

On the screenshot below we can observe this one is a quite more complex query, using an optional parameter as we noticed that some values happen to be missing on DBpedia, either for Movements or Artworks…

Can we deduce that 19th-Century French women artists records are rather incomplete in English DBpedia ?

We’ll let you investigate on this point.

Multilingual 

It is also possible to translate (and display) your configuration in any language of your choice, so that you can showcase your knowledge graph in different languages – even if the graph itself does not contain labels or values with this language… 

Here translated in French :

Fully documented 

This new version of Sparnatural comes with an extensive documentation of all the features that can be used to date, from basic installation to more advanced configuration of the tool.

Get started with Hello Sparnatural !

Cet article Sparnatural : say it with SHACL ! est apparu en premier sur Sparna Blog.

Another star to light on EU’s linked open data maturity flag !

Not talking about 2024 exceptional Northern Lights to come, but this one’s also good news for science !

➡️ Late 2023, the Publications Office of the European Union announced on social media the public release of the new CORDIS SPARQL endpoint.

CORDIS, aka « the Community Research and Development Information Service of the European Commission », is « the […] primary source of results from the projects funded by the EU’s framework programmes for research and innovation, from FP1 to Horizon Europe ». Described as a « rich and structured public repository with all project information held by the European Commission such as project factsheets, participants, reports, deliverables and links to open-access publications », the CORDIS catalog has also been made available in 6 European languages by Publications Office’s editorial team.

Cherry on top of a whole process, the CORDIS SPARQL endpoint release comes to crown a long-term linked open data project. The aim identifying, acquiring, preserving and providing access to knowledge in a common will to share with the widest public possible a trust-worthy, qualified and structured information (see Publications Office 2021 Annual Management Report).

In the context of the pandemic (and recent opening of data.europa.eu, the official portal for European data, as defined in 2017–2025 European Open Data Space strategy), the EuroSciVoc taxonomy of fields of science was released April 2020, followed December 2021 by the publishing of European research information ontology (EURIO) on the EU Vocabularies website .

As presented at ENDORSE conference March 2021, the redesign of CORDIS data-model in accordance with Semantic Web standards contributed to bring the platform « from acting as a data repository to finally playing an active role as data provider », where EuroSciVoc taxonomy & EURIO ontology both played key roles in the creation of future CORDIS knowledge graph and SPARQL endpoint :

EuroSciVoc […] is a multilingual, SKOS-XL based taxonomy that represents all the main fields of science that were discovered from the CORDIS content, e.g., project abstracts. It was built starting from the hierarchy of the OECD’s Fields of R&D classification (FoRD) as root and extended through a semi-automatic process based on NLP techniques. It contains almost 1 000 categories in 6 languages (English, French, German, Italian, Polish and Spanish) and each category is enriched with relevant keywords extracted from the textual description of CORDIS projects. It is constantly evolving and is available on EU Vocabularies website […].

In order to transform CORDIS data into Linked Open Data, thus aligning with Semantic Web standards, best practices and tools in industry and public organizations, the need for an ontology emerged. CORDIS created the EURIO (European Research Information Ontology) based on data about research projects funded by the EU’s framework programmes for research and innovation. EURIO is aligned with EU ontologies such as DINGO and FRAPO and de facto standard ontologies such as schema.org and the Organization Ontology from W3C. It models projects, their results and actors such as people and organizations, and includes administrative information like funding schemes and grants.

EURIO, which is available on EU Vocabularies website, was the starting point to develop a Knowledge Graph of CORDIS data that will be publicly available via a dedicated SPARQL endpoint. »

(Enrico Bignotti & Baya Remaoun, « EuroSciVoc taxonomy and EURIO ontology: CORDIS as (semantic) data provider  » , ENDORSE March 16, 2021. PDF VIDEO)

… A Knowledge graph that was soon released in 2022-2023 (see INDUSTRY TRACK 1 on Tuesday, 25 October of ISWC 2022 Conference for more detail), until final opening of a CORDIS SPARQL endpoint late november 2023.

