It uses a new feature of Sparnatural : the ability to act as a single UI facade to multiple SPARQL endpoints. The user visually writes a single query, the query is sent to multiple data sources, and results are aggregated to be presented to the user in a single result set, that includes each result provenance.

This is depicted in the diagram below:

The user writes his query visually in Sparnatural, here « All archaelogical sites where burials have been found, with the name of their discoverer, if known » :

The visual query is translated into SPARQL, and that SPARQL query is sent to each data source in the federation (actually, the user can select the ones he wants to query). This is depicted by the green arrows in the diagram, numbered « 1 ».

The SPARQL query looks like the following; note how it uses complex CIDOC-CRM property paths, such as the highlighted one, while the visual user query was simple:

Each SPARQL service returns a result. This is depicted by the orange arrows in the diagram, numbered « 2 ».

When every endpoint have answered, their results are aggregated into a single result set. This is number « 3 » in the diagram. During this aggregation, an extra column is added in the result set, containing the name of the source from which the result was retrieved.

The user sees the aggregated result; here, the name of the site, the name of its discovered when known, and the source in which the result was found:

This is possible thanks to the catalog configuration of Sparnatural.

Sparnatural can be passed a catalog of SPARQL endpoints in a federation, and in this case, it will send the same SPARQL query to each, and will aggregate the results. This happens for the final query of course, but also during selection of values in the query UI.

There are two main limits of this approach:

• Limit 1 : all sources in the federation must share the same data model, as the same query is sent to every source
• Limit 2 : each source must be independant : there should be no links from one source to another source so that the query can be solved by each endpoint independantly (so actually, no truly distributed linked data)

Those are the reasons I have entitled the post « simple federation facade ». Those 2 hypothesis are met in OpenArchaeo, and they were also met in the case of the (never released) prototype of the Europeana Linked Data taskforce. If you know other cases of data federation in which this is also true, tell us ! (we could actually try the same on a few DBPedia endpoints using the dbo ontology as a pivot model)

Now guess what ? in Sparnatural we have a « query UI to SPARQL » transformation step, thanks to the SHACL configuration of Sparnatural. Basically we can map a UI property on an underlying property path. Then it would not be too difficult to do this mapping on a source-by-source basis, to have different queries sent to each source, from a single query in the UI. The result set structure would be the same, and result set aggregation can still happen. We would then overcome the first limit described above. That’s the next step !

Cet article Sparnatural as a simple data federation facade est apparu en premier sur Sparna Blog.

Paired with the CIDOC-CRM in a federated way, OpenArchaeo aims at :

• making available the archaeological datasets produced by the MASAplus consortium’s partners on the semantic web, in the form of a triplestore with data aligned with the ontology and its extensions dedicated to archaeology ;
• providing an intuitive query interface for archaeological data.

The latter query interface integrates the Sparnatural knowledge graph exploration component. The UI of this component was heavily inspired by the British Museum’s ResearchSpace semantic search feature, as the system proposes the user to build his own queries based on the CIDOC-CRM model underlying the data.

About ResearchSpace platform

Initiated in 2009 by a cross-disciplinary team at the British Museum, ResearchSpace is « A full CIDOC-CRM authoring and search system, based on an exhaustive collection of forms that reflects all applicable relationships from the CIDOC CRM ontology. »

Among a wide range of semantic tools to create, manipulate, analyse and visualise data, the platform provides a semantic structured search component based on categories and relations.

While open source, ResearchSpace’s code didn’t fit our architecture : we just chose to follow the simple visual elements of ResearchSpace’s query interface to develop our own Sparnatural query builder for OpenArchaeo, and set up a system of icons to identify the main components of the archaeological data.

ResearchSpace has recently (december 2024) released a brand new 4.0.0 version. This latest can be installed easily and now comes with a default setup of forms based on the CIDOC-CRM. It enables image annotations, knowledge maps creations, semantic narratives writing, timeline productions, and more semantic tools.

