Extended KBpedia With Wikipedia Categories

A knowledge graph is an ever evolving structure. It needs to be extended to be able to cope with new kinds of knowledge; it needs to be fixed and improved in all kinds of different ways. It also needs to be linked to other sources of data and to other knowledge representations such as schemas, ontologies and vocabularies. One of the core tasks related to knowledge graphs is to extend its scope. This idea seems simple enough, but how can we extend a general knowledge graph that has nearly 40,000 concepts with potentially multiple thousands more? How can we do this while keeping it consistent, coherent and meaningful? How can we do this without spending undue effort on such a task? These are the questions we will try to answer with the methods we cover in this article.

The methods we are presenting in this article are how we can extend Cognonto‘s KBpedia Knowledge Graph using an external source of knowledge, one which has a completely different structure than KBpedia and one which has been built completely differently with a different purpose in mind than KBpedia. In this use case, this external resource is the Wikipedia categories structure. What we will show in this article is how we may automatically select the right Wikipedia categories that could lead to new KBpedia concepts. These selections are made using a SVM classifier trained over graph embedding vectors generated by a DeepWalk model based on the KBpedia Knowledge Graph structure linked to the Wikipedia categories. Once appropriate candidate categories are selected using this model, the results are then inspected by a human to take the final selection decisions. This semi-automated process takes 5% of the time it would normally take to conduct this task by comparable manual means.

Continue reading “Extended KBpedia With Wikipedia Categories”

Building and Maintaining the KBpedia Knowledge Graph

The Cognonto demo is powered by an extensive knowledge graph called the KBpedia Knowledge Graph, as organized according to the KBpedia Knowledge Ontology (KKO). KBpedia is used for all kinds of tasks, some of which are demonstrated by the Cognonto use cases. KBpedia powers dataset linkage and mapping tools, machine learning training workflows, entity and concept extractions, category and topic tagging, etc.

The KBpedia Knowledge Graph is a structure of more than 39,000 reference concepts linked to 6 major knowledge bases and 20 popular ontologies in use across the Web. Unlike other knowledge graphs that analyze big corpuses of text to extract “concepts” (n-grams) and their co-occurrences, KBpedia has been created, is curated, is linked, and evolves using humans for the final vetting steps. KBpedia and its build process is thus a semi-automatic system.

The challenge with such a project is to be able to grow and refine (add or remove relations) within the structure without creating unknown conceptual issues. The sheer combinatorial scope of KBpedia means it is not possible for a human to fully understand the impact of adding or removing a relation on its entire structure. There is simply too much complexity in the interaction amongst the reference concepts (and their different kinds of relations) within the KBpedia Knowledge Graph.

What I discuss in this article is how Cognonto creates and then constantly evolves the KBpedia Knowledge Graph. In parallel with our creating KBpedia over the years, we also have needed to develop our own build processes and tools to make sure that every time something changes in KBpedia’s structure that it remains satisfiable and coherent.

Continue reading “Building and Maintaining the KBpedia Knowledge Graph”

Web Page Analysis With Cognonto

Extract Structured Content, Tag Concepts & Entities


Cognonto is brand new. At its core, it uses a structure of nearly 40 000 concepts. It has about 138,000 links to external classes and concepts that defines huge public datasets such as Wikipedia, DBpedia and USPTO. Cognonto is not a children’s toy. It is huge and complex… but it is very usable. Before digging into the structure itself, before starting to write about all the use cases that Cognonto can support, I will first cover all of the tools that currently exist to help you understand Cognonto and its conceptual structure and linkages (called KBpedia).

The embodiment of Cognonto that people can see are the tools we created and that we made available on the cognonto.com web site. Their goal is to show the structure at work, what ties where, how the conceptual structure and its links to external schemas and datasets help discover new facts, how it can drive other services, etc.

This initial blog post will discuss the demo section of the web site. What we call the Cognonto demo is a web page crawler that analyzes web pages to tag concepts, to tag named entities, to extract structured data, to detect language, to identity topics, and so forth. The demo uses the KBpedia structure and its linkages to Wikipedia, Wikidata, Freebase and USPTO to tag content that appears in the analyzed web pages. But there is one thing to keep in mind: the purpose of Cognonto is to link public or private datasets to the structure to expand its knowledge and make these tools (like the demo) even more powerful. This means that a private organization could use Cognonto, add their own datasets and link their own schemas, to improve their own version of Cognonto or to tailor it for their own purpose.

