Talk about the use of Linked Data in historical research on census data. Has some slides about TabLInker as well (http://github.com/Data2Semantics/TabLinker). Part of the data2semantics project (http://data2semantics.org)
Encoding changing country codes in RDF with ISO 3166 and SKOSJakob .
How to encode ISO 3166 with its dynamic and substructure in a proposed way of SKOS for the Semantic Web. Presented at MTSR'07 (Second International Conference on Metadata and Semantics Research, Ionian Academy, Corfu.
Managing Metadata for Science and Technology Studies: the RISIS caseRinke Hoekstra
Presentation of our paper at the WHISE workshop at ESWC 2016 on requirements for metadata over non-public datasets for the science & technology studies field.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
Encoding changing country codes in RDF with ISO 3166 and SKOSJakob .
How to encode ISO 3166 with its dynamic and substructure in a proposed way of SKOS for the Semantic Web. Presented at MTSR'07 (Second International Conference on Metadata and Semantics Research, Ionian Academy, