A cross-RDF Graph Database investigation: the case of the missing context!

What is a graph in RDF?

RDF Graph Databases, also known as Triplestores, are a subset of Graph Databases where data is represented in triples. A simple triple consists of a subject, a predicate and an object aka subject-predicate-object. The predicate is the edge in the data graph that connects the subject to the object nodes. If we add context or graph information to a triple, we end up having the following structure: graph-subject-predicate-object. And when we talk about a graph in an RDF Graph Database, we always refer to it as the context. This type of triple, in turn, is named a quad.

The graph exists to structure and represent your data better because the triples with the same graph have the same context. The existence of the graph is one of the main differences between a property graph database and an RDF graph database. Yes, you can store your graph information in a property graph database too, but the RDF store is designed from the ground up with this in mind.

In the end, the choice of the database type is a matter of performance and how you want your data to be represented best for your use case.

What happens if there is no graph? 

One can insert data in the RDF Graph Database that does not contain the graph information. These simple triples are stored in the so called “unnamed graph” or “default graph” of the database. We want to see how to access this graph and we know that the DEFAULT SPARQL keyword is usually used in such cases.

Now that we specified what the DEFAULT graph is in relation to an RDF Graph Database, we will take a look at different triplestores and their specific implementation of it. We will look at some basic actions like data insert, delete and query. 

The triplestores we evaluated are: RDF4J 2.4, Stardog 6.1.1, GraphDB 8.8, Virtuoso  v7.2.2.1, AllegroGraph  6.4.6, MarkLogic 9.0, Apache JENA TDB, Oracle Spatial and Graph 18c. From now on when we mention one of them, we refer to the versions listed here. We did not change any configurations upon installation, so our observations relate to the default setup. 

Learnings

Data insert observations

The insert data SPARQL query used is

INSERT DATA {

<http://example.org/picasso> <http://example.org/paints> <http://example.org/guernica>

}

This query inserts a triple which has to graph information. The triple is stored in the DEFAULT graph of each RDF Graph Database. However there is a difference from store to store of what the DEFAULT graph represents. 

In Stardog, the DEFAULT graph keywords does not exist and instead one needs to use <tag:stardog:api:context:default>. All triples land here. 

Apache JENA TDB uses <urn:x-arq:DefaultGraph\> as default graph and the triples land here. You can use the DEFAULT keyword to query them.

Virtuoso has an internal default graph but the big difference is that a user cannot access it by using the DEFAULT keyword. The triples without graph information are added to this internal default graph.

Select data observations

The SPARQL query for selecting data used is:

SELECT * WHERE {

?s ?p ?o

}

For most of the triplestores what happens is that the data retrieved is coming from all graphs, including the DEFAULT graph. Basically it does not take into account any specific graph. The exceptions are:

Stradog retrieves data only from its internal default graph <tag:stardog:api:context:default>.

For Virtuoso you always need a graph otherwise you receive: “No default graph specified in the preamble”.

Delete data observations

The SPARQL query used to delete a triple is:

DELETE {

?s ?p ?o

} WHERE {

<http://example.org/picasso> <http://example.org/paints> ?o

}

Generally the triples that match the pattern are deleted from ALL graphs it exist in. Exceptions from this behaviour we found in:

Stradog deletes the triple only in the defined default graph. 

MarkLogic and Apache JENA TDB behaves the same. It deletes the triples that match the pattern only from the internal default graph. 

In Virtuoso one always needs to specify a graph to delete data. 

We also want to remark how a SPARQL query looks like when the DEFAULT keyword is present. The query to select data would look like:

SELECT * FROM DEFAULT WHERE {

?s ?p ?o

}

Additional known configurations 

In Stardog there is a configuration property which lets you choose which behaviour you like better. Through the query.all.graphs = true parameter, when you query without a graph, it will look in all graphs – default and named graphs – exactly like in the case of RDF4J. And if the property is set to false, it will only query the internal default graph. 

Additionally, if for some reason, you really need a graph in your SPARQL query even when you only need data from the DEFAULT graph, in Stardog you can write it as: FROM <tag:stardog:api:context:default>. And if you want to query all graphs, you can also do FROM <tag:stardog:api:context:all>.