Sparnatural’s first use-case was OpenArchaeo’s CIDOC-CRM model !

The structure of the knowledge graph of OpenArchaeo relies on the CIDOC-CRM and some of its extension (CRMarchaeo, CRMsci and CRMba). It is a generic model that covers the basic concepts found in most archaeological corpuses (site, operation, structure, feature, wall, burial, stratigraphic unit and artifact).

Here a focus on Class S19 :

Several external thesauri were added too for querying the datasets : PACTOLS thesaurus for archaelogy, but also Geonames and Periodo for spatial and temporal searches.

This way, when users wish to connect two elements (artifact and site for example), the interface automatically suggests the available relationships between these entities, enabling users to formulate their request in a simple way without having to know either the entities and properties of CIDOC CRM, or the structure of the system : the SPARQL queries that correspond to the sentences visually built by users will be automatically computed. In addition, the usage of thesauri allows the users to cross-reference easily multiple datasets through the different widgets proposed in Sparnatural.

Get the latest release of Sparnatural !

Since it was created for OpenArchaeo in 2019, Sparnatural UI has been fully redesigned. It now offers a large panel of features, from different widgets for value selection (dropdown lists, ordered by occurrence count or alphabetically, autocomplete search fields, date pickers, tree widgets…) to brand new result display plugins : the default visualisation is a table of results, but if the results are geolocalized they can be shown in a map. Also grid, stats, pie or bar charts, and a timeline plugin have been made available and documented.

To go further on OpenArchaeo’s platform …

See a presentation of the project on the CIDOC Museum Documentation Channel
(« Semantic modelling of archaeological data online workshop series »)

The platform : http://openarchaeo.huma-num.fr/

The project : https://masa.hypotheses.org/openarchaeo

Read full research paper about the project : https://ceur-ws.org/Vol-2375/paper1.pdf

Image : Vestiges of a large villa in Courbehaye « les Deux Muids / le Moulin de Mongé », photo Alain Lelong (2003), Atlas des Établissements Ruraux de Beauce Antique, licence CC BY-NC-SA

Cet article The Genesis of Sparnatural in the context of the OpenArchaeo platform est apparu en premier sur Sparna Blog.

Voilà maintenant près d’un an et demi que FranceArchives, le Portail national des Archives de France, a annoncé le déploiement de l’outil « Supernatural » (comprendre Sparnatural) via ses réseaux, dans l’optique de proposer à ses usagers « un accès nouveau aux métadonnées archivistiques, complémentaire de la recherche classique par le moteur du portail ».

Porté par le Service interministériel des Archives de France, le portail FranceArchives offre une recherche fédérée dans près de 26 millions de métadonnées archivistiques produites par près de 170 institutions et entièrement sémantisées en RDF par le biais de l’ontologie RiC-O version 0.2 publiée en février 2021.

C’est une des premières utilisations de RiC-O à grande échelle (même s’il faudra à l’avenir qu’il se mette à jour sur la version 1.0 de RiC-O publiée depuis !), et c’est également l’un des premiers entrepôts de données archivistiques de cette taille sur le Linked Open Data.

… Un graphe de données qui a tout pour être « Supernaturalisé »

Des données de qualité à une recherche augmentée

… Enfin, il va surtout s’agir de ses « données de qualité », autrement dit les :

• inventaires avec leurs composants,
• notices descriptives de producteurs d’archives,
• fiches signalétiques des services d’archives,

 … tous objets liés à une autorité « personnes et institutions », « lieux » et « thèmes » de qualité (soit moins de 5% des métadonnées du portail avant conversion… et plus de 70% de l’ensemble du réservoir en RDF !), autorités de qualité elles-mêmes harmonisées et alignées vers des référentiels nationaux et internationaux.

