SHACL is a SHApe Constraint Language. It specifies a vocabulary (using triples) to describe the shape that data should have. The shape specifies things like the following simple requirements:

• How many triples with a specified subject and predicate should be in the repository (e.g. at least 1, at most 1, exactly 1).
• What the nature of the object of a triple with a specified subject and predicate should be (e.g. a string, an integer, etc.)

See the specification for more examples.

SHACL allows you to validate that your data is conforming to desired requirements.

For a given validation, the shapes are in the Shapes Graph (where graph means a collection of triples) and the data to be validated is in the Data Graph (again, a collection of triples). The SHACL vocabularly describes how a given shape is linked to targets in the data and also provides a way for a Data Graph to specify the Shapes Graph that should be used for validatation. The result of a SHACL validation describes whether the Data Graph conforms to the Shapes Graph and, if it does not, describes each of the failures.

Namespaces Used in this Document

Along with standard predefined namespaces (such as rdf: for <http://www.w3.org/1999/02/22-rdf-syntax-ns#> and rdfs: for <http://www.w3.org/2000/01/rdf-schema#>), the following are used in code and examples below:

prefix fr: <https://franz.com#>  
prefix sh: <http://www.w3.org/ns/shacl#>  
prefix franz: <https://franz.com/ns/allegrograph/6.6.0/>  

A Simple Example

Suppose we have a Employee class and for each Employee instance, there must be exactly one triple of the form

emp001 hasID "000-12-3456" 

where the object is the employee’s ID Number, which has the format is [3 digits]-[2 digits]-[4 digits].

This TriG file encapsulates the constraints above:

@prefix sh: <http://www.w3.org/ns/shacl#> .  
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .  
 
<https://franz.com#Shapes> {  
 <https://franz.com#EmployeeShape>  
  a sh:NodeShape ;  
  sh:targetClass <https://franz.com#Employee> ;  
  sh:property [  
    sh:path <https://franz.com#hasID> ;  
    sh:minCount 1 ;  
    sh:maxCount 1 ;  
    sh:datatype xsd:string ;  
    sh:pattern "^[0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9][0-9][0-9]$" ;  
  ] .  
} 

It says that for instances of fr:Employee (sh:targetClass <https://franz.com#Employee>), there must be exactly 1 triple with predicate (path) fr:hasID and the object of that triple must be a string with pattern [3 digits]-[2 digits]-[4 digits] (sh:pattern "^[0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9][0-9][0-9]$").

This TriG file defines the Employee class and some employee instances:

@prefix fr: <https://franz.com#> .  
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .  
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .  
 
{  
 fr:Employee  
  a rdfs:Class .  
 fr:emp001  
  a fr:Employee ;  
  fr:hasID "000-12-3456" ;  
  fr:hasID "000-77-3456" .  
 fr:emp002  
  a fr:Employee ;  
  fr:hasID "00-56-3456" .  
 fr:emp003  
  a fr:Employee .  
 } 

Recalling the requirements above, we immediately see these problems with these triples:

1. emp001 has two hasID triples.
2. The value of emp002‘s ID has the wrong format (two leading digits rather than 3).
3. emp003 does not have a hasID triple.

We load the two TriG files into our repository, and end up with the following triple set. Note that all the employee triples use the default graph and the SHACL-related triples use the graph <https://franz.com#Shapes> specified in the TriG file.

Now we use agtool shacl-validate to validate our data:

bin/agtool shacl-validate --data-graph default  --shapes-graph https://franz.com#Shapes shacl-repo-1  
Validation report:             Does not conform  
Created:                       2019-06-27T10:24:10  
Number of shapes graphs:       1  
Number of data graphs:         1  
Number of NodeShapes:          1  
Number of focus nodes checked: 3  
 
3 validation results:  
Result:  
 Focus node:           <https://franz.com#emp001>  
 Path:                 <https://franz.com#hasID>  
 Source Shape:         _:b7A1D241Ax1  
 Constraint Component: <http://www.w3.org/ns/shacl#MaxCountConstraintComponent>  
 Severity:             <http://www.w3.org/ns/shacl#Violation>  
 
Result:  
 Focus node:           <https://franz.com#emp002>  
 Path:                 <https://franz.com#hasID>  
 Value:                "00-56-3456"  
 Source Shape:         _:b7A1D241Ax1  
 Constraint Component: <http://www.w3.org/ns/shacl#PatternConstraintComponent>  
 Severity:             <http://www.w3.org/ns/shacl#Violation>  
 
Result:  
 Focus node:           <https://franz.com#emp003>  
 Path:                 <https://franz.com#hasID>  
 Source Shape:         _:b7A1D241Ax1  
 Constraint Component: <http://www.w3.org/ns/shacl#MinCountConstraintComponent>  
 Severity:             <http://www.w3.org/ns/shacl#Violation> 

The validation fails with the problems listed above. The Focus node is the subject of a triple that did not conform. Path is the predicate or a property path (predicates in this example). Value is the offending value. Source Shape is the shape that established the constraint (you must look at the shape triples to see exactly what Source Shape is requiring).

We revise our employee data with the following SPARQL expresssion, deleting one of the emp001 triples, deleting the emp002 triple and adding a new one with the correct format, and adding an emp003 triple.

prefix fr: <https://franz.com#>  
 
DELETE DATA {fr:emp002 fr:hasID "00-56-3456" } ;  
 
INSERT DATA {fr:emp002 fr:hasID "000-14-1772" } ;  
 
DELETE DATA {fr:emp001 fr:hasID "000-77-3456" } ;  
 
INSERT DATA {fr:emp003 fr:hasID "000-54-9662" } ; 

Now our employee triples are

We run the validation again and are told our data conforms:

% bin/agtool shacl-validate --data-graph default  --shapes-graph https://franz.com#Shapes shacl-repo-1  
Validation report:             Conforms  
Created:                       2019-06-27T10:32:19  
Number of shapes graphs:       1  
Number of data graphs:         1  
Number of NodeShapes:          1  
Number of focus nodes checked: 3 

When we refer to this example in the remainder of this document, it is to the un-updated (incorrect) triples.

SHACL API

The example above illustrates the SHACL steps:

1. Have a data set with triples that should conform to a shape
2. Have SHACL triples that express the desired shape
3. Run SHACL validation to determine if the data conforms

Note that SHACL validation does not modify the data being validated. Once you have the conformance report, you must modify the data to fix the conformance problems and then rerun the validation test.

The main entry point to the API is agtool shacl-validate. It takes various options and has several output choices. Online help for agtool shacl-validate is displayed by running agtool shacl-validate --help.