Let’s see what the demo looks like, what is the information it extracts and analyzes from any web page, and how it ties into the KBpedia structure.

Continue reading “Web Page Analysis With Cognonto”

New UMBEL Concept Noun Tagger Web Service & Other Improvements

Last week, we released the UMBEL Concept Plain Tagger web service endpoint. Today we are releasing the UMBEL Concept Noun Tagger. umbel_ws

This noun tagger uses UMBEL reference concepts to tag an input text, and is based on the plain tagger, except as noted below.

The noun tagger uses the plain labels of the reference concepts as matches against the nouns of the input text. With this tagger, no manipulations are performed on the reference concept labels nor on the input text except if you specify the usage of the stemmer. Also, there is NO disambiguation performed by the tagger if multiple concepts are tagged for a given keyword.

Intended Users

This tool is intended for those who want to focus on UMBEL and do not care about more complicated matches. The output of the tagger can be used as-is, but it is intended to be the input to more sophisticated reference concept matching and disambiguation methods. Expect additional tagging methods to follow.

Stemming Option

This web service endpoint does have a stemming option. If the option is specified, then the input text will be stemmed and the matches will be made against an index where all the preferred and alternative labels have been stemmed as well. Then once the matches occurs, the tagger will recompose the text such that unstemmed versions of the input text and the tagged reference concepts are presented to the user.

Depending on the use case. users may prefer turning on or off the stemming option on this web service endpoint.

The Web Service Endpoint

The web service endpoint is freely available. It can return its resultset in JSON, Clojure code or EDN (Extensible Data Notation).

This endpoint will return a list of matches on the preferred and alternative labels of the UMBEL reference concepts that match the noun tokens of an input text. It will also return the number of matches and the position of the tokens that match the concepts.

The Online Tool

We also provide an online tagging tool that people can use to experience interacting with the web service.

The results are presented in two sections depending on whether the preferred or alternative label(s) were matched. Multiple matches, either by concept or label type, are coded by color. Source words with matches and multiple source occurrences are ranked first; thereafter, all source words are presented alphabetically.

The tagged concepts can be clicked to have access to their full description.


Other UMBEL Website Improvements

We also did some more improvements to the UMBEL website.

Search Autocompletion Mode

First, we created a new autocomplete option on the UMBEL Search web service endpoint. Often people know the concept they want to look at, but they don’t want to go to a search results page to select that concept. What they want is to get concept suggestions instantly based on the letters they are typing in a search box.

Such a feature requires a special kind of search which we call an “autocompletion search”. We added that special mode to the existing UMBEL search web service endpoint. Such a search query takes about 30ms to process. Most of that time is due to the latency of the network since the actual search function takes about 0.5 millisecond the complete.

To use that new mode, you only have to append /autocomplete to the base search web service endpoint URL.

Search Autocompletion Widget

Now that we have this new autocomplete mode for the Search endpoint, we also leveraged it to add autocompletion behavior on the top navigation search box on the UMBEL website.

Now, when you start typing characters in the top search box, you will get a list of possible reference concept matches based on the preferred labels of the concepts. If you select one of them, you will be redirected to their description page.


Tagged Concepts Within Concept Descriptions

Finally, we improved the quality of the concept description reading experience by linking concepts that were mentioned in the descriptions to their respective concept pages. You will now see hyperlinks in the concept descriptions that link to other concepts.


Validating RDF Data by Evaluating RDF/Clojure Code

I recently started to investigate different ways to serialize RDF triples using Clojure code 1 2 3. I had at least two goals in mind: first, ending up with an RDF serialization format that is valid Clojure code and that could easily be manipulated using core Clojure functions. The second goal was to be able to “execute” the code to validate the data according to the semantics of the ontologies used to define the data.

This blog post focuses on showing how the second goal can be implemented.

Before doing so, let’s take some time to explore what the sayings of ‘Code as Data' and ‘Data as Code' may mean in that context.

Code as Data, Data as Code

What is Code as Data? It means that the program code you write is also data that can be manipulated by a program. In other words, the code you are writing can be used as input [to a macro], which can then be transformed and then evaluated. The code is considered to be data to be manipulated by a macro system to output executable code. The code itself becomes data that can be manipulated with some internal mechanism in the language. But the result of these manipulations is still executable code.