Modèle particulièrement adapté à la description des archives en RDF, c’est l’ontologie RiC-O (v0.2) qui a été utilisée pour la sémantisation des données XML EAD – XML EAC-CPF vers RDF, complétée de schema.org pour les fiches signalétiques des services de l’annuaire au format XML EAG.

Les informations relatives aux archives et à leurs producteurs étant décrites dans des fichiers différents, la recherche avancée via SPARQL rend désormais possible une interrogation fédérée plus fine d’un vaste corpus de notices en « traversant » le graphe structuré selon le modèle RiC-O. En effet, l’intérêt de l’interrogation via SPARQL est de casser les silos entre types de métadonnées : il permet de faire une recherche transversale entre données provenant de fichiers EAD et de fichier EAC-CPF.

Les notices affichées en résultats de recherche montrent les alignements existants vers les notices de producteurs externes, Wikidata, data.bnf, GeoNames ou encore le Thesaurus pour l’indexation matières des archives locales. C’est ainsi dans l’onglet Personnes/indexations liées que sont exploités les résultats de la conversion en RDF, par le biais de suggestions de recherches complémentaires sur le portail classique.

Une façon de faire bénéficier le grand public du RDF de manière complètement transparente pour lui !

Quelques exemples de requêtes…

On accède à l’outil via le menu « Recherche SPARQL » en haut à droite du site du portail :

Plusieurs exemples de requêtes sont à disposition pour explorer les données :

De la requête la plus simple :

Personne est membre de Institution

À des requêtes de plus en plus élaborées et complexes, comme ici :

Lieux qui sont le sujet des archives reliées au fonds « Fabrique de berlingot Eysséric »

Où l’on voit que l’on peut retracer le cheminement de la requête à travers le graphe de l’ontologie RiC-O en cliquant sur « Afficher/masquer l’éditeur SPARQL ».

Des archives à la page…

À noter que le projet, qui avait fait l’objet d’une présentation à l’occasion de SWIB (Semantic Web in Libraries) et de SemWebPro 2023 a été entièrement déployé (et configuré !) à partir de la documentation disponible sur le site web de Sparnatural.

N’hésitez pas à aller la consulter !

Hello Sparnatural

How-to configure in SHACL

Reference documentation of Sparnatural widgets

Pour aller plus loin sur la sémantisation des archives…

Le déploiement de Sparnatural sur FranceArchives fait suite à une autre réalisation de l’année précédente, le démonstrateur Sparnatural des Archives nationales. Celui-ci avait permis de faire évoluer Sparnatural et de le déployer sur un graphe sémantique en RiC-O de 20 millions de triplets (hors inférence), alimenté avec le contenu de 1577 instruments de recherche décrivant les archives de 40 des 122 études notariales de Paris conservées aux Archives nationales, de 1120 notices décrivant ces études et les notaires qui y ont exercé, et d’autres référentiels des Archives nationales notamment sur les lieux de Paris. La réalisation de ce démonstrateur a été entièrement documentée en français et en anglais. Ce démonstrateur et ses interfaces évolueront d’ailleurs bientôt.

Depuis, Sparna s’est impliqué dans le domaine de la sémantisation des archives puisque nous développons également, pour les comptes des Archives Nationales, l’outil Ric-O converter.

Celui-ci permet la conversion de notices EAD et EAC vers du RDF exprimé en RiC-O. Nous finalisons actuellement une nouvelle version du convertisseur pour le rendre compatible RiC-O 1.0 (et même 1.1 dont la sortie est imminente).

Un nouvel article à paraître ici sur RiC-O ? … Stay tuned !

Cet article Retour sur … Le déploiement de Sparnatural pour FranceArchives est apparu en premier sur Sparna Blog.

“

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.

”

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.

The possible entities, connections and properties that are shown to the user need to be specified to Sparnatural. This configuration is written in a SHACL specification. This specification encodes both the structure of the knowledge graph (as we want it to be presented to the user), plus some additionnal UI-oriented information, like icons, order of entries, or value selection widgets to use.