Now fancy a few SPARQL queries in there ?

Follow the SPARQL

CORDIS SPARQL endpoint is actually made available on CORDIS Datalab (and already referenced in EU Knowledge Graph among other European SPARQL endpoints ! see the query / see the results)

Here you can access a quick documentation guide to CORDIS Linked Open Data : https://cordis.europa.eu/about/sparql.

Let’s have a look at EURIO ontology first : we need to understand it to query CORDIS knowledge graph.

As we are told in the guide, the latest version can be downloaded on EU Vocabularies website. When we unzip the archive we access the whole documentation about EURIO Classes & properties that we need to write our SPARQL queries – and a diagram of main classes and properties of CORDIS data model : 

At first sight we can observe on the schema 3 main groups of entities :

• On the top right, the projects & publications associated, key ressources of CORDIS ;
• On the top left, the fundings & grants materials, on « monetary » side of the project ;
• On the bottom, the organisations & persons implied, with references & coordinates.

Let’s open CORDIS SPARQL endpoint – some easy queries can be run to begin exploring CORDIS knowledge graph.

Nb : the data on SPARQL endpoint is a snapshot, but freshest dumps can be found on European data portal !

Here a simple one to find a list of FundingSchemes with their titles and IDs corresponding to « Horizon 2020 » programme :

▶️ See the results

The FILTER REGEX enables us to display the IDs corresponding to H2020 Funding Schemes.

We can make another query to get the projects with the Funding Scheme Programme they are related to (note that, in EURIO a eurio:hasFundingSchemeProgramme is a sub-property of eurio:fundingScheme) :

▶️ See the results

(Here we used a property path with a « / » to shorten the query to get the acronyms of projects & Funding Scheme Programmes codes).

… and combining with the first query we can find the projects depending on H2020 Funding Scheme Programme in particular :

▶️ See the results

It is also possible to get the list of all existing Funding Scheme Programmes CORDIS projects have been funded by – we observe 27 of them here (from the SPARQL endpoint) – while adding a count function to know how many projects per FundingSchemeProgramme :

▶️ See the results

Querying the organisations properties will return other kind of useful informations about geographical location of the projects stakeholders. Let’s figure out we want to find the projects whose coordinating organisations have sites located in France :

Projects whose coordinating organisations have sites located in France  PREFIX skos: <http://www.w3.org/2004/02/skos/core#> PREFIX eurio: <http://data.europa.eu/s66#> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> SELECT ?project ?acronym ?role ?organisation ?country WHERE { # select the projects with their acronyms … ?project a eurio:Project. ?project eurio:hasAcronym/eurio:shortForm ?acronym. # … and organisations with ‘coordinator’ role and name … ?project eurio:hasInvolvedParty ?organisationrole. ?organisationrole eurio:roleLabel ?role. ?organisationrole eurio:roleLabel « coordinator ». ?organisationrole eurio:isRoleOf/eurio:legalName ?organisation. # … with address country for the sites defined at ‘FR’ ?organisationrole eurio:isRoleOf/eurio:hasSite/eurio:hasAddress/eurio:addressCountry ?country. VALUES ?country { ‘FR’ } } LIMIT 100

▶️ See the results

Depending on available data, you can either query via PostalAddress info (eurio:addressCountry ‘FR’) or AdministrativeArea (eurio:hasGeographicalLocation) … Here we’re lucky as both fields are mandatory ones.