In Virtuoso we learned that you always need to specify a graph when you query. So how do we work with the DEFAULT graph than?

There is a specific syntax for Virtuoso which lets you define/set your graph at the beginning of the query:

define input:default-graph-uri <graph_name>

INSERT DATA

{<http://example.org/picasso> <http://example.org/paints> <http://example.org/guernica>

}

Read more about it in the Virtuoso documentation.

AllegroGraph also provides some configurations. The defaultDatasetBehavior can be used directly in the SPARQL query to determine if  :all, :default or :rdf should be used when no graphs name is specified in the query. 

Or one can fix the default graph name with the default-graph-uris option (or the default-dataset-behavior) upon the run-sparql command.

In MarkLogic when working with REST or XQuery one has the default-graph-uri and a named-graph-uri parameters available, like mentioned in the SPARQL 1.1 Protocol recommendation to specify the graph.

In Apache JENA TDB all named graphs can be called  with <urn:x-arq:UnionGraph>. The configuration parameter tdb:unionDefaultGraph can be added to switch the default graph to the union of all graphs. And the default graph can be specifically called with <urn:x-arq:DefaultGraph\>

Conclusion

RDF Graph Databases are built from the group up with the context of your data in mind. Knowing your graphs and triplestore setup is, from my point of view, a basic knowledge for both developers but also data engineers. Always start with the question: “what setup do I need for my use case?”

Cross-RDF Graph Database behavior – the DEFAULT graph 

Triple store behavior on new installWRITE triples without graphSELECT triple without graphDELETE triple without graph
RDF4J 2.4Triples are added to DEFAULT graph.Retrieves data from ALL graphs including the DEFAULT graph.Deletes triples that match the pattern, from ALL graphs.
Stardog 6.1.1Triples are added to <tag:stardog:api:context:default>  graph which acts as the DEFAULT graph.It retrieves data only from the <tag:stardog:api:context:default> graph.It tries to delete the triple in the defined default graph. 
AllegroGraph  6.4.6Triples are added to an internal DEFAULT graph.Retrieves data from ALL graphs including the DEFAULT graph.Deletes triples that match the pattern, from ALL graphs.
MarkLogic 9.0Triples are added to an internal DEFAULT graph.Retrieves data from ALL graphs including the DEFAULT graph.It tries to delete the data in the internal DEFAULT graph.
GraphDB 8.8Triples are added to DEFAULT graph.Retrieves data from ALL graphs including the DEFAULT graph. Deletes triples that match the pattern, from ALL graphs.
Virtuoso v7.2.2.1Triples are added to an internal DEFAULT graph.You always need a graph otherwise you receive: “No default graph specified in the preamble”You always need to specify a graph to delete data.
Apache JENA TDBTriples are added to <urn:x-arq:DefaultGraph\>  graph which acts as the DEFAULT graph.It retrieves data only from the <urn:x-arq:DefaultGraph\> graph.It tries to delete the triple in the specified default graph. 
Oracle Spatial and Graph 18cTriples are added to an internal DEFAULT graph.Retrieves data from ALL graphs including the DEFAULT graph.Deletes triples that match the pattern, from ALL graphs.
Triple store behavior on new installWRITE triples without graphSELECT triple without graphDELETE triple without graph

What I’ve learned while triplifying a real dictionary

The Linked Data Lexicography for High-End Language Technology (LDL4HELTA) project was started in cooperation between Semantic Web Company (SWC) and K Dictionaries. LDL4HELTA combines lexicography and Language Technology with semantic technologies and Linked (Open) Data mechanisms and technologies. One of the implementation steps of the project is to create a language graph from the dictionary data.

The input data, described further, is a Spanish dictionary core translated into multiple languages and available in XML format. This data should be triplified (which means to be converted to RDF – Resource Description Framework) for several purposes, including to enrich it with external resources. The triplified data needs to comply with Semantic Web principles.