In order to validate triples, the system must know:

1. What tripes to examine
2. What rules (SHACL triples) to use
3. What to do with the results

Specifying what triples to examine

Two arguments to agtool shacl-validate specify the triples to evaluate: --data-graph and --focus-node. Each can be specified multiple times.

• The --data-graph argument specifies the graph value for triples to be examined. Its value must be an IRI or default. Only triples in the specified graphs will be examined. default specifies the default graph. It is also the default value of the --data-graph argument. If no value is specified for --data-graph, only triples in the default graph will be examined. If a value for --data-graph is specified, triples in the default graph will only be examined if --data-graph default is also specified.
• The --focus-node argument specifies IRIs which are subjects of triples. If this argument is specified, only triples with these subjects will be examined. To be examined, triples must also have graph values specified by --data-graph arguments. --focus-node does not have a default value. If unspecified, all triples in the specified data graphs will be examined. This argument can be specified multiple times.

The --data-graph argument was used in the simple example above. Here is how the --focus-node argument can be used to restrict validation to triples with subjects <https://franz.com#emp002>and <https://franz.com#emp003> and to ignore triples with subject <https://franz.com#emp001> (applying agtool shacl-validate to the orignal non-conformant data):

% bin/agtool shacl-validate --data-graph default  \  
  --shapes-graph https://franz.com#Shapes \  
  --focus-node https://franz.com#emp003 \  
  --focus-node https://franz.com#emp002 shacl-repo-1  
Validation report:             Does not conform  
Created:                       2019-06-27T11:37:49  
Number of shapes graphs:       1  
Number of data graphs:         1  
Number of NodeShapes:          1  
Number of focus nodes checked: 2  
 
2 validation results:  
Result:  
 Focus node:           <https://franz.com#emp003>  
 Path:                 <https://franz.com#hasID>  
 Source Shape:         _:b7A1D241Ax2  
 Constraint Component: <http://www.w3.org/ns/shacl#MinCountConstraintComponent>  
 Severity:             <http://www.w3.org/ns/shacl#Violation>  
 
Result:  
 Focus node:           <https://franz.com#emp002>  
 Path:                 <https://franz.com#hasID>  
 Value:                "00-56-3456"  
 Source Shape:         _:b7A1D241Ax2  
 Constraint Component: <http://www.w3.org/ns/shacl#PatternConstraintComponent>  
 Severity:             <http://www.w3.org/ns/shacl#Violation> 

Specifying What Shape Triples to Use

Two arguments to agtool shacl-validate, analogous to the two arguments for data described above, specify Shape triples to use. Further, following the SHACL spec, data triples with predicate <http://www.w3.org/ns/shacl#shapeGraph> also specify graphs containing Shape triples to be used.

The arguments to agtool shacl-validate are the following. Each may be specified multiple times.

• The --shapes-graph argument specifies the graph value for shape triples to be used for SHACL validation. Its value must be an IRI or default. default specifies the default graph. The --shapes-graph argument has no default value. If unspecified, graphs specified by data triples with the <http://www.w3.org/ns/shacl#shapeGraph> predicate will be used (they are used whether or not --shapes-graph has a value). If --shapes-graph has no value and there are no data triples with the <http://www.w3.org/ns/shacl#shapeGraph> predicate, the data graphs are used for shape graphs. (Shape triples have a known format and so can be identified among the data triples.)
• The --shape argument specifies IRIs which are subjects of shape nodes. If this argument is specified, only shape triples with these subjects and subsiduary triples to these will be used for validation. To be included, the triples must also have graph values specified by the --shapes-graph arguments or specified by a data triple with the <http://www.w3.org/ns/shacl#shapeGraph> predicate. --shape does not have a default value. If unspecified, all shapes in the shapes graphs will be used.

Other APIs

There is a lisp API using the function validate-data-graph, defined next:

The function validation-report-conforms-p returns t or nil as the validation struct returned by validate-data-graph does or does not conform.

There is also a REST API. See HTTP reference.

Validation Output

The simple example above and the SHACL examples below show output from agtool validate-shacl. There are various output formats, specified by the --output option. Those examples use the plain format, which means printing results descriptively. Other choices include json, trig, trix, turtle, nquads, rdf-n3, rdf/xml, and ntriples. Here are the simple example (uncorrected) results using ntriples output:

% bin/agtool shacl-validate --output ntriples --data-graph default --shapes-graph https://franz.com#Shapes shacl-repo-1  
 
_:b271983AAx1 <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/shacl#ValidationReport> .  
_:b271983AAx1 <http://www.w3.org/ns/shacl#conforms> "false"^^<http://www.w3.org/2001/XMLSchema#boolean> .  
_:b271983AAx1 <http://purl.org/dc/terms/created> "2019-07-01T18:26:03"^^<http://www.w3.org/2001/XMLSchema#dateTime> .  
_:b271983AAx1 <http://www.w3.org/ns/shacl#result> _:b271983AAx2 .  
_:b271983AAx2 <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/shacl#ValidationResult> .  
_:b271983AAx2 <http://www.w3.org/ns/shacl#focusNode> <https://franz.com#emp001> .  
_:b271983AAx2 <http://www.w3.org/ns/shacl#resultPath> <https://franz.com#hasID> .  
_:b271983AAx2 <http://www.w3.org/ns/shacl#resultSeverity> <http://www.w3.org/ns/shacl#Violation> .  
_:b271983AAx2 <http://www.w3.org/ns/shacl#sourceConstraintComponent> <http://www.w3.org/ns/shacl#MaxCountConstraintComponent> .  
_:b271983AAx2 <http://www.w3.org/ns/shacl#sourceShape> _:b271983AAx3 .  
_:b271983AAx1 <http://www.w3.org/ns/shacl#result> _:b271983AAx4 .  
_:b271983AAx4 <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/shacl#ValidationResult> .  
_:b271983AAx4 <http://www.w3.org/ns/shacl#focusNode> <https://franz.com#emp002> .  
_:b271983AAx4 <http://www.w3.org/ns/shacl#resultPath> <https://franz.com#hasID> .  
_:b271983AAx4 <http://www.w3.org/ns/shacl#resultSeverity> <http://www.w3.org/ns/shacl#Violation> .  
_:b271983AAx4 <http://www.w3.org/ns/shacl#sourceConstraintComponent> <http://www.w3.org/ns/shacl#PatternConstraintComponent> .  
_:b271983AAx4 <http://www.w3.org/ns/shacl#sourceShape> _:b271983AAx3 .  
_:b271983AAx4 <http://www.w3.org/ns/shacl#value> "00-56-3456" .  
_:b271983AAx1 <http://www.w3.org/ns/shacl#result> _:b271983AAx5 .  
_:b271983AAx5 <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/shacl#ValidationResult> .  
_:b271983AAx5 <http://www.w3.org/ns/shacl#focusNode> <https://franz.com#emp003> .  
_:b271983AAx5 <http://www.w3.org/ns/shacl#resultPath> <https://franz.com#hasID> .  
_:b271983AAx5 <http://www.w3.org/ns/shacl#resultSeverity> <http://www.w3.org/ns/shacl#Violation> .  
_:b271983AAx5 <http://www.w3.org/ns/shacl#sourceConstraintComponent> <http://www.w3.org/ns/shacl#MinCountConstraintComponent> .  
_:b271983AAx5 <http://www.w3.org/ns/shacl#sourceShape> _:b271983AAx3 . 

