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Montolo restriction type definitions and measures: DOI

The current MontoloStats dataset: DOI

Montolo

To understand how current RDF-based ontologies are modeled we created Montolo which currently describes concepts related to Restrictions in ontologies. Ontologies which are built with the RDF framework consist of concepts and relationship between these concepts. Additionally several restrictions in the form of axioms can be defined, using terms of the RDFS and OWL vocabulary.

The montolo.ttl dataset is created by YARRRML rules and the data of the montolo-raw directory.

MontoloStats is a dataset containing statistics about ontology modeling described using the MontoloVoc vocabulary based on W3C PROV and W3C DataCube. So far MontoloStats contains statistics for 660 (98%) LOV and 565 BioPortal ontologies.

MontoloSHACLStats is a dataset containing similar statistics but for the use of SHACL constraints instead of OWL axioms. Currently it contains statistics for a few data shapes hostd on GitHub and common data shapes such as schema.org-SHACL and SHACL-of-SHACL.

This repository contains code to download ontologies, create the MontoloStats dataset and perform analyses.

Creation of MontoloStats

Download ontologies

LOV

First you have to download a current list of LOV ontologies and then you can download all versions and filter for most recent versions.

# Get a list of ontologies listed in LOV
curl https://lov.linkeddata.es/dataset/lov/api/v2/vocabulary/list > lov_list.json

# Install needed nodejs modules
npm install 

# Download LOV ontologies
export NODE_OPTIONS=--max_old_space_size=4096
mkdir ontologies/2019-07-19_lov-all
node get-lov-ontologies.js lov_list.json ontologies/2019-07-19_lov-all

# Filter most current ontology version
mkdir ontologies/2019-07-19_lov-current
node get-latest-ontologies.js ontologies/2019-07-19_lov-all ontologies/2019-07-19_lov-current

BioPortal

First you have to download a current list of BioPortal ontologies, usually these ontologies are either in OWL or OBO format. However, in both cases a proper convertion to RDF is needed taking the OWL-to-RDF semantics into account.

We use the robot tool for this task, afterwards the RDF data can be transformed using N3.js to any other RDF serialization (in our case n-triples needed for LODStats).

todo: add script to repo

Compute statistics

The statistics are computed using a LODStats extension and are semantically described.

# Create stats for LOV
todo: add script to repo

# Create stats for BioPortal
todo: add script to repo

# Combine results in a single turtle file
# Create a single file from all made observations
cd analysis
python combine-results.py -i ../stats/lov_2019-07-16 -i ../stats/bioportal_2019-07-16 -o ../stats/MontoloStats.ttl -f 'turtle'

Analysis of MontoloStats

The dataset is described using a W3C PROV and W3C DataCube-based vocabulary and thus can be analyzed using SPARQL queries.

# Optional:
# Create a csv file with all observations to further analyze the observations programatically, e.g. using R.
python create-observations-csv.py -i ../stats/MontoloStats.ttl -o montolo-observations.csv

The montolo file contains the descriptions of several restriction types, restriction type expressions and restriction type measures. Statistical observations made based on MontoloVoc, should link to a corresponding data structure definition as defined by RDF Data Cube (see following listing). We also provide several such data structures in the montolo file.

@prefix mon: <https://w3id.org/montolo/ns/montolo#> .
@prefix mov: <https://w3id.org/montolo/ns/montolo-voc#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix qb: <http://purl.org/linked-data/cube#> .

# Created statistics, described using the RDF Data Cube compliant LOVCube vocabulary
# The observation links to a corresponding RDF Data Cube dataset (described below)
[] a qb:Observation, prov:Entity, mov:RestrictionTypeStatistic ;
    mon:detectorDimension mon:maximumUnqualifiedCardinalityDetector ;
    mon:detectorVersionDimension mon:maximumUnqualifiedCardinalityLODStatsDetectorOwl-v1 ;
    mon:executionTimeDimension "2019-04-05T11:48:21.845150"^^xsd:dateTime ;
    mon:ontologyRepositoryDimension mon:lov ;
    mon:ontologyVersionDimension prov: ;
    mon:restrictionTypeDimension mon:maximumUnqualifiedCardinality ;
    mon:restrictionTypeOccurrence 1 ;
    qb:dataSet _:N740f60a3437f4b46869218f604ee20e4 ;
    prov:qualifiedGeneration _:Nd459003b47e54c04ae84a21707a1460b .

_:Nd92c84d33f9144549dd1fdfc1b98b620 a prov:Activity .

_:Nd459003b47e54c04ae84a21707a1460b a prov:Generation,
        prov:InstantaneousEvent ;
    prov:activity _:Nd92c84d33f9144549dd1fdfc1b98b620 ;
    prov:atTime "2019-04-05T11:48:21.845150"^^xsd:dateTime .

# A RDF Data Cube dataset which refers to a data structure definition, defined in this repository (montolo.ttl)
_:N740f60a3437f4b46869218f604ee20e4 a qb:DataSet,
        prov:Entity,
        mov:Dataset ;
    qb:structure mon:restrictionTypesAmount ;
    prov:qualifiedGeneration _:Nd459003b47e54c04ae84a21707a1460b ;
    prov:wasGeneratedBy _:Nd92c84d33f9144549dd1fdfc1b98b620 .

How to use?

On https://w3id.org/montolo/data/montolo-stats (alternatively here the existing MontoloStats dataset can be queried. It was created based on 98% of LOV and 97% BioPortal ontologies.

Example SPARQL query to get restriction types and correpsonding occurrence measure

PREFIX qb: <http://purl.org/linked-data/cube#>
PREFIX mon: <https://w3id.org/montolo/ns/montolo#>

SELECT ((strafter(str(?type), "#")) as ?typeName) (SUM(?occurrence) as ?total) ?ontology
WHERE {
  ?obs a qb:Observation ;
    mon:restrictionTypeOccurrence ?occurrence ;
    mon:restrictionTypeDimension ?type ;
    mon:detectorVersionDimension ?detectorVersion ;
    mon:ontologyRepositoryDimension ?ontologyRepository ;
    mon:ontologyVersionDimension ?ontology .
}
GROUP BY ?type ?ontology
ORDER BY ?type ASC(?total) ?ontology