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Supervision team Academy assistants
Inger Leemans Lora Aroyo Cristina-Iulia Bucur
Department of History Department of Computer Sciences Department of Computer Sciences & Bioinformatics
Chris Dijkshoorn Sebastien Valkenberg Leon Wessels
Department of Computer Sciences University Library Department of History
Programme
15.00-15.15 Coffee / Tea
15.15-15.25 Introduction of INVENiT
15.25-15.45 Introduction to search strategies
- The Rijksmuseum interface
- RDF and SPARQL queries
- Using structure in search
- New ways of opening up religious heritage
15.45-16.15 Try-out of the various demonstrators
16.15-17.00 Drinks and evaluation
Introduction of INVENiT
Inger Leemans
Department of History
INVENiT
Main goal
Creating opportunities for research by
improving the interactivity and transparency of
cultural heritage collections.
INVENiT¹
Connecting data to create a novel query
environment.
- Prints Cabinet Online
- Short Title Catalogue of the Netherlands
INVENiT²
Defining genre-specific relevance values for
linked data patterns
Introduction to search
strategies
The Rijksmuseum
interface
Leon Wessels
Academy assistant
Department of History
Starting point
- The VU University Library collection of
illustrated bibles
- The Rijksmuseum collection of online digital
heritage
The Rijksmuseum interface
The Rijksmuseum interface
Can we ‘reconstruct’ an illustrated bible by
collecting/clustering all the engravings in the
Rijksmuseum collection?
The Rijksmuseum interface
The Rijksmuseum interface
The Rijksmuseum interface
The Rijksmuseum interface
The Rijksmuseum interface
Can we determine how often Noah’s Ark is
depicted in the illustrated bibles of the
Rijksmuseum collection in a certain historical
period?
The Rijksmuseum interface
The Rijksmuseum interface
The Rijksmuseum interface
How can we (re)structure metadata to improve
future research?
RDF and SPARQL queries
Cristina-Iulia Bucur
Academy assistant
Department of Computer Sciences & Bioinformatics
Overview
Resource Description Framework - RDF
State of RDF today
- Semantic Web: vocabularies, ontologies
- Linked Data
SPARQL queries
- Basic rules
- Some examples
Resource Description Framework - RDF(1)
- A knowledge representation data model
- Framework for describing resources on the web
- Designed to be read by computers
- Data is in the form of triplets
Resource Description Framework - RDF(2)
State of RDF today
Semantic web
- Framework to share and reuse data
- Vocabularies, ontologies
- Focused on reasoning
Linked Data
- Method to publish structured data
- Connecting data from different sources
Vocabularies
- Define concepts, relationships and constraints
- Easier to read by humans
Examples:
- AAT - Art & Architecture Thesaurus
- ULAN - Union List of Artist Names
- Iconclass
- STCN - Short Title Catalogue of the Netherlands
State of RDF today - Semantic Web
State of RDF today - Semantic Web
Ontologies
- More complex and more formal concepts
- Easier to read by computers
Examples:
- RDFS - RDF Schema
- OWL - Web Ontology Language
- SKOS - Simple Knowledge Organization System
- EDM - Europeana Data Model
State of RDF today - Linked Data
Large scale
integration
of data
SPARQL
Query language for databases
- SPARQL – Sparql Protocol and Rdf Query Language
- Based on the triple model - (subject, predicate, object)
- Query over multiple datasets
SPARQL - Basic Rules
PREFIX <- declare schema used
SELECT .... <- values to be returned
FROM ... <- identify data sources
WHERE { ... FILTER ...} <- subject, predicate, object to be
matched against triples of the RDF with or without
constraints
ORDER BY ... <- how the values are to be ordered
SPARQL - An example
SPARQL - A first query
Retrieve all prints by Bernard Picart
- Rijksmuseum interface with STCN
Step 1: Find the Bernard Picart we are looking for
Step 2: Retrieve all prints by Bernard Picart
Retrieve all prints by Bernard Picart
PREFIX <- declare schema used
SELECT .... <- values to be returned
FROM ... <- identify data sources
WHERE { ... FILTER … } <- subject,
predicate, object to be matched
against triples of the RDF with or
without constraints
ORDER BY ... <- how the values are
to be ordered
Retrieve all prints by Bernard Picart
PREFIX <- declare schema used
SELECT .... <- values to be returned
FROM ... <- identify data sources
WHERE { ... FILTER … } <- subject,
predicate, object to be matched
against triples of the RDF with or
without constraints
ORDER BY ... <- how the values are
to be ordered
SELECT DISTINCT ?publicationTitle WHERE {
?publication rdf:type
<http://stcnv.data2semantics.org/resource/vocab/Pu
blication> .