You can have the triples added to the repository by specifying the --add-to-repo option true.

In the plain output information is provided about how many data graphs are examined, how many shape graphs were specified and node shapes are found, and how many focus nodes are checked. If zero focus nodes are checked, that is likely not what you want and something has gone wrong. Here we mis-spell the name of the shape graph (https://franz.com#shapes instead of https://franz.com#Shapes) and get 0 focus nodes checked:

% bin/agtool shacl-validate --data-graph default --shapes-graph https://franz.com#shapes shacl-repo-1  
Validation report:             Conforms  
Created:                       2019-06-28T10:34:22  
Number of shapes graphs:       1  
Number of data graphs:         1  
Number of NodeShapes:          0  
Number of focus nodes checked: 0  

SPARQL integration

There are two sets of magic properties defined: one checks for basic conformance and the other produces validation reports as triples:

• ?valid franz:shaclConforms ( ?dataGraph [ ?shapesGraph ] )
• ?valid franz:shaclFocusNodeConforms1 ( ?dataGraph ?nodeOrNodeCollection )
• ?valid franz:shaclFocusNodeConforms2 ( ?dataGraph ?shapesGraph ?nodeOrNodeCollection )
• ?valid franz:shaclShapeConforms1 ( ?dataGraph ?shapeOrShapeCollection [ ?nodeOrNodeCollection ] )
• ?valid franz:shaclShapeConforms2 ( ?dataGraph ?shapesGraph ?shapeOrShapeCollection [ ?nodeOrNodeCollection ] )
• (?s ?p ?o) franz:shaclValidationReport ( ?dataGraph [ ?shapesGraph ] )
• (?s ?p ?o) franz:shaclFocusNodeValidationReport1 ( ?dataGraph ?nodeOrNodeCollection ) .
• (?s ?p ?o) franz:shaclFocusNodeValidationReport2 ( ?dataGraph ?shapesGraph ?nodeOrNodeCollection ) .
• (?s ?p ?o) franz:shaclShapeValidationReport1 ( ?dataGraph ?shapeOrShapeCollection [ ?nodeOrNodeCollection ] ) .
• (?s ?p ?o) franz:shaclShapeValidationReport2 ( ?dataGraph ?shapesGraph ?shapeOrShapeCollection [ ?nodeOrNodeCollection ] ) .

In all of the above ?dataGraph and ?shapesGraph can be IRIs, the literal ‘default’, or a variable that is bound to a SPARQL collection (list or set) that was previously created with a function like https://franz.com/ns/allegrograph/6.5.0/fn#makeSPARQLList or https://franz.com/ns/allegrograph/6.5.0/fn#lookupRdfList. If a collection is used, then the SHACL processor will create a temporary RDF merge of all of the graphs in it to produce the data graph or the shapes graph.

Similarly, ?shapeOrShapeCollection and ?nodeOrNodeCollection can be bound to an IRI or a SPARQL collection. If a collection is used, then it must be bound to a list of IRIs. The SHACL processor will restrict validation to the shape(s) and focus node(s) (i.e. nodes that should be validated) specified.

The shapesGraph argument is optional in both of the shaclConforms and shaclValidationReport magic properties. If the shapesGraph is not specified, then the shapesGraph will be created by following triples in the dataGraph that use the sh:shapesGraph predicate. If there are no such triples, then the shapesGraph will be the same as the dataGraph.

For example, the following SPARQL expression

construct { ?s ?p ?o } where {  
  # form a collection of focusNodes  
  bind(<https://franz.com/ns/allegrograph/6.6.0/fn#makeSPARQLList>(  
    <http://Journal1/1942/Article25>,  
    <http://Journal1/1943>) as ?nodes)  
  (?s ?p ?o) <https://franz.com/ns/allegrograph/6.6.0/shaclShapeValidationReport1>  
    ('default' <ex://franz.com/documentShape1> ?nodes) .  
} 

would use the default graph as the Data Graph and the Shapes Graph and then validate two focus nodes against the shape <ex://franz.com/documentShape1>.

SHACL Example

We build on our simple example above. Start with a fresh repository so triples from the simple example do not interfere with this example.

We start with a TriG file with various shapes defined on some classes.

@prefix sh: <http://www.w3.org/ns/shacl#> .  
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .  
@prefix fr: <https://franz.com#> .  
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .  
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .  
 
<https://franz.com#ShapesGraph> {  
fr:EmployeeShape  
   a sh:NodeShape ;  
   sh:targetClass fr:Employee ;  
   sh:property [  
     ## Every employee must have exactly one ID  
     sh:path fr:hasID ;  
     sh:minCount 1 ;  
     sh:maxCount 1 ;  
     sh:datatype xsd:string ;  
     sh:pattern "^[0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9][0-9][0-9]$" ;  
    ] ;  
   sh:property [  
     ## Every employee is a manager or a worker  
     sh:path fr:employeeType ;  
     sh:minCount 1 ;  
     sh:maxCount 1 ;  
     sh:datatype xsd:string ;  
     sh:in ("Manager" "Worker") ;  
    ] ;  
    sh:property [  
      ## If birthyear supplied, must be 2001 or before  
      sh:path fr:birthYear ;  
      sh:maxInclusive 2001 ;  
      sh:datatype xsd:integer ;  
    ] ;  
    sh:property [  
      ## Must have a title, may have more than one  
      sh:path fr:hasTitle ;  
      sh:datatype xsd:string ;  
      sh:minCount 1 ;  
    ] ;  
 
    sh:or (  
      ## The President does not have a supervisor  
      [  
        sh:path fr:hasTitle ;  
        sh:hasValue "President" ;  
      ]  
      [  
       ## Must have a supervisor  
         sh:path fr:hasSupervisor ;  
         sh:minCount 1 ;  
         sh:maxCount 1 ;  
         sh:class fr:Employee ;  
      ]  
      ) ;  
 
    sh:or (  
      # Every employee must either have a wage or a salary  
      [  
       sh:path fr:hasSalary ;  
       sh:datatype xsd:integer ;  
       sh:minInclusive 3000 ;  
       sh:minCount 1 ;  
       sh:maxCount 1 ;  
      ]  
      [  
       sh:path fr:hasWage ;  
       sh:datatype xsd:decimal ;  
       sh:minExclusive 15.00 ;  
       sh:minCount 1 ;  
       sh:maxCount 1 ;  
      ]  
    )  
    .  
   } 