Last but not least, we can also play with CORDIS vocabularies : here you’ll have the choice to investigate via plain keywords of Projects or Publications items, querying titles, abstracts or other types of literals…

An example of projects with abstracts containing string ❄ ‘winter’ ❄ – the URL giving the exact link to the project online :

▶️ See the results

But funniest way will be using EuroSciVoc taxonomy (and navigating through thesaurus hierarchy) : to do so we need to navigate through property « eurio:hasEuroSciVocClassification » to get the Concepts skosxl:prefLabel property … to finally obtain the thesaurus labels (don’t forget to choose a prefered language with a FILTER (lang parameter) :

Projects with their associated EuroSciVoc keywords (English prefLabels ) PREFIX skosxl: <http://www.w3.org/2008/05/skos-xl#> PREFIX skos: <http://www.w3.org/2004/02/skos/core#> PREFIX eurio: <http://data.europa.eu/s66#> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> SELECT ?project ?acronym ?ESV WHERE { # select the projects with their acronyms … ?project eurio:hasAcronym/eurio:shortForm ?acronym. # … with EuroSciVoc Classification prefLabels … ?project eurio:hasEuroSciVocClassification/skosxl:prefLabel/skosxl:literalForm ?ESV. # … only returning ‘English’ prefLabels FILTER (lang(?ESV) = ‘en’) } LIMIT 100

▶️ See the results

A bit more complex one using first level of hierarchy of the taxonomy : here we are searching for all skos:broader concepts « with no other broader concept » (the FILTER NOT EXISTS formula), aka the top concepts or root concepts of the vocabulary used to describe the projects. Then counting the projects by each category :

▶️ See the results

… and maybe again more explicit results if refined to level 2 of hierarchy  :

▶️ See the results

And a little last one with a count, to enumerate most used EuroSciVoc Concepts for indexing projects :

▶️ See the results

This one an ideal one to generate a word cloud maybe ?

What if we send the CSV data to some nice online word cloud generator then ?

(OMG they also have a shooting star shape  in there 🤩)

As a conclusion…

According to Science (CORDIS saying !), New Year’s resolutions appear difficult to be held… because most of time too ambitious, restrictive or unprecisely formulated : indeed, « the effectiveness of resolutions depends on how they are framed. »

Horizon 2024, let’s suggest a(n RDF ?) well-framed one : may CORDIS SPARQL endpoint initiative be an example for other structures who want to share Linked Open Data !

Wishing you Best Interoperability and a Very Merry ✨ Sparqling New Year ! ✨

Cet article CORDIS : a SPARQL endpoint is born ! est apparu en premier sur Sparna Blog.

Comment savoir si vous êtes un geek du web sémantique ? cette vidéo de parodie à propos des temps de réponse de DBPedia et de la fiabilité du serveur vous fait marrer. Merci Pierre-Yves !

La question de la stabilité et de la disponibilité des endpoints SPARQL publics est une vraie question et un angle d’attaque facile des détracteurs de SPARQL : comment n’importe quelle application en production pourrait-elle dépendre de la disponibilité d’un serveur qui serait en principe capable de répondre à n’importe quelle requête ? ce n’est pas comme les APIs, qui peuvent limiter le nombre de requêtes par jour par utilisateur, utiliser des mécanismes de cache, ou brider les types de requêtes possibles. Un service SPARQL met à disposition toutes les données pour tout type de requête. C’est la différence entre open data et open API que décrit Christian Fauré.

Bon, sinon, en plus de faire des vidéos parodiques, Pierre-Yves il a aussi créé le SPARQL Endpoint Status, un service qui monitore la disponibilité de tous les endpoints référencés dans datahub.io (environ 450). Comme ça, vous pouvez voir d’un coup d’oeil si votre source de données préférées est disponible, si elle a de bon temps de réponse sur les dernières heures ou les derniers jours, et quels opérateurs SPARQL elle supporte. Tout ça dans le but de faire en sorte que les services SPARQL ouverts le soient dans de bonnes conditions de disponibilité et de temps de réponse.

Rappelons, comme le fait la vidéo parodique, que DBPedia a tout de même un excellent uptime de 95% malgré tout son trafic, et qu’il est hébergé gratuitement pour mettre à disposition de tous les données structurées de Wikipedia.

Cet article Vidéo parodique sur la disponibilité de DBPedia – et les services SPARQL est apparu en premier sur Sparna Blog.