?publication stcn:title ?publicationTitle .
?publication stcn:primary_author ?author .
?publication dcterms:hasPart ?print .
?author stcn:name_full ?authorName .
FILTER regex(str(?authorName), "Picart, Bernard")
}
SPARQL - A more complicated query
Show all book titles by Bernard Picart with
their Iconclass themes
Step 1: Query for a person
Step 2: Query for a book
Step 3: Query for a subject matter concept
Show all book titles by Bernard Picart with
their Iconclass themes
PREFIX <- declare schema used
SELECT .... <- values to be returned
FROM ... <- identify data sources
WHERE { ... FILTER … } <- subject,
predicate, object to be matched
against triples of the RDF with or
without constraints
ORDER BY ... <- how the values are
to be ordered
Show all book titles by Bernard Picart with
their Iconclass themes
PREFIX <- declare schema used
SELECT .... <- values to be returned
FROM ... <- identify data sources
WHERE { ... FILTER … } <- subject,
predicate, object to be matched
against triples of the RDF with or
without constraints
ORDER BY ... <- how the values are
to be ordered
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
SELECT DISTINCT ?authorName ?publicationTitle
?subjectLabel WHERE {
?author rdf:type
<http://stcnv.data2semantics.org/resource/vocab/Author> .
?author stcn:name_full ?authorName .
?publication stcn:primary_author ?author .
?publication stcn:title ?publicationTitle .
?publication dcterms:hasPart ?print .
?print dc:subject ?subject .
?subject skos:prefLabel ?subjectLabel .
FILTER regex(str(?authorName), "Picart, Bernard") .
}
Use
SPARQL
and
QUERY
ON
Chris Dijkshoorn
Department of Computer Sciences
Using structure
in search
Cluster Search
Collection: Rijksmuseum
collection
Ontology: Europeana Data Model
Semantics: Post-query
disambiguation
Audience: Museum website
visitors
Research Space
Collection: British Museum
collection
Ontology: CIDOC-CRM
Semantics: Pre-query
disambiguation
Audience: Researchers
Cluster Search
Rijksmuseum Collection
550,000 objects records
160,000 images
Links to thesaury
Graph search algorithm
Demonstrator online
http://sealinc.ops.few.vu.nl/clustersearch/cluster_search
Graph Search Algorithm
Match user query with text in database
Find paths to artwork using graph structure
Use path to create clusters
Example query: Rubens
Research Space
British Museum Collection
2,000,000 object records
1,100,000 images
Structured query formulation
Relies heavily on CIDOC-CRM ontology
Demonstrator kinda online
http://prototype.researchspace.org
Well defined classes and properties
Well defined classes and properties help formulate search queries
New ways of opening up religious
heritage at the VU University Library
Sebastien Valkenberg
VU University Library
Image database: limited data
Beeldbank
Crowdsourcing
- Stichting Vrienden UBVU
- VU Vereniging
- Donateurs HDC
Exodus twice
A print bible (illustrations with short narrations of
the original biblical stories): Taferelen der
voornaamste geschiedenissen van het Oude en
Nieuwe Testament, en andere boeken, bij de
Heilige Schrift gevoegt, door de vermaarde
kunstenaars Hoet, Houbraken, en Picart
getekent [...] en met beschrijvingen uitgebreid.
's Gravenhaage, P. de Hondt, 1728 (Translation
of: Figures de la Bible. Amsterdam, B. Picart,
1720.
A Bible with illustrations: Alle de
voornaamste historien des ouden en
nieuwen testaments. Verbeeld in
uytsteekende Konst-platen door den wyd-
beroemden Heer Romeyn de Hooge.
Amsterdam, Jacob Lindenberg, 1703.