This file says the following about instances of the class fr:Employee:

1. Every employee must have exactly one ID (object of fr:hasID), a string of the form NNN-NN-NNNN where the Ns are digits (this is the simple example requirement).
2. Every employee must have exactly one fr:employeeType triple with value either “Manager” or “Worker”.
3. Employees may have a fr:birthYear triple, and if so, the value must be 2001 or earlier.
4. Employees must have a fr:hasTitle and may have more than one.
5. All employees except the one with title “President” must have a supervisor (specified with fr:hasSupervisor).
6. Every employee must either have a wage (a decimal specifying hourly pay, greater than 15.00) or a salary (an integer specifying monthly pay, greater than or equal to 3000).

Here is some employee data:

@prefix fr: <https://franz.com#> .  
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .  
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .  
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .  
 
{  
 fr:Employee  
  a rdfs:Class .  
 
 fr:emp001  
  a fr:Employee ;  
  fr:hasID "000-12-3456" ;  
  fr:hasTitle "President" ;  
  fr:employeeType "Manager" ;  
  fr:birthYear "1953"^^xsd:integer ;  
  fr:hasSalary "10000"^^xsd:integer .  
 
 fr:emp002  
  a fr:Employee ;  
  fr:hasID "000-56-3456" ;  
  fr:hasTitle "Foreman" ;  
  fr:employeeType "Worker" ;  
  fr:birthYear "1966"^^xsd:integer ;  
  fr:hasSupervisor fr:emp003 ;  
  fr:hasWage "20.20"^^xsd:decimal .  
 
 fr:emp003  
  a fr:Employee ;  
  fr:hasID "000-77-3232" ;  
  fr:hasTitle "Production Manager" ;  
  fr:employeeType "Manager" ;  
  fr:birthYear "1968"^^xsd:integer ;  
  fr:hasSupervisor fr:emp001 ;  
  fr:hasSalary "4000"^^xsd:integer .  
 
 fr:emp004  
  a fr:Employee ;  
  fr:hasID "000-88-3456" ;  
  fr:hasTitle "Fitter" ;  
  fr:employeeType "Worker" ;  
  fr:birthYear "1979"^^xsd:integer ;  
  fr:hasSupervisor fr:emp002 ;  
  fr:hasWage "17.20"^^xsd:decimal .  
 
 fr:emp005  
  a fr:Employee ;  
  fr:hasID "000-99-3492" ;  
  fr:hasTitle "Fitter" ;  
  fr:employeeType "Worker" ;  
  fr:birthYear "2000"^^xsd:integer ;  
  fr:hasWage "17.20"^^xsd:decimal .  
 
 fr:emp006  
  a fr:Employee ;  
  fr:hasID "000-78-5592" ;  
  fr:hasTitle "Filer" ;  
  fr:employeeType "Intern" ;  
  fr:birthYear "2003"^^xsd:integer ;  
  fr:hasSupervisor fr:emp002 ;  
  fr:hasWage "14.20"^^xsd:decimal .  
 
 fr:emp007  
  a fr:Employee ;  
  fr:hasID "000-77-3232" ;  
  fr:hasTitle "Sales Manager" ;  
  fr:hasTitle "Vice President" ;  
  fr:employeeType "Manager" ;  
  fr:birthYear "1962"^^xsd:integer ;  
  fr:hasSupervisor fr:emp001 ;  
  fr:hasSalary "7000"^^xsd:integer .  
 } 

Comparing these data with the requirements, we see these problems:

1. emp005 does not have a supervisor.
2. emp006 is pretty messed up, with (1) employeeType “Intern”, not an allowed value, (2) a birthYear (2003) later than the required maximum of 2001, and (3) a wage (14.40) less than the minimum (15.00).

Otherwise the data seems OK.

We load these two TriG files into an emply repository (which we have named shacl-repo-2). We specify the default graph for the data and the https://franz.com#ShapesGraph for the shapes. (Though not required, it is a good idea to specify a graph for shape data as it makes it easy to delete and reload shapes while developing.) We have 101 triples, 49 data and 52 shape. Then we run agtool shacl-validate:

% bin/agtool shacl-validate --shapes-graph https://franz.com#ShapesGraph --data-graph default shacl-repo-2 

There are four violations, as expected, one for emp005 and three for emp006.

Validation report:             Does not conform  
Created:                       2019-07-03T11:35:27  
Number of shapes graphs:       1  
Number of data graphs:         1  
Number of NodeShapes:          1  
Number of focus nodes checked: 7  
 
4 validation results:  
Result:  
 Focus node:           <https://franz.com#emp005>  
 Value:                <https://franz.com#emp005>  
 Source Shape:         <https://franz.com#EmployeeShape>  
 Constraint Component: <https://www.w3.org/ns/shacl#OrConstraintComponent>  
 Severity:             <https://www.w3.org/ns/shacl#Violation>  
 
Result:  
 Focus node:           <https://franz.com#emp006>  
 Path:                 <https://franz.com#employeeType>  
 Value:                "Intern"  
 Source Shape:         _:b19D062B9x221  
 Constraint Component: <http://www.w3.org/ns/shacl#InConstraintComponent>  
 Severity:             <http://www.w3.org/ns/shacl#Violation>  
 
Result:  
 Focus node:           <https://franz.com#emp006>  
 Path:                 <https://franz.com#birthYear>  
 Value:                "2003"^^<http://www.w3.org/2001/XMLSchema#integer>  
 Source Shape:         _:b19D062B9x225  
 Constraint Component: <http://www.w3.org/ns/shacl#MaxInclusiveConstraintComponent>  
 Severity:             <http://www.w3.org/ns/shacl#Violation>  
 
Result:  
 Focus node:           <https://franz.com#emp006>  
 Value:                <https://franz.com#emp006>  
 Source Shape:         <https://franz.com#EmployeeShape>  
 Constraint Component: <http://www.w3.org/ns/shacl#OrConstraintComponent>  
 Severity:             <http://www.w3.org/ns/shacl#Violation>  

Fixing the data is left as an exercise for the reader.

Franz Inc., an early innovator in Artificial Intelligence (AI) and leading supplier of Graph and Document Database technology for Knowledge Graphs, today announced that it has been named to Database Trends and Applications (DBTA) – 2019 Top 100 That Matter Most in Data.