What is Data as Code? It means that you can use a programming language’s code to embed (serialize) data. It means that you can specify your own sublanguage (DSL), translate it into code (using macros) and execute the resulting code.

The initial goal of a RDF/Clojure serialization is to specify a way to write RDF triples (data) as Clojure (code). That code is data that can be manipulated by macros to produce executable code. The evaluation of the resulting code is the validation of the data structures (the graph defined by the triples) according to the semantics defined in the ontologies. This means that validating the graph may also occur by evaluating the resulting code (and running the functions).

Ontology Creation

In my previous blog posts about serializing RDF data as Clojure code, I noted that the properties, classes and datatypes that I was referring to in those blog posts were to be defined elsewhere in the Clojure application and that I would cover it in another blog post. Here it is.

All of the ontology properties, classes and datatypes that we are using to serialize the RDF data are defined as Clojure code. They can be defined in a library, directly in your application’s code or even as data that gets emitted by a web service endpoint that you evaluate at runtime (for data that has not yet been evaluated).

In the tests I am doing, I define RDF properties as Clojure functions; the RDF classes and datatypes are normal records that comply with the same RDF serialization rules as defined for the instance records.

Some users may wonder: why is everything defined as a map but not the properties? Though each property’s RDF description is available as a map, we use it as Clojure meta-data for that function. We consider that properties are functions and not a map. As you will see below, these functions are used to validate the RDF data serialized in Clojure code. That is the reason why they are represented as Clojure functions and not as maps like everything else.

Someone could easily leverage the RDF/Clojure serialization without worrying about the ontologies. He could get the triples that describes the records without worrying about the semantics of the data as represented by the ontologies. However, if that same person would like to reason over the data that is presented to him — if he wants to make sure the data is valid and coherent –then he will require the ontologies descriptions.

Now let’s see how these ontologies are being generated.

Creating OWL Classes

As I said above, an OWL class is nothing but another record. It is described using the same rules as previously defined4. However, it is described using the OWL language and refers to a specific semantic. Creating such a class is really easy. We just have to follow the semantics of the OWL language, and the rules of RDF/Clojure serialization. For example, take this example that creates a simple FOAF person class:

[cc lang=’lisp’ line_numbers=’false’]
[raw](def foaf:+person
“The class of all the persons.”
{#’uri “http://xmlns.com/foaf/0.1/Person”
#’rdf:type #’owl:+class
#’rdfs:label “Person”
#’rdfs:comment “The class of all the persons.”})[/raw]

As you can see, we are describing the class the same way we were defining normal instance records. However, we are doing it using the OWL language.

Creating OWL Datatypes

Datatypes are also serialized like normal RDF/Clojure records; that is, just like classes. However, since the datatypes are fairly static in the way we define them, I created a simple macro called gen-datatype that can be used to generate datatypes:

[cc lang=’lisp’ line_numbers=’false’]
[raw](defmacro gen-datatype
“Create a new datatype that represents a OWL datatype class.
[name] is the name of the datatype to create.
Optional parameters are:
[:uri] this is the URI of the datatype to create
[:base] this is the URI of base XSD datatype of this new datatype
[:pattern] this is a regex pattern to use to use to validate that
a given string represent a value that belongs to that datatype
[:docstring] the docstring to use when creating this datatype”
[name & {:keys [uri base pattern docstring]}]
`(def ~name
~(str docstring)
(merge {#’rdf:type “http://www.w3.org/TR/rdf-schema#Datatype”}
(if ~uri {#’rdf.core/uri ~uri})
(if ~pattern {#’xsp:pattern ~pattern})
(if ~base {#’xsp:base ~base}))))[/raw]

You can use this macro like this:

[cc lang=’lisp’ line_numbers=’false’]
[raw](gen-datatype *full-us-phone-number
:uri “http://purl.org/ontology/foo#phone-number”
:pattern “^[0-9]{1}-[0-9]{3}-[0-9]{3}-[0-9]{4}$”
:base “http://www.w3.org/2001/XMLSchema#string”
:docstring “Datatype representing a phone US phone number”)

And it will generate a datatype like this:

[cc lang=’lisp’ line_numbers=’false’]
[raw]{#’ontologies.core/xsp:base “http://www.w3.org/2001/XMLSchema#string”
#’ontologies.core/xsp:pattern “^[0-9]{1}-[0-9]{3}-[0-9]{3}-[0-9]{4}$”
#’rdf.core/uri “http://purl.org/ontology/foo#phone-number”
#’ontologies.core/rdf:type “http://www.w3.org/TR/rdf-schema#Datatype”}[/raw]

What this datatype defines is a class of literals that represents the full version of an US phone number. I will explain how such a datatype is used to validate RDF data records below.