Accurator Crowdsourcing
Try-out of the various
demonstrators
Drinks and evaluation

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Presentation of the INVENiT Expert Meeting on Monday 16 February 2015

  • 1. Supervision team Academy assistants Inger Leemans Lora Aroyo Cristina-Iulia Bucur Department of History Department of Computer Sciences Department of Computer Sciences & Bioinformatics Chris Dijkshoorn Sebastien Valkenberg Leon Wessels Department of Computer Sciences University Library Department of History
  • 2. Programme 15.00-15.15 Coffee / Tea 15.15-15.25 Introduction of INVENiT 15.25-15.45 Introduction to search strategies - The Rijksmuseum interface - RDF and SPARQL queries - Using structure in search - New ways of opening up religious heritage 15.45-16.15 Try-out of the various demonstrators 16.15-17.00 Drinks and evaluation
  • 3. Introduction of INVENiT Inger Leemans Department of History
  • 4. INVENiT Main goal Creating opportunities for research by improving the interactivity and transparency of cultural heritage collections.
  • 5. INVENiT¹ Connecting data to create a novel query environment. - Prints Cabinet Online - Short Title Catalogue of the Netherlands
  • 6. INVENiT² Defining genre-specific relevance values for linked data patterns
  • 8. The Rijksmuseum interface Leon Wessels Academy assistant Department of History
  • 9. Starting point - The VU University Library collection of illustrated bibles - The Rijksmuseum collection of online digital heritage
  • 11. The Rijksmuseum interface Can we ‘reconstruct’ an illustrated bible by collecting/clustering all the engravings in the Rijksmuseum collection?
  • 16. The Rijksmuseum interface Can we determine how often Noah’s Ark is depicted in the illustrated bibles of the Rijksmuseum collection in a certain historical period?
  • 19. The Rijksmuseum interface How can we (re)structure metadata to improve future research?
  • 20. RDF and SPARQL queries Cristina-Iulia Bucur Academy assistant Department of Computer Sciences & Bioinformatics
  • 21. Overview Resource Description Framework - RDF State of RDF today - Semantic Web: vocabularies, ontologies - Linked Data SPARQL queries - Basic rules - Some examples
  • 22. Resource Description Framework - RDF(1) - A knowledge representation data model - Framework for describing resources on the web - Designed to be read by computers - Data is in the form of triplets
  • 24. State of RDF today Semantic web - Framework to share and reuse data - Vocabularies, ontologies - Focused on reasoning Linked Data - Method to publish structured data - Connecting data from different sources
  • 25. Vocabularies - Define concepts, relationships and constraints - Easier to read by humans Examples: - AAT - Art & Architecture Thesaurus - ULAN - Union List of Artist Names - Iconclass - STCN - Short Title Catalogue of the Netherlands State of RDF today - Semantic Web
  • 26. State of RDF today - Semantic Web Ontologies - More complex and more formal concepts - Easier to read by computers Examples: - RDFS - RDF Schema - OWL - Web Ontology Language - SKOS - Simple Knowledge Organization System - EDM - Europeana Data Model
  • 27. State of RDF today - Linked Data Large scale integration of data
  • 28. SPARQL Query language for databases - SPARQL – Sparql Protocol and Rdf Query Language - Based on the triple model - (subject, predicate, object) - Query over multiple datasets
  • 29. SPARQL - Basic Rules PREFIX <- declare schema used SELECT .... <- values to be returned FROM ... <- identify data sources WHERE { ... FILTER ...} <- subject, predicate, object to be matched against triples of the RDF with or without constraints ORDER BY ... <- how the values are to be ordered
  • 30. SPARQL - An example
  • 31. SPARQL - A first query Retrieve all prints by Bernard Picart - Rijksmuseum interface with STCN Step 1: Find the Bernard Picart we are looking for Step 2: Retrieve all prints by Bernard Picart
  • 32. Retrieve all prints by Bernard Picart PREFIX <- declare schema used SELECT .... <- values to be returned FROM ... <- identify data sources WHERE { ... FILTER … } <- subject, predicate, object to be matched against triples of the RDF with or without constraints ORDER BY ... <- how the values are to be ordered