To get from a dictionary’s XML format to its triples, I learned that you must have a model. One piece of the sketched model, representing two Spanish words which have senses that relate to each other, is presented in Figure 1.

'arriesgado' model
Figure 1: Language model example

This sketched model first needs to be created by a linguist who understands both the language complexity and Semantic Web principles.

Language is very complex. With this we all agree! How complex it really is, is probably often underestimated, especially when you need to model all its details and triplify it.

So why is the task so complex?

To start with, the XML structure is complex in itself, as it contains nested structures. Each word constitutes an entry. One single entry can contain information about:

  • Pronunciation
  • Inflection
  • Range Of Application
  • Sense Indicator
  • Compositional Phrase
  • Translations
  • Translation Example
  • Alternative Scripting
  • Register
  • Geographical Usage
  • Sense Qualifier
  • Provenance
  • Version
  • Synonyms
  • Lexical sense
  • Usage Examples
  • Homograph information
  • Language information
  • Specific display information
  • Identifiers
  • and more…

Entries can have predefined values, which can recur but their fields can also have so-called free values, which can vary too. Such fields are:

  • Aspect
  • Tense
  • Subcategorization
  • Subject Field
  • Mood
  • Grammatical Gender
  • Geographical Usage
  • Case
  • and more…

As mentioned above, in order to triplify a dictionary one needs to have a clear defined model. Usually, when modelling linked data or just RDF it is important to make use of existing models and schemas to enable easier and more efficient use and integration. One well-known lexicon model is Lemon. Lemon contains good pieces of information to cover our dictionary needs, but not all of them. We started using also the Ontolex model, which is much more complex and is considered to be the evolution of Lemon. However, some pieces of information were still missing, so we created an additional ontology to cover all missing corners and catch the specific details that did not overlap with the Ontolex model (such as the free values).

An additional level of complexity was the need to identify exactly the missing pieces in Ontolex model and its modules and create the part for the missing information. This was part of creating the dictionary’s model which we called ontolexKD.

As a developer you never sit down to think about all the senses or meanings or translations of a word (except if you specialize in linguistics), so just to understand the complexity was a revelation for me. And still, each dictionary contains information that is specific to it and which needs to be identified and understood.

The process used in order to do the mapping consists of several steps. Imagine this as a processing pipeline which manipulates the XML data. UnifiedViews is an ETL tool, specialized in the management of RDF data, in which you can configure your own processing pipeline. One of its use cases is to triplify different data formats. I used it to map XML to RDF and upload it into a triple store. Of course this particular task can also be achieved with other such tools or methods for that matter.

In UnifiedViews the processing pipeline resembles what appears in Figure 2.

UnifiedViews
Figure 2: UnifiedViews pipeline used to triplify XML

The pipeline is composed out of data processing units (DPUs) which communicate iteratively. In a left-to-right order the process in Figure 2 represents:

  • A DPU used to upload the XML files into UnifiedViews for further processing;
  • A DPU which transforms XML data to RDF using XSLT. The style sheet is part of the configuration of the unit;
  • The .rdf generated files are stored on the filesystem;
  • And, finally, the .rdf generated files are uploaded into a triple store, such as Virtuoso Universal server.

Basically the XML is transformed using XSLT.

Complexity increases also through the URIs (Uniform Resource Identifier) that are needed for mapping the information in the dictionary, because with Linked Data any resource should have a clearly identified and persistent identifier! The start was to represent a single word (headword) under a desired namespace and build on it to associate it with its part of speech, grammatical number, grammatical gender, definition, translation – just to begin with.

The base URIs follow the best practices recommended in the ISA study on persistent URIs following the pattern:http://{domain}/{type}/{concept}/{reference}.

An example of such URIs for the forms of a headword is:

  • http://kdictionaries.com/id/lexiconES/entendedor-n-m-sg-form
  • http://kdictionaries.com/id/lexiconES/entendedor-n-f-sg-form

These two URIs represent the singular masculine and singular feminine forms of the Spanish word entendedor.