Creating OWL Properties

Properties are different from classes and datatypes. They are represented as functions in the RDF/Clojure serialization. I created another simple macro called gen-property to generate these OWL properties:

[cc lang=’lisp’ line_numbers=’false’]
[raw](defmacro gen-property
“Create a new property that represents a OWL property.
[name] is the name of the property/function to create. This is the name that will be
used in your Clojure code.
[:uri] this is the URI of the property to create
[:description] this is the description of the property to create
[:domain] this is the domain of the URI to create. The domain is represented by one or multiple
classes that represent that domain. If there is more than one class that represent the domain
you can specify the ^intersection-of or the ^union-of meta-data to specify if the classes
should be interpreted as a union or an intersection of the set of classes.
[:range] this is the range of the URI to create. The range is represented by one or multiple
classes that represent that range. If there is more than one class that represent the range
you can specify the ^intersection-of or the ^union-of meta-data to specify if the classes
should be interpreted as a union or an intersection of the set of classes.
[:sub-class-of] one or multiple classes that are super-classes of this class
[:equivalent-property] one or multiple classes that are equivalent classes of this class
[:is-object-property] true if the property being created is an object property
[:is-datatype-property] true if the property being created is a datatype property
[:is-annotation-property] true if the property being created is an annotation property
[:cardinality] cardinality of the property”
[name & {:keys [uri
(let [vals (gensym “label-“)
docstring (if description
(str description “.\n [” vals “] is the preferred label to specify.”)
(str “”))
type (if is-object-property
(if is-annotation-property
metadata (merge (if uri {#’rdf.core/uri uri})
(if type {#’rdf:type type})
(if label {#’iron:pref-label label})
(if description {#’iron:description description})
(if range {#’rdfs:range range})
(if domain {#’rdfs:domain domain})
(if cardinality {#’owl:cardinality cardinality}))]
`(defn ~(with-meta name metadata)
~(str docstring)
(rdf.property/validate-property #’~name ~vals))))[/raw]

Note that this macro currently only accommodates a subset of the OWL language. For example, there is no way to use the macro to specify cardinality, etc. I only created what was required for writing this blog post.

You can then use this macro to create new properties like this:

[cc lang=’lisp’ line_numbers=’false’]
[raw](gen-property foo:phone
:is-datatype-property true
:label “phone number”
:uri “http://purl.org/ontology/foo#phone”
:range *full-us-phone-number
:domain #’owl:+thing
:cardinality 1)

(gen-property foo:knows
:is-object-property true
:label “a person that knows another person”
:uri “http://purl.org/ontology/foo#knows”
:range #’umbel.ref/umbel-rc:+person
:domain #’umbel.ref/umbel-rc:+person)

Some other Classes, Datatypes and Properties

So, here is the list of classes, datatypes and properties that will be used later in this blog post for demonstrating how validation occurs in such a framework:

[cc lang=’lisp’ line_numbers=’false’]
[raw](in-ns ‘rdf.core)
(defn uri
(URI. #^String s)
(catch Exception e
(throw (IllegalStateException. (str “Invalid URI: \”” s “\””))))))

(defn datatype
(if (var? s)
(if (not= (get @s #’ontologies.core/rdf:type) “http://www.w3.org/TR/rdf-schema#Datatype”)
(throw (IllegalStateException. (str “Provided value for datatype is not a datatype: \”” s “\””))))
(throw (IllegalStateException. (str “Provided value for datatype is not a datatype: \”” s “\””)))))

(in-ns ‘ontologies.core)

(gen-property iron:pref-label
:uri “http://purl.org/ontology/iron#prefLabel”
:label “Preferred label”
:description “Preferred label for describing a resource”
:domain #’owl:+thing
:range #’rdfs:*literal
:is-datatype-property true)

(def owl:+thing
“The class of OWL individuals.”
{#’uri “http://www.w3.org/2002/07/owl#Thing”
#’rdf:type #’rdfs:+class
#’rdfs:label “Thing”
#’rdfs:comment “The class of OWL individuals.”})

(gen-datatype xsd:*string
:uri “http://www.w3.org/2001/XMLSchema#string”
:docstring “Datatypes that represents all the XSD strings”)

Concluding with Ontologies

Ontologies are easy to write in RDF/Clojure. There is a simple set of macros that can be used to help create the ontology classes, properties and datatypes. However, in the future I am anticipating to create a library that would use the OWLAPI to take any OWL ontology and to serialize it using these rules. The output could be Clojure code like this, or JAR libraries. Additionally, some investigation will be done to use more Clojure idiomatic projects like Phil Lord’s Tawny-OWL project.

RDF Data Instantiation Using Clojure Code

Now that we have the classes, datatypes and properties defined in our Clojure application, we can start defining data records like this:

[cc lang=’lisp’ line_numbers=’false’]
[raw](def valid-record (r {uri “http://foo-bar.com/test/”
rdf:type owl:+thing
foo:phone [“1-421-353-9057”]
iron:pref-label {value “Test cardinality validation”
lang “en”
datatype xsd:*string}}))

Data Validation

Now that we have all of the ontologies defined in our Clojure application, we can start to define records. Let’s start with a record called valid-record that describes something with a phone number and a preferred label. The data is there and available to you. Now, what if I would like to do a bit more than this, what if I would like to validate it?

Validating such a record is as easy as evaluating it. What does that mean? It means that each value of the map that describes the record will be evaluated by Clojure. Since each key refers to a function, then evaluating each value means that we evaluate the function and use the value as specified by the description of the record. Then we iterate over the whole map to validate all of the triples.

To perform this kind of process, we can create a validate-resource function that looks like:

[cc lang=’lisp’ line_numbers=’false’]
[raw](defn validate-resource [resource]
(doseq [[property value] resource]
(do (println (str “validating resource property: ” property))
(if (fn? @property)
(@property value)))))

You can use it like this:

[cc lang=’lisp’ line_numbers=’false’]
[raw](validate-resource valid-record)[/raw]

If no exceptions are thrown, then the record is considered valid according to the ontology specifications. Easy, no? Now let’s take a look at how this works.

If you check the gen-property macro, you will notice that every time a function is evaluated, the #'rdf.property/validate-property function is called. What this function does is to perform the validation of the property given the specified value(s). The validation is done according to the description of the property in the ontology specification. Such a validate-property looks like:

[cc lang=’lisp’ line_numbers=’false’]
[raw](defn validate-property
“Validate that the values of the property are valid according to the description of that property
[property] should be the reference to the function, like #’foo-phone
[values] are the actual values of that property”
[property values]
(validate-owl-cardinality property values)
(validate-rdfs-range property values)))

So what it does is to run a series of other functions to validate different characteristics of a property. For this blog post, we demonstrate how the following characteristics are being validated:

  1. Cardinality of a property
  2. URI validation
  3. Datatype validation
  4. Range validation when the range is a class.

Cardinality Validation

Validating the cardinality of a property means that we check if the number of values of a given property is as specified in the ontology. In this example, we validate the exact cardinality of a property. It could be extended to validate the maximum and minimum cardinalities as well.

The function that validates the cardinality is the validate-owl-cardinality function that is defined as:

[cc lang=’lisp’ line_numbers=’false’]
[raw](defn validate-owl-cardinality
[property values]
(doseq [[meta-key meta-val] (seq (meta property))]
; Only validate if there is a owl/cardinality property defined in the metadata
(if (= meta-key #’ontologies.core/owl:cardinality)
; If the value is a string, a var or a map, we check if the cardinality is 1
(if (or (string? values) (map? values) (var? values))
(if (not= meta-val 1)
(throw (IllegalStateException.
(format “CARDINALITY VALIDATION ERROR: property %s has 1 values and was expecting %d values” property meta-val))))
; If the value is an array, we validate the expected cardinality
(if (not= (count values) meta-val )
(throw (IllegalStateException.
(format “CARDINALITY VALIDATION ERROR: property %s has %d values and was expecting %d values” property (count values) meta-val))))))))[/raw]

For each property, it checks to see if the owl:cardinality property is defined. If it is, then it makes sure that the number of values for that property is valid according to what is defined in the ontology. If there is a mismatch, then the validation function will throw an exception and the validation process will stop.