“We’re excited to announce our seventh annual list, as the industry continues to grow and evolve,” remarked Thomas Hogan, Group Publisher at Database Trends and Applications. “Today, more than ever, businesses are looking to increase their efficiency, agility and ability to innovate by managing and leveraging data in new and novel ways. This list seeks to highlight those companies that have been successful in establishing themselves as unique resources for data professionals and stakeholders.”

“We are honored to receive this acknowledgement for our efforts in delivering Enterprise Knowledge Graph Solutions,” said Dr. Jans Aasman, CEO, Franz Inc. “In the past year, we have seen demand for Enterprise Knowledge Graphs take off across industries along with recognition from top technology analyst firms that Knowledge Graphs provide the critical foundation for artificial intelligence applications and predictive analytics. Our AllegroGraph Knowledge Graph Platform Solution offers a unique comprehensive approach for helping companies accelerate the creation of Enterprise Knowledge Graphs that deliver new value to their organization.”

Franz’s Knowledge Graph Platform Solution includes both technology and services for building industrial strength Knowledge Graphs based on best-of-class tools, products, knowledge, skills and experience. At the core of the solution is Franz’s graph database technology, AllegroGraph, which is utilized by dozens of the top F500 companies worldwide and enables businesses to extract sophisticated decision insights and predictive analytics from highly complex, distributed data that cannot be uncovered with conventional databases.

Franz delivers the expertise for designing ontology and taxonomy-based solutions by utilizing standards-based development processes and tools. Franz also offers data integration services from siloed data using W3C industry standard semantics, which can then be continually integrated with information that comes from other data sources. In addition, the Franz data science team provides expertise in custom algorithms to maximize data analytics and uncover hidden knowledge.

Companies Across the Globe Use Franz Knowledge Graph Solutions

Organizations in customer service, healthcare, life science, publishing and technology have relied on Franz to help develop their knowledge graph solutions.

Global B2B technology firm N3 Results has utilized Franz’s Knowledge Graph Solution to build an ‘Intelligent Sales Organization,’ which uses graph based technology for taxonomy driven entity extraction, speech recognition, machine learning and predictive analytics to improve quality of conversations, increase sales and improve business visibility.

“In a typical sales organization, the valuable content within the online chat or voice conversation between the agent and customer goes into a black hole,” said Shannon Copeland, COO of N3. “Franz helped us build a modern Intelligent Sales Organization (ISO) by creating a real-time Knowledge Graph that knows everything about customers and agents and provides the raw data for machine learning to improve doing the business of ISO. Now we use the rich information between agents and customers to improve the quality of the interaction in real time, which ultimately creates more sales and provides far better analytics for management.”

In 2015, Dr. Parsa Mirhaji, his colleagues and industry partners, including Franz Inc. embarked on a project to bring Knowledge Graph technology to Montefiore, a Bronx-based medical center. “Our strategy at Montefiore is to build a data-driven and evidence-based health system – essentially a learning healthcare system – that can understand its own population thoroughly, understand and improve its practices, and develop the highest quality of services for the people it serves,” said Parsa Mirhaji, MD, PhD, Director of the Center for Health Data Innovations at Montefiore and the Albert Einstein College of Medicine. “In order to accomplish that goal, we have created a system that harvests every piece of data that we can possibly find, from our own EMRs and devices to patient-generated data to socioeconomic data from the community. It’s extremely important to use anything we can find that can help us categorize our patients more accurately.” (Health IT Analytics, At Montefiore, Artificial Intelligence Becomes Key to Patient Care, September 10, 2018)

Wolters Kluwer is using graph analytic techniques to accelerate the knowledge discovery process for its clients. “What we’re really interested in is achieving insights that today take a person to analyze and that are prohibitive computationally,” said Greg Tatham, Wolters Kluwer CTO of Global Platforms. “We’re providing this live feedback. As you’re typing, we’re providing question and suggestions for you live. AllegroGraph gives us a performant way to be able to just work our way through the whole knowledge model and come up with suggestions to the user in real time.” (Datanami, How AI Boosts Human Expertise at Wolters Kluwer, June 6, 2018)

Gartner Identifies Knowledge Graphs and Semantics as Key Technologies for AI
Gartner recently recognized knowledge graphs as a key new technology in both their Hype Cycle for Artificial Intelligence and Hype Cycle for Emerging Technologies. Gartner’s Hype Cycle for Artificial Intelligence 2018 states, “The rising role of content and context for delivering insights with AI technologies, as well as recent knowledge graph offerings for AI applications have pulled knowledge graphs to the surface.”

Semantics has also been identified by Gartner as critical for effectively utilizing enterprise data assets. “Unprecedented levels of data scale and distribution are making it almost impossible for organizations to effectively exploit their data assets. Data and analytics leaders must adopt a semantic approach to their enterprise data assets or face losing the battle for competitive advantage.” (Gartner, How to Use Semantics to Drive the Business Value of Your Data, Guido De Simoni, November 27, 2018) For more information about the Gartner report, visit the Gartner Report Order Page.

About Franz Inc.
Franz Inc. is an early innovator in Artificial Intelligence (AI) and leading supplier of Semantic Graph Database technology with expert knowledge in developing and deploying Knowledge Graph solutions. The foundation for Knowledge Graphs and AI lies in the facets of semantic technology provided by AllegroGraph and Allegro CL. The ability to rapidly integrate new knowledge is the crux of the Knowledge Graph and Franz Inc. provides the key technologies and services to address your complex challenges. Franz Inc. is your Knowledge Graph technology partner.

About Database Trends and Applications
Database Trends and Applications (DBTA), published by Information Today, Inc., is a bimonthly magazine that delivers advanced trends analysis and case studies in data management and analysis developed by a team with more than 25 years of industry experience. Visit www.dbta.com for subscription information. DBTA also delivers groundbreaking market research exclusively through its Unisphere Research group.

Franz’s CEO, Jans Aasman’s recent Forbes article:

There’s a fascinating dichotomy in artificial intelligence between statistics and rules, machine learning and expert systems. Newcomers to artificial intelligence (AI) regard machine learning as innately superior to brittle rules-based systems, while  the history of this field reveals both rules and probabilistic learning are integral components of AI.

This fact is perhaps nowhere truer than in establishing explainable AI, which is central to the long-term business value of AI front-office use cases.

Granted, simple machine learning can automate backend processes. However, the full extent of deep learning or complex neural networks — which are much more accurate than basic machine learning — for mission-critical decision-making and action requires explainability.

Using rules (and rules-based systems) to explicate machine learning results creates explainable AI. Many of the far-reaching applications of AI at the enterprise level — deploying it to combat financial crimes, to predict an individual’s immediate and long-term future in health care, for example — require explainable AI that’s fair, transparent and regulatory compliant.