  • http://kdictionaries.com/id/lexiconES/entendedor-adj-form-1
  • http://kdictionaries.com/id/lexiconES/entendedor-adj-form-2

If the dictionary contains two different adjectival endings, as with entendedor which has different endings for the feminine and masculine forms (entendedora and entendedor), and they are not explicitly mentioned in the dictionary than we use numbers in the URI to describe them. If the gener would be explicitly mentioned the URIs would be:

  • http://kdictionaries.com/id/lexiconES/entendedor-adj-form
  • http://kdictionaries.com/id/lexiconES/entendedora-adj-form

In addition, we should consider that the aim of triplifying the XML was for all these headwords with senses, forms and translations, to connect and be identified and linked following Semantic Web principles. The actual overlap and linking of the dictionary resources remains open. A second step for improving the triplification and mapping similar entries, if possible at all, still needs to be carried out. As an example, let’s take two dictionaries, say German, which contain a translation into English and an English dictionary which also contains translations into German. We get the following translations:

Bank – bank – German to English

bank – Bank – English to German

The URI of the translation from German to English was designed to look like:

  • http://kdictionaries.com/id/tranSetDE-EN/Bank-n-SE00006116-sense-bank-n-Bank-n-SE00006116-sense-TC00014378-trans

And the translation from English to German would be:

  • http://kdictionaries.com/id/tranSetEN-DE/bank-n-SE00006110-sense-Bank-n-bank-n-SE00006110-sense-TC00014370-trans

In this case both represent the same translation but have different URIs because they were generated from different dictionaries (mind the translation order). These should be mapped so as to represent the same concept, theoretically, or should they not?

The word Bank in German can mean either a bench or a bank in English. When I translate both English senses back into German I get again the word Bank, but I cannot be sure which sense I translate unless the sense id is in the URI, hence the SE00006110 and SE00006116. It is important to keep the order of translation (target-source) but later map the fact that both translations refer to the same sense, same concept. This is difficult to establish automatically. It is hard even for a human sometimes.

One of the last steps of complexity was to develop a generic XSLT which can triplify all the different languages of this dictionary series and store the complete data in a triple store. The question remains: is the design of such a universal XSLT possible while taking into account the differences in languages or the differences in dictionaries?

The task at hand is not completed from the point of view of enabling the dictionary to benefit from Semantic Web principles yet. The linguist is probably the first one who can conceptualize “the how to do this”.

As a next step we will improve the Linked Data created so far and bring it to the status of a good linked language graph by enriching the RDF data with additional information, such as the history of a term or additional grammatical information etc.

Connected Data London

In July this year I had the opportunity to represent the company I work for, Semantic Web Company, at Connected Data London.  I had a 15 minutes slot to present some client success stories with connected data.  At the conference I also actively represented PoolParty, our  Software Suite, at the official stand offered to partners and sponsors.

I found London to be somehow “smaller” than the expectations floating around it. However, the people I interacted with (not at the conference) gave me immediately a very international flair of the city, more than in Vienna.

Anyway, next, you can see my recorder talk:

https://www.youtube.com/watch?v=l-nRbwJpZdU

In the opening of the event David Meza presented how it is to use RDF and graph technologies at NASA. I was really happy to attend his session and to meet him in person.  Next, you can watch here his video as well:

https://www.youtube.com/watch?v=5TxkNWd6zTQ

Ready to connect to the Semantic Web – now what?

As an open data fan or as someone who is just looking to learn how to publish data on the Web and distribute it through the Semantic Web you will be facing the question “How to describe the dataset that I want to publish?” The same question is asked also by people who apply for a publicly funded project at the European Commission and want to have a Data Management plan. Next we are going to discuss possibilities which help describe the dataset to be published.

The goal of publishing the data should be to make it available for access or download and to make it interoperable. One of the big benefits is to make the data available for software applications which in turn means the datasets have to be machine-readable. From the perspective of a software developer some additional information than just name, author, owner, date… would be helpful:

  • the condition for re-use (rights, licenses)
  • the specific coverage of the dataset (type of data, thematic coverage, geographic coverage)
  • technical specifications to retrieve and parse an instance (a distribution) of the dataset (format, protocol)
  • the features/dimensions covered by the dataset (temperature, time, salinity, gene, coordinates)
  • the semantics of the features/dimensions (unit of measure, time granularity, syntax, reference taxonomies)

To describe a dataset the best is always to look first at existing standards and existing vocabularies. The answer is not found looking only at one vocabulary but at several.