Here is an example of a record that has a cardinality validation error as defined by the property (see the description of the property below):

[cc lang=’lisp’ line_numbers=’false’]
[raw](def card-validation-test (r {uri “http://foo-bar.com/test/”
rdf:type owl:+thing
foo:phone [“1-421-353-9057” “(1)-(412)-342-3246”]
iron:pref-label {value “Test cardinality validation”
lang “en”
datatype xsd:*string}}))[/raw]

[cc lang=’lisp’ line_numbers=’false’]
[raw]user> (validate-resource card-validation-test)
IllegalStateException CARDINALITY VALIDATION ERROR: property #’dataset-test.core/foo:phone has 2 values and was expecting 1 values rdf.property/validate-owl-cardinality (property.clj:36)[/raw]

URI Validation

Everything you define in RDF/Clojure has a URI. However, not every string is a valid URI. All of the URIs you may define can be validated as well. When you define a URI, you use the #'rdf.core/uri function to specify the URI. That function is defined as:

[cc lang=’lisp’ line_numbers=’false’]
[raw](defn uri
(URI. #^String s)
(catch Exception e
(throw (IllegalStateException. (str “Invalid URI: \”” s “\””))))))[/raw]

As you can see, we are using the java.net.URI function to validate the URI you are defining for your records/classes/properties/datatypes. If you make a mistake when writing a URI, then a validation error will be thrown and the validation process will stop.

Here is an example of a record that has an invalid URI:

[cc lang=’lisp’ line_numbers=’false’]
[raw](def uri-validation-test (r {uri “-http://foo-bar.com/test/”
rdf:type owl:+thing
foo:phone “1-421-353-9057”
iron:pref-label {value “Test URI validation”
lang “en”
datatype xsd:*string}}))[/raw]

[raw]user> (validate-resource uri-validation-test)
IllegalStateException Invalid URI: “-http://foo-bar.com/test/” rdf.core/uri (core.clj:16)[/raw]

Datatype Validation

In OWL, a datatype property is used to refer to literal values that belong to classes of literals (datatypes classes). A datatype class is a class that represents all the literals that belong to that class of literal values as defined by the datatype. For example, the *full-us-phone-number datatype we described above defines the class of all the literals that are full US phone numbers.

Validating the value of a property according to its datatype means that we make sure that the literal value(s) belong to that datatype. Most of the time, people will use the XSD datatypes. If custom datatypes are created, then they will be based on one of the XSD datatypes, and a regex pattern will be defined to specify how the literal should be constructed.

[cc lang=’lisp’ line_numbers=’false’]
[raw](defn validate-rdfs-range
[property values]
; If the value is a map, then validate the “value”, “lang” and “datatype” assertions
(if (map? values)
(validate-map-properties values))
(doseq [[meta-key ranges] (seq (meta property))]
; make sure a range is defined for this property
(if (= meta-key #’ontologies.core/rdfs:range)
(let [ranges (if (vector? ranges)
^:intersection-of [ranges])]
(if (true? (:intersection-of (meta ranges)))
; consider that all the values of the range is a intersection-of
(doseq [range ranges]
(if (is-datatype-property? property)
; we are checking the range of a datatype property
; @TODO here we have to change that portion to call a function that will do the validation
; according to the existing XSD types, or any custom datatype based on these core
; XSD datatypes. Just like the DVT (Dataset Validation Tool)
; For now, we simply test using a datatype that has a pattern defined.
(let [pattern (get range #’ontologies.core/xsp:pattern)]
(if pattern
; a validation pattern has been defined for this value
(if (vector? values)
; Validate all the values of the property according to this Datatype
(doseq [v values]
(validate-range-pattern v pattern ranges))
; Validate the value according to the datatype
(validate-range-pattern values pattern ranges))))
; we are checking the range of an object property
(if (vector? values)
(doseq [v values]
(validate-range-object v range property))
(validate-range-object values range property))))
; consider that all the values of the range is an union-of
(println “@TODO Ranges union validation”)))))))