Rules can explain machine learning results for these purposes and others.

Read the full article at Forbes

AllegroGraph provides two ways to add metadata to triples. The first one is very similar to what typical property graph databases provide: we use the named graph of triples to store meta data about that triple. The second approach is what we have termed triple attributes. An attribute is a key/value pair associated with an individual triple. Each triple can have any number of attributes. This approach, which is built into AllegroGraph’s storage layer, is especially handy for security and bookkeeping purposes. Most of this article will discuss triple attributes but first we quickly discuss the named graph (i.e. fourth element or quad) approach.

1.0 The Named Graph for Properties

Semantic Graph Databases are actually defined by the W3C standard to store RDF as ‘Quads’ (Named Graph, Subject, Predicate, and Object).  The ‘Triple Store’ terminology has stuck even though the industry has moved on to storing quads.   We believe using the named graph approach to store metadata about triples is richer model that the property graph database method.

The best way to understand this is to give an example. Below we see two statements about Bruce weighing 105 kilos. The triple portions (subject, predicate, object) are identical but the named graphs (fourth elements) differ. They are used to provide additional information about the triples. The graph values are S1 and S2. By looking at these graphs we see that

• The author of the first triple (with graph S1) is Sophia and the author of the second (with graph S2) is Bruce (who is also the subject of the two triples).
• Sophia is 100% certain about her statement while Bruce is only 10% certain about his.

Using the named graph we can do even more than a property graph database, as the value of a graph can itself be a node, and is the subject of various triples which specify the original triple’s author, date, and certainty. Additional triples tell us the ages of the authors and the fact that the authors are married.

Here is the data displayed in Gruff, AllegroGraph’s associated triple store browser:

Using named graphs for a  triple’s metadata is a powerful tool but it does have limitations: (1) only one graph value can be associated with a triple, (2) it can be important that metadata is stored directly and physically with the triple (with named graphs, the actual metadata is usually stored in additional triples with the graph as the subject, as in the example above), and (3) named graphs have competing uses and may not be available for metadata.

2.0 The Triple Attributes approach

AllegroGraph uniquely offers a mechanism called triple attributes where a collection of user defined key/value pairs can be stored with each individual triple. The advantage of this approach is manyfold, but the original use case was designed for triple level security for an Intelligence agency.

By having triple attributes physically connected to the triples in the storage layer we can provide a very powerful and flexible mechanism to protect triples at the lowest possible level in AllegroGraph’s architecture. Our first example below shows this use case in great detail. Other use cases are for example to add weights or costs to triples, to be used in graph algorithms. Or we can add a recorded time or expiration times to a triple and use that to provide a time machine in AllegroGraph or do automatic clean-up of old data.

Example with Attributes:

      Subject – <http://dbpedia.org/resource/Arif_Babayev>
      Predicate – <http://dbpedia.org/property/placeOfDeath>
      Object – <http://dbpedia.org/resource/Baku>
      Named Graph – <http://ex#trans@@1142684573200001>
      Triple Attributes – {“securityLevel”: “high”, “department”: “hr”, “accessToken”: [“E”, “D”]}

This article provides an initial introduction to attributes and the associated concept of static filters, showing how they are set up and used. We start with a security example which also describes the basics of adding attributes to triples and filtering query results based on attribute values. Then we discuss other potential uses of attributes.

2.1 Triple Attribute Basics: a Security Example

One important purpose of attributes, when they were added as a feature, was to allow for very fine triple-level security, so that triples would be visible or invisible to users according to the attributes of the triples and the permissions associated with the query being posed by the user.

Note that users as such do not have attributes. Instead, attribute values are assigned when a query is posed. This is an important point: it is natural to think that there can be an attribute SECURITY-LEVEL, and a triple can have attribute SECURITY-LEVEL=3, and USER1 can have an attribute SECURITY-LEVEL=2 and USER2 can have an attribute SECURITY-LEVEL=4, and the system can require that the user SECURITY-LEVEL attribute must be greater than the triple SECURITY-LEVEL for the triple to be visible to the user. But that is not how attributes work. The triples can have the attribute SECURITY-LEVEL=2 but users do not have attributes. Instead, the filter is made part of the query.

Here is a simple example. We define attributes and static attribute filters using AGWebView. We have a repository named repo. Here is a portion of its AGWebView page:

The red arrow points to the commands of interest: Manage attribute definitions and Set static attribute filter. We click on Set static attribute filter to define an attribute. We have filled in the attribute information (name security-level, minimum and maximum number allowed per triple, allowed values, and whether order or not (yes in our case):

We click Save and the attribute is defined:

Then we define a filter (on the Set static attribute filter page):

We defined the filter (attribute-set> user.security-level triple.security-level) and clicked Save (the definition appears in both the Edit and the Current fields). The filter says that the “user” security level must be greater than the triple security level. We put “user” in quotes because the user security level is specified as part of the query, and has no direct connection to any specific user.

Here are some triples in a nqx file fr.nqx. The first triple has no attributes and the other three each has a security-level attribute value.

     <http://www.franz.com#emp0> <http://www.franz.com#position> “intern” .

     <http://www.franz.com#emp1> <http://www.franz.com#position> “worker” {“security-level”: “2”} .

     <http://www.franz.com#emp2> <http://www.franz.com#position> “manager” {“security-level”: “3”} .

     <http://www.franz.com#emp3> <http://www.franz.com#position> “boss” {“security-level”: “4”} .

We load this file into a repository which has the security-level attribute defined as above and the static filter mentioned above also defined. (Triples with attributes can also be entered directly when using AGWebView with the Import RDF from a text area input command).

Once the triples are loaded, we click View triples in AGWebView and we see no triples:

This result is often surprising to users just beginning to work with attributes and filters, who may expect the first triple, abbreviated to [emp0 position intern], to be visible, but the system is doing what it is supposed to do. It will only show triples where the security-level of the user posing the query is greater than the security level of the triple. The user has no security level and so the comparison fails, even with triples that have no security-level attribute value. We will describe below how to ensure you can see triples with no attributes.

So we need to specify an attribute value to the user posing the query. (As said above, users do not themselves have attribute values. But the attribute value of a user posing a query can be specified as part of the query.) “User” attributes are specified with a prefix like the following:

     prefix franzOption_userAttributes: <franz:%7B%22security-level%22%3A%223%22%7D>

so the query should be

     prefix franzOption_userAttributes: <franz:%7B%22security-level%22%3A%223%22%7D>

     select ?s ?p ?o { ?s ?p ?o . }

We will show the results below, but first what are all the % signs and numbers doing there? Why isn’t the prefix just prefix franzOption_userAttributes: <franz:{“security-level”:”3″}>? The issue is that {“security-level”:”3″} won’t read correctly. It must be URL encoded. We do this by going to https://www.urlencoder.org/ (there are other websites that do this as well) and put {“security-level”:”3″} in the first box, click Encode and get %7B%22security-level%22%3A%223%22%7D.  We then paste that into the query, as shown above.