How can the SHACL configuration of Sparnatural be produced ? we faced 3 different situations : either we do it manually, or (semi-)automatically, or with an off-the-shelf specification complemented with manual annotations. We will give you a brief description of each possibility below.

Write your SHACL manually (in Excel)

SHACL has one main disadvantage : there is no widely available, free-to-use SHACL editor to create/edit these specs (while Protégé, for example, allows anyone to edit an OWL ontology). To overcome this lack of tool, we are using an Excel-to-SHACL conversion tool, xls2rdf. We (and our users) write the specification in an Excel template, that is then turned in SHACL by an API call to xls2rdf. This is what we described in a previous post.

We have a good documentation on how to design your Sparnatural documentation in Excel, if you want to try it (but see the Hello Sparnatural tutorial first to setup your working test page). And we are here to help if you need to ! (ask questions on the Github repository).

If you are looking for a general-purpose SHACL Excel template, there is one in SHACL Play.

Get your SHACL automatically (statistics included)

SHACL is machine-readable and also writable, so we can use our SHACL generation algorithm to produce a SHACL « profile » of an RDF dataset. The algorithm sends SPARQL queries to identify all classes and properties, with their range, cardinalities, datatype, etc. This SHACL profile can be fed to Sparnatural directly. With an additionnal bonus : it is also very useful to gather statistics of the dataset at this stage. Number of instances of each class, number of occurrences of each property and number of distinct values. Why is it useful ? first because Sparnatural is able to show them in the UI, giving a hint to the user on how many entities of each type exist in the dataset:

second because by knowing how many distinct values exist for a given property, Sparnatural can show either a dropdown list (if there are less than 500) or an autocomplete search field (if there are more).

Also, as a side effect, seeing the profile and statistics of your dataset can help you spot errors (« why does the statistics tells me that this identifier is not always present ? it should be mandatory ! »).

You can also provide the OWL ontology to Sparnatural along with the SHACL specification. Simply pass the 2 files in the configuration of the component. The ontology can be leveraged for 2 things : the hierarchy of classes and properties from the ontology can be used in the Sparnatural UI, and all labels and comments of classes and properties can be read to be displayed in the UI, giving nice defaults if they are not present in the SHACL spec.
This automated SHACL generation was the one used for the Nakala Sparnatural query interface, and here is how the properties look like, you can see it is organized according to the DublinCore properties hierarchy:

Grab an off-the-shelf SHACL specification

Some clients do write and publish SHACL specifications. Yes. This is the case for example for the European Parliament in their open-data portal. They publish the documentation of their data-model with our SHACL Play documentation generator. The specification existed long before we tried Sparnatural on their data. We simply took their specification, loaded it in Sparnatural and… voilà ! it worked seamlessly, but to make it nicer we added in a separate file additionnal icons and tooltips information.

SHACL Synergies

There is a lot of synergies to find with a SHACL configuration in a model-driven perspective. You can publish the documentation, validate data, power your Sparnatural UI, and generate your API JSON schema from the same specification file. And probably much more – what would YOU do with a SHACL specification of your knowledge graph ?

Cet article Sparnatural SHACL configuration : manual, automated, off-the-shelf 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.

Après la conférence les supports seront à retrouver sur la section « bibliographie » du site.

J’en profite pour vous indiquer les 2 dernières démos mises au point avec Sparnatural :

• Recherche sur les partitions musicales de la Cité de la Musique / Philharmonie de Paris, modélisées avec FRBRoo + Doremus, pour le projet ScoreBot (chatbot de recherche sur des partitions musicales)
• Recherche/navigation sur la maquette du référentiel d’interopérabilité du médicament. Ce référentiel est publié sur le SMT de l’ANS.

Cet article Sparnatural à SemWeb.pro 2022 le 8 novembre est apparu en premier sur Sparna Blog.