(defn- validate-range-pattern
[v pattern range]
(if (string? v)
(if (nil? (re-seq (java.util.regex.Pattern/compile pattern) v))
(throw (IllegalStateException.
(format “Value \”%s\” invalid according to the definition of the datatype \”%s\”” v range))))
(if (and (map? v) (nil? (validate-map-properties v)))
(if (nil? (re-seq (java.util.regex.Pattern/compile pattern) (get v ‘value)))
(throw (IllegalStateException.
(format “Value \”%s\” invalid according to the definition of the datatype \”%s\”” v range)))))))

(defn- validate-map-properties
(doseq [[p v] m]
(if (fn? @p)
(@p v))))


What this function does is to validate the range of a property. It checks what kind of values that exist for the input property according to the RDF/Clojure specification (is it a string, a map, an array, a var, etc.?). Then it checks if the property is an object property or a datatype property. If it is a datatype property, then it checks if a range has been defined for it. If it does, then it validates the value(s) according to the datatype defined in the range of the property.

Here is an example of a few records that have different datatype validation errors:

[cc lang=’lisp’ line_numbers=’false’]
[raw](def datatype-validation-test (r {uri “http://foo-bar.com/test/”
rdf:type owl:+thing
foo:phone “1-421-353-90573”
iron:pref-label {value “Test cardinality validation”
lang “en”
datatype xsd:*string}}))
(def datatype-validation-test-2 (r {uri “http://foo-bar.com/test/”
rdf:type owl:+thing
foo:phone “1-421-353-9057”
iron:pref-label {value “Test datatype validation”
lang “en”
datatype “not-a-datatype”}}))

(def xsd:*string-not-a-datatype)

(def datatype-validation-test-3 (r {uri “http://foo-bar.com/test/”
rdf:type owl:+thing
foo:phone “1-421-353-9057”
iron:pref-label {value “Test datatype validation”
lang “en”
datatype xsd:*string-not-a-datatype}}))

(def datatype-validation-test-4 (r {uri “http://foo-bar.com/test/”
rdf:type owl:+thing
foo:phone [{value “1-421-353-9057”
datatype xsd:string-not-a-datatype}]
iron:pref-label {value “Test datatype validation”
lang “en”
datatype xsd:

[cc lang=’lisp’ line_numbers=’false’]
[raw]user> (validate-resource datatype-validation-test)
IllegalStateException Value “1-421-353-90573” invalid according to the definition of the datatype “[{#’ontologies.core/xsp:pattern “^[0-9]{1}-[0-9]{3}-[0-9]{3}-[0-9]{4}$”, #’rdf.core/uri “http://purl.org/ontology/foo#phone-number”, #’ontologies.core/rdf:type “http://www.w3.org/TR/rdf-schema#Datatype”}]” rdf.property/validate-range-pattern (property.clj:150)

user> (validate-resource datatype-validation-test-2)
IllegalStateException Provided value for datatype is not a datatype: “not-a-datatype” rdf.core/datatype (core.clj:31)

user> (validate-resource datatype-validation-test-3)
IllegalStateException Provided value for datatype is not a datatype: “#’dataset-test.core/xsd:*string-not-a-datatype” rdf.core/datatype (core.clj:30)

user> (validate-resource datatype-validation-test-4)
IllegalStateException Provided value for datatype is not a datatype: “#’dataset-test.core/xsd:*string-not-a-datatype” rdf.core/datatype (core.clj:30)

As you can see, the validate-rdfs-range is incomplete regarding datatype validation. I am still updating this function to make sure that we validate all the existing XSD datatypes. Then we have to better validate the custom datatypes to make sure that we consider their xsp:base type, etc. The code that should be created is similar to the one I created for the Data Validation Tool (which is written in PHP).

Range validation when the range is a class

Finally, let’s shows how the range of an object property can be validated. Validating the range of an object property means that we make sure that the record referenced by the object property belongs to the class of the range of the property.

For example, consider a property foo:knows that has a range that specifies that all the values of foo:knows needs to belong to the class umbel-rc:+person. This means that all of the values defined for the foo:knows property for any record needs to refer to a record that is of type umbel-rc:+person. If it is not the case, then there is a validation error.