When we try that query in AGWebView, we get one result:

If we encode {“security-level”:”5″} to get the query

prefix franzOption_userAttributes: <franz:%7B%22security-level%22%3A%225%22%7D>
select ?s ?p ?o { ?s ?p ?o . }

we get three results:

     emp3    position                “boss”
     emp2    position                “manager”
     emp1    position                “worker”

since now the “user” security-level is greater than that of any triples with a security-level attribute. But what about the triple with subject emp0, the triple with no attributes? It does not pass the filter which required that the user attribute be greater than the triple attribute. Since the triple has no attribute value so the comparison failed.

Let us redefine the filter to:

(or (attribute-set> user.security-level triple.security-level)
    (empty triple.security-level))

Now a triple will pass the filter if either (1) the “user” security-level is greater than the triple security-level or (2) the triple does not have a security-level attribute. Now the query from above where the user has attribute security-level:”5” will show all the triples with security-level less than 5 and with no attributes at all. That happens to be all four triples so far defined:

The triple

     emp0    position                “intern”

will now appears as a result in any query where it satisfies the SPARQL select regardless of the security-level of the “user”.

It would be a useful feature that we could associate attributes with actual users. However, this is not as simple as it sounds. Attributes are features of repositories. If I have a REPO1 repository, it can have a bunch of defined attributes and filters but my REPO2 may know nothing about them and its triples may not have any attributes at all, and no attributes are defined, and (as a result) no filters. But users are not repository-linked objects. While a repository can be made read-only or unreadable for a user, users do not have finer repository features. So an interface for providing users with attributes, since it would only make sense on a per-repository basis, requires a complicated interface. That is not yet implemented (though we are considering how it can be done).

Instead, users can have specific prefixes associated with them and that prefix and be included in any query made by the user.

But if all it takes to specify “user” attributes is to put the right line at the top of your SPARQL query, that does not seem to provide much security. There is a feature for users “Allow user attributes via SPARQL PREFIX franzOption_userAttributes” which can restrict a user’s ability to specify “user” attributes in a query, but that is a rather blunt instrument. Instead, the model is that most users (outside of trusted administrators) are not actually allowed to pose SPARQL queries directly. Instead, there is an intermediary program which takes the query a user requests and, having determined the status of the user and what attribute values should be given to the user, modifies the query with the appropriate franzOption_userAttributes prefixes and then sends the query on to the server, following which it captures the results and sends them back to the requesting user. That intermediate program will store the prefix suitable for a user and thus associate “user” attributes with specific users.

2.2 Using attributes as additional data

Although triple security is one powerful use of attributes, security is far from the only use. Just as the named graph can serve as additional data, so can attributes. SPARQL queries can use attribute values just as static filters can filter out triples before displaying them. Let us take a simple example: the attribute timeAdded. Every triple we add will have a timeAdded attribute value which will be a string whose contents are a datetime value, such as “2017-09-11T:15:52”. We define the attribute:

Now let us define some triples:

     <http://www.franz.com#emp0> <http://www.franz.com#callRank> “2” {“timeAdded”: “2019-01-12T10:12:45” } .
     <http://www.franz.com#emp0> <http://www.franz.com#callRank> “1” {“timeAdded”: “2019-01-14T14:16:12” } .
     <http://www.franz.com#emp0> <http://www.franz.com#callRank> “3” {“timeAdded”: “2019-01-11T11:15:52” } .
     <http://www.franz.com#emp1> <http://www.franz.com#callRank> “5” {“timeAdded”: “2019-01-13T11:03:22” } .
     <http://www.franz.com#emp0> <http://www.franz.com#callRank> “2” {“timeAdded”: “2019-01-13T09:03:22” } .

 

We have a call center with employees making calls. Each call has a ranking from 1 to 5, with 1 the lowest and 5 the highest. We have data on five calls, four from emp0 and one from emp1. Each triples has a timeAdded attribute with a string containing a dateTime value. We load these into a empty repository named at-test where the timeAdded attribute is defined as above:

 

SPARQL queries can use the attribute magic properties (see https://franz.com/agraph/support/documentation/current/triple-attributes.html#Querying-Attributes-using-SPARQL). We use the attributesNameValue magic property to see the subject, object, and attribute value:

     select ?s ?o ?value { 
       (?ta ?value) <http://franz.com/ns/allegrograph/6.2.0/attributesNameValue>    (?s ?p ?o) . 
     }

But we are really interested just in emp0 and we would like to see the results ordered by time, that is by the attribute value, so we restrict the query to emp0 as the subject and order the results:

     select ?o ?value { 
       (?ta ?value) <http://franz.com/ns/allegrograph/6.2.0/attributesNameValue>    (<http://www.franz.com#emp0> ?p ?o) . 
     }  order by ?value

There are the results for emp0, who is clearly having difficulties because the call rankings have been steadily falling over time.

Another example using timeAdded is employee salary data. In the Human Resources data, the salary of an employee is stored:

      emp0 hasSalary 50000

Now emp0 gets a raise to 55000. So we delete the triple above and add the triple

      emp0 hasSalary 55000

But that is not satisfactory because we have lost the salary history. If the boss asks “How much was emp0 paid initially?” we cannot answer. There are various solutions. We could define a salary change object, with predicates effectiveDate, previousSalary, newSalary, and so on:

     salaryChange017 forEmployee emp0
     salaryChange017 effectiveDate “2019-01-12T10:12:45”
     salaryChange017 oldSalary “50000”
     salaryChange017 newSalary “55000”

     emp0 hasSalaryChange salaryChange017

and that would work fine, but perhaps it is more setup and effort than is needed. Suppose we just have hasSalary triples each with a timeAdded attribute. Then the current salary is the latest one and the history is the ordered list. Here that idea is worked out:

<http://www.franz.com#emp0> <http://www.franz.com#hasSalary> “50000”^^<http://www.w3.org/2001/XMLSchema#integer> {“timeAdded”: “2017-01-12T10:12:45” } .
<http://www.franz.com#emp0> <http://www.franz.com#hasSalary> “55000”^^<http://www.w3.org/2001/XMLSchema#integer> {“timeAdded”: “2019-03-17T12:00:00” } .