Data Catalog Vocabulary (DCAT)

DCAT is an RDF Schema vocabulary for representing data catalogs. It is an RDF vocabulary for describing any dataset, which can be standalone or part of a catalog.

Vocabulary of Interlinked Datasets (VoID)

VoID is an RDF vocabulary, and a set of instructions, that enable the discovery and usage of linked data sets. VOID is an RDF vocabulary for expressing metadata about RDF datasets.

Data Cube vocabulary

Data Cube vocabulary is focused purely on the publication of multi-dimensional data on the web. It is an RDF vocabulary for describing statistical datasets.

Asset Description Metadata Schema (ADMS)

ADMS is a W3C standard developed in 2013 and is a profile of DCAT, used to describe semantic assets.

You will find only partial answers of how to describe your dataset in existing vocabularies while some aspects are missing or complicated to express.

  1. Type of data – there is no specific property for the type of data covered in a dataset. This value should be machine readable which means it should be standardized, possibly to an URI which can be de-reference-able to a thing. And this ‘thing’ should be part of an authority list/taxonomy which is not existing yet. However one can use the adms:representationTechnique, which gives more information about the format in which a dataset is released. This points only to dcterms:format and dcat:mediaType.
  2. Technical properties like – format, protocol etc.
    There is no property for protocol and again these values should be machine-readable, standardized possibly to an URI.
    VoID can help with the protocol metadata but only for RDF datasets: dataDump, sparqlEndpoint.
  3. Dimensions of a dataset.
    • SDMX defines a dimension as “A statistical concept used, in combination with other statistical concepts, to identify a statistical series or single observations.” Dimensions in a dataset can therefore be called features, predictors, or variables (depending on the domain). One can use dc:conformsTo and use a dc:Standard if the dataset dimensions can be defined by a formalized standard. Otherwise statistical vocabularies can help with this aspect which can become quite complex. One can use the Data Cube vocabulary specifically qd:DimensionProperty, qd:AttributeProperty, qd:MeasureProperty, qd:CodedProperty in combination with skos:Concept and sdmx:ConceptRole.Data Cube
  4. Data provenance – there is the dc:source that can be used at dataset level but there is no solution if we want to specify the source at data record level.

In the end one needs to combine different vocabularies to best describe a dataset.

Add a dataset

The tools out there used for helping in publishing data seem to be missing one or more of the above mentioned parts.

  • CKAN maintained by the Open Knowledge Foundation uses most of DCAT and doesn’t describe dimensions.
  • Dataverse created by Harvard University uses a custom vocabulary and doesn’t describe dimensions.
  • CIARD RING uses full DCAT AP with some extended properties (protocol, data type) and local taxonomies with URIs mapped when possible to authorities.
  • OpenAIRE, DataCite (using re3data to search repositories) and Dryad use their own vocabularies.

The solution to these existing issues seem to be in general, introducing custom vocabularies.

References:

Developing for the Semantic Web

This year’s DevFest was again a blast!

I had the opportunity to hold a presentation about what I have been doing lately: a Web Application to show off the power of SPARQL. I turned my experience into an introduction of how to “Developing for the Semantic Web”.

Take a look:



My video from DevFest:



DevFest Vienna Website.

Introduction to Semantic Web

Hitchhiker’s guide to the Semantic Web

What? There is more to the web than what we know? But why? What is semantic web? Why do we need it? How does it look like? How do we use it? Where is this applicable? What does linked data got to do with it? Is this the future of web?

I was invited in March 2015 at the Women Techmakers Istanbul event where I got to hold an introduction about Semantic Web.


“The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.”  Tim Berners-Lee [1]

————

[1] The Semantic Web by Tim Berners-Lee, James Hendler, and Ora Lassila, Scientific American, 2001