Here is an example of a record where the foo:knows property is not properly used:

[cc lang=’lisp’ line_numbers=’false’]
[raw](def wrench (r {uri “http://foo-bar.com/test/bob”
rdf:type umbel.ref/umbel-rc:+product
iron:pref-label “The biggest wrench ever”}))

(def object-range-validation-test (r {uri “http://foo-bar.com/test/bob”
rdf:type umbel.ref/umbel-rc:+person
foo:knows wrench
iron:pref-label {value “Test object range validation”
lang “en”
datatype xsd:*string}}))[/raw]

Remember we defined the foo:knows property with the range of umbel-rc:+person. However, in the example, the reference is to a wrench record that is of type umbel-rc:+product. Thus, we get a validation error:

[cc lang=’lisp’ line_numbers=’false’]
[raw]user> (validate-resource object-range-validation-test)
IllegalStateException The resource “http://umbel.org/umbel/rc/Product” referenced by the property “#’dataset-test.core/foo:knows” does not belong to the class “#’umbel.ref/umbel-rc:+person” as defined by the range of the property rdf.property/validate-range-object (property.clj:142)[/raw]

The function that validates the ranges of the object properties is defined as:

[cc lang=’lisp’ line_numbers=’false’]
[raw](defn- validate-range-object
[r range property]
(do (println range)
(let [r (if (var? r)
(deref r)
(if (map? r)
(if (string? r)
; @TODO get the resource’s description from a dataset index
uri (get (deref (get r #’ontologies.core/rdf:type)) #’rdf.core/uri)
uri-ending (do (println uri) (if (> (.lastIndexOf uri “/”) -1)
(subs uri (inc (.lastIndexOf uri “/”)))
(str “”)))
super-classes (try
(read-string (:body (clj-http.client/get (str “http://umbel.org/ws/super-classes/” uri-ending)
{:headers {“Accept” “application/clojure”}
:throw-exceptions false})))
(catch Exception e
(eval nil)))
range-uri (get @range #’rdf.core/uri)]
(if-not (some #{range-uri} super-classes)
(throw (IllegalStateException. (str “The resource \”” uri “\” referenced by the property \”” property “\” does not belong to the class \”” range “\” as defined by the range of the property” )))))))[/raw]

Normally, this kind validation should be done using the descriptions of the loaded ontologies. However, for the benefit of this blog post, I used a different way to perform this validation. I purposefully used some UMBEL Reference Concepts as the type of the records I described. Then the object range validation function leverages the UMBEL super-classes web service endpoint to check get the super-classes of a given class.

So what this function does is to check the type of the record(s) referenced by the foo:knows property. Then it checks the type of these record(s). What needs to be validated is whether the type(s) of the referenced record is the same, or is included, in the class defined in the range of the foo:knows property.

In our example, the range is #'umbel-rc:+person. This means that the foo:knows property can only refer to umbel-rc:+person records. In the example where we have a validation error, the type of the wrench record is umbel-rc:+product. What the validation function does is to get the list of all the super classes of the umbel-rc:+product class, and check if it is a sub-class of the umbel-rc:+person class. In this case, it is not, thus an error is thrown.

What is interesting with this example is the UMBEL super-classes web service endpoint does return the list of super classes as Clojure code. Then we use the read-string function to evaluate the list before manipulating it as if it was part of the application’s code.


What is elegant with this kind RDF/Clojure serialization is that the validation of RDF data is the same as evaluating the underlying code (Data as Code). If the data is invalid, then exceptions are thrown and the validation process aborts.

One thing that I yet have to investigate with such a RDF/Clojure serialization is how the semantics of the properties, classes and datatypes could be embedded into the RDF/Clojure records such that we end up with stateful RDF records that embed their own semantic at a specific point in time. This leverage would mean that even if an ontology changes in the future, the records will still be valid according to the original ontology that was used to describe them at a specific point in time (when they got written, when they got emitted by a web service endpoint, etc.).

Also, as some of my readers pointed out with my previous blog post about this subject, the fact that I use vars to serialize the RDF triples means that the serialization won’t produce valid ClojureScript code since vars doesn’t exists in ClojureScript. Paul Gearon was proposing to use keywords as the key instead of vars. Then to get the same effect as with the vars, to use a lookup index to call the functions. This avenue will be investigated as well and should be the topic of a future blog post about this RDF/Clojure serialization.