What is the current salary? A simple SPARQL query tells us:

      select ?o ?value { 
       (?ta ?value) <http://franz.com/ns/allegrograph/6.2.0/attributesNameValue>  
                       (<http://www.franz.com#emp0> <http://www.franz.com#hasSalary> ?o) . 
        }  order by desc(?value) limit 1

 

The salary history is provided by the same query without the LIMIT:

     select ?o ?value { 
       (?ta ?value) <http://franz.com/ns/allegrograph/6.2.0/attributesNameValue>   
                      (<http://www.franz.com#emp0> <http://www.franz.com#hasSalary> ?o) . 
        }  order by desc(?value)

 

This method of storing salary data may not easily support more complex questions which might be easily answered if we went the salaryChange object route mentioned above but if you are not looking to ask those questions, you should not do the extra work (and the risk of data errors) required.

You could use the graph of each triple for the timeAdded. All the examples above would work with minor tweaks. But there are many uses for the named graph of a triple. Attributes are available and using them for one purpose does not restrict their use for other purposes.

 

According to Donald Feinberg, vice president and distinguished analyst at Gartner, the very challenge created by digital disruption — too much data — has also created an unprecedented opportunity. The vast amount of data, together with increasingly powerful processing capabilities enabled by the cloud, means it is now possible to train and execute algorithms at the large scale necessary to finally realize the full potential of AI.

“The size, complexity, distributed nature of data, speed of action and the continuous intelligence required by digital business means that rigid and centralized architectures and tools break down,” Mr. Feinberg said. “The continued survival of any business will depend upon an agile, data-centric architecture that responds to the constant rate of change.”

Gartner recommends that data and analytics leaders talk with senior business leaders about their critical business priorities and explore how the following top trends can enable them.

 

Trend No. 5: Graph

Graph analytics is a set of analytic techniques that allows for the exploration of relationships between entities of interest such as organizations, people and transactions.

The application of graph processing and graph DBMSs will grow at 100 percent annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science.

Graph data stores can efficiently model, explore and query data with complex interrelationships across data silos, but the need for specialized skills has limited their adoption to date, according to Gartner.

Graph analytics will grow in the next few years due to the need to ask complex questions across complex data, which is not always practical or even possible at scale using SQL queries.

https://www.gartner.com/en/newsroom/press-releases/2019-02-18-gartner-identifies-top-10-data-and-analytics-technolo

Algorithm-XLab – March 2019

Franz CEO Dr. Jans Aasman Explains how to manage AI Modelling Risks.

AI model risk management has moved to the forefront of contemporary concerns for statistical Artificial Intelligence, perhaps even displacing the notion of ethics in this regard because of the immediate, undesirable repercussions of tenuous machine learning and deep learning models.

AI model risk management requires taking steps to ensure that the models used in artificial applications produce results that are unbiased, equitable, and repeatable.

The objective is to ensure that given the same inputs, they produce the same outputs.

If organizations cannot prove how they got the results of AI risk models, or have results that are discriminatory, they are subject to regulatory scrutiny and penalties.

Strict regulations throughout the financial services industry in the United Statesand Europe require governing, validating, re-validating, and demonstrating the transparency of models for financial products.

There’s a growing cry for these standards in other heavily regulated industries such as healthcare, while the burgeoning Fair, Accountable, Transparent movementtypifies the horizontal demand to account for machine learning models’ results.

AI model risk management is particularly critical in finance.

Financial organizations must be able to demonstrate how they derived the offering of any financial product or service for specific customers.

When deploying AI risk models for these purposes, they must ensure they can explain (to customers and regulators) the results that determined those offers.

Read the full article at Algorithm-XLab.

Forbes – February 2019

Although you may not have heard of JavaScript Object Notation Linked Data (JSON-LD), it is already affecting your business. Search engine giant Google has mentioned JSON-LD as a preferred means of adding structured data to webpages to make them considerably easier to parse for more accurate search engine results. The Google use case is indicative of the larger capacity for JSON-LD to increase web traffic for sites and better guide users to the results they want.

Expectations are high for JSON-LD, and with good reason. It effectively delivers the many benefits of JSON, a lightweight data interchange format, into the linked data world. Linked data is the technological approach supporting the World Wide Web and one of the most effective means of sharing data ever devised.

In addition, the growing number of enterprise knowledge graphs fully exploit the potential of JSON-LD as it enables organizations to readily access data stored in document formats and a variety of semi-structured and unstructured data as well. By using this technology to link internal and external data, knowledge graphs exemplify the linked data approach underpinning the growing adoption of JSON-LD — and the demonstrable, recurring business value that linked data consistently provides.

Read the full article at Forbes.

InfoSecurity – February 2019

Dr. Jans Aasman was quoted in this article about how AllegroGraph’s Triple Attributes provide Storage Layer Security.

With horizontal standards such as the General Data Protection Regulation (GDPR) and vertical mandates like the Fair Credit Reporting Act increasing in scope and number, information security is impacted by regulatory compliance more than ever.

Organizations frequently decide between concentrating protection at the access layer via role-based security filtering, or at the storage layer with methods like encryption, masking, and tokenization.

The argument is that the former underpins data governance policy and regulatory compliance by restricting data access according to department or organizational role. However, the latter’s perceived as providing more granular security implemented at the data layer.

 

A hybrid of access based security and security at the data layer—implemented by triple attributes—can counteract the weakness of each approach with the other’s strength, resulting in information security that Franz CEO Jans Aasman characterized as “fine-grained and flexible enough” for any regulatory requirements or security model.

 

The security provided by this semantic technology is considerably enhanced by the addition of key-value pairs as JSON objects, which can be arbitrarily assigned to triples within databases. These key-value pairs provide a second security mechanism “embedded in the storage, so you cannot cheat,” Aasman remarked.

 

When implementing HIPPA standards with triple attributes, “even if you’re a doctor, you can only see a patient record if all your other attributes are okay,” Aasman mentioned.

 

“We’re talking about a very flexible mechanism where we can add any combination of key-value pairs to any triples, and have a very flexible language to specify how to use that to create flexible security models,” Aasman said.

 

Read the full article at InfoSecurity.

Dataversity –  December 2018

There’s a general consensus throughout the data ecosystem that Data Preparation is the most substantial barrier to capitalizing on data-driven processes. Whether organizations are embarking on Data Science initiatives or simply feeding any assortment of enterprise applications, the cleansing, classifying, mapping, modeling, transforming, and integrating of data is the most time honored (and time consuming) aspect of this process.

Approximately 80 percent of the work of data scientists is mired in Data Preparation, leaving roughly 20 percent of their jobs to actually exploiting data. Moreover, the contemporary focus on external sources, Big Data, social and mobile technologies has exploded the presence of semi-structured and unstructured data, which accounts for nearly 80 percent of today’s data and further slows the preparation processes.

Read the full article at Dataversity.