VladimirAlexiev

Prefixes

At the beginning the query defines all prefixes it uses, including for individuals like nom:, nomBib:, nomLang:.

The ontology nomo.ttl defines an even wider set of prefixes: when loaded to GraphDB, these become repository namespaces, so they are used in result display and export, which is very useful for the end-user.

Output

VladimirAlexiev

all: sparql-anything-test-xml.ttl sparql-anything-test-html.ttl

sparql-anything-test-xml.ttl: sparql-anything.sparql test.xml
	sparql-anything.bat -q sparql-anything.sparql -v type=application/xml -v file=test.xml > sparql-anything-test-xml.ttl

sparql-anything-test-html.ttl: sparql-anything.sparql test.xml
	sparql-anything.bat -q sparql-anything.sparql -v type=text/html       -v file=test.xml > sparql-anything-test-html.ttl

VladimirAlexiev

{
  "@context": {
    "rdf"                                    : "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "cim"                                    : "http://ucaiug.org/ns/CIM#",
    "eu"                                     : "http://iec.ch/TC57/CIM100-European#",
    "dct"                                    : "http://purl.org/dc/terms/",
    "dcat"                                   : "http://www.w3.org/ns/dcat#",
    "prov"                                   : "http://www.w3.org/ns/prov#",
    "xsd"                                    : "http://www.w3.org/2001/XMLSchema#",
    "cim:Bay.VoltageLevel"                   : {"@type" : "@id"},

VladimirAlexiev

Migrating J. Paul Getty Museum Agent ID from P2432 to P12040

https://www.wikidata.org/wiki/Property:P12040

• Renamed P2432 to "J. Paul Getty Museum agent DOR ID (old)"
• New prop P12040 "J. Paul Getty Museum agent ID"

The old IDs https://www.getty.edu/art/collection/artists/377 redirect to new IDs https://www.getty.edu/art/collection/person/103JV9 . These pages include human-readable info and "APIs & other identifiers" on the bottom that lists:

• Permalink: the new prop
• DOR ID (internal digital object repository): the old prop. WD has 1054 values (about 9% of total)

VladimirAlexiev

epo:AccessTerm
epo:AcquiringCentralPurchasingBody
epo:AgentInRole
epo:AwardCriterion
epo:AwardDecision
epo:AwardEvaluationTerm
epo:Awarder
epo:AwardingCentralPurchasingBody
epo:BudgetProvider
epo:Business

VladimirAlexiev

Most CB permalinks are uppercase, but a tiny percentage include uppercase letters:

grep "[A-Z]" permalink.txt|sort>permalink-uppercase.txt
wc -l permalink.txt permalink-uppercase.txt
 2050775 permalink.txt
     272 permalink-uppercase.txt

I attach the file so it can be added as exceptions to Wikidata.

VladimirAlexiev

Representing CrunchBase IPOs in FIBO

CrunchBase has a table ipos with the following fields (see ipos-sample.csv):

field	type	descr
uuid	string	Unique identifier
name	string	Entity name
permalink	string	Suffix of url. Sometimes changes

VladimirAlexiev

Converting FIBO from RDF to Turtle

• Download FIBO 2022Q2 from https://github.com/edmcouncil/fibo/releases
• Expand to folder 2022Q2-rdf (291 .rdf files in total)
• Run rdf2ttl.sh:
  • Creates then runs mkdirs.sh, which replicates the directory structure from 2022Q2-rdf to 2022Q2-ttl
  • Creates then runs rdf2ttl-one.sh, which converts each .rdf file to a respective .ttl using Jena Riot
  • Collects and deduplicates all prefixes (@prefix lines) to prefixes.ttl (385 prefixes in total). This file is very useful until you can copy prefixed names driectly from the FIBO Viewer (edmcouncil/onto-viewer#270)
• Finally, checks that no prefix and no namespace is used twice

VladimirAlexiev

@prefix nomo:  <https://nomenclature.info/nom/ontology/> .
@prefix nomShape: <https://nomenclature.info/nom/shape/> .
@prefix owl:   <http://www.w3.org/2002/07/owl#> .
@prefix rdf:   <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix sh:    <http://www.w3.org/ns/shacl#> .
@prefix skosxl: <http://www.w3.org/2008/05/skos-xl#> .
@prefix xsd:   <http://www.w3.org/2001/XMLSchema#> .
@prefix skos:  <http://www.w3.org/2004/02/skos/core#> .
@prefix rdfs:  <http://www.w3.org/2000/01/rdf-schema#> .
@prefix nom:   <https://nomenclature.info/nom/> .

VladimirAlexiev

Crunchbase Challenge

Here's a challenge to the KG Construction CG:

• Take Crunchbase: 10.5M rows, across 18 tables, served as CSV, updated daily.
• The data of some nodes comes from multiple tables (eg Organization from organizations, org_parents, org_descriptions)
• RDFize and store the total dataset, in under 1-2 hours time
  • Using the approach described here, GraphDB 9.11 with OntoRefine takes 76-119 minutes (1.3-2 hours) depending on hardware to produce and load 138M triples (19-30k triples per second)
• Update the data daily, replacing the data of recently updated rows.
  • Using the approach described here, it takes about 15 minutes to update all of Crunchbase
• Do it with your favorite RDFization toolkit, and preferably do it declaratively