  • 33. Retrieve all prints by Bernard Picart PREFIX <- declare schema used SELECT .... <- values to be returned FROM ... <- identify data sources WHERE { ... FILTER … } <- subject, predicate, object to be matched against triples of the RDF with or without constraints ORDER BY ... <- how the values are to be ordered SELECT DISTINCT ?publicationTitle WHERE { ?publication rdf:type <http://stcnv.data2semantics.org/resource/vocab/Pu blication> . ?publication stcn:title ?publicationTitle . ?publication stcn:primary_author ?author . ?publication dcterms:hasPart ?print . ?author stcn:name_full ?authorName . FILTER regex(str(?authorName), "Picart, Bernard") }
  • 34. SPARQL - A more complicated query Show all book titles by Bernard Picart with their Iconclass themes Step 1: Query for a person Step 2: Query for a book Step 3: Query for a subject matter concept
  • 35. Show all book titles by Bernard Picart with their Iconclass themes PREFIX <- declare schema used SELECT .... <- values to be returned FROM ... <- identify data sources WHERE { ... FILTER … } <- subject, predicate, object to be matched against triples of the RDF with or without constraints ORDER BY ... <- how the values are to be ordered
  • 36. Show all book titles by Bernard Picart with their Iconclass themes PREFIX <- declare schema used SELECT .... <- values to be returned FROM ... <- identify data sources WHERE { ... FILTER … } <- subject, predicate, object to be matched against triples of the RDF with or without constraints ORDER BY ... <- how the values are to be ordered PREFIX skos: <http://www.w3.org/2004/02/skos/core#> SELECT DISTINCT ?authorName ?publicationTitle ?subjectLabel WHERE { ?author rdf:type <http://stcnv.data2semantics.org/resource/vocab/Author> . ?author stcn:name_full ?authorName . ?publication stcn:primary_author ?author . ?publication stcn:title ?publicationTitle . ?publication dcterms:hasPart ?print . ?print dc:subject ?subject . ?subject skos:prefLabel ?subjectLabel . FILTER regex(str(?authorName), "Picart, Bernard") . }
  • 38. Chris Dijkshoorn Department of Computer Sciences Using structure in search
  • 39. Cluster Search Collection: Rijksmuseum collection Ontology: Europeana Data Model Semantics: Post-query disambiguation Audience: Museum website visitors Research Space Collection: British Museum collection Ontology: CIDOC-CRM Semantics: Pre-query disambiguation Audience: Researchers
  • 40. Cluster Search Rijksmuseum Collection 550,000 objects records 160,000 images Links to thesaury Graph search algorithm Demonstrator online http://sealinc.ops.few.vu.nl/clustersearch/cluster_search
  • 41.
  • 42. Graph Search Algorithm Match user query with text in database Find paths to artwork using graph structure Use path to create clusters Example query: Rubens
  • 43. Research Space British Museum Collection 2,000,000 object records 1,100,000 images Structured query formulation Relies heavily on CIDOC-CRM ontology Demonstrator kinda online http://prototype.researchspace.org
  • 44.
  • 45. Well defined classes and properties
  • 46. Well defined classes and properties help formulate search queries
  • 47.
  • 48.
  • 49.
  • 50.
  • 51.
  • 52. New ways of opening up religious heritage at the VU University Library Sebastien Valkenberg VU University Library
  • 55. Crowdsourcing - Stichting Vrienden UBVU - VU Vereniging - Donateurs HDC
  • 56. Exodus twice A print bible (illustrations with short narrations of the original biblical stories): Taferelen der voornaamste geschiedenissen van het Oude en Nieuwe Testament, en andere boeken, bij de Heilige Schrift gevoegt, door de vermaarde kunstenaars Hoet, Houbraken, en Picart getekent [...] en met beschrijvingen uitgebreid. 's Gravenhaage, P. de Hondt, 1728 (Translation of: Figures de la Bible. Amsterdam, B. Picart, 1720. A Bible with illustrations: Alle de voornaamste historien des ouden en nieuwen testaments. Verbeeld in uytsteekende Konst-platen door den wyd- beroemden Heer Romeyn de Hooge. Amsterdam, Jacob Lindenberg, 1703.
  • 58. Try-out of the various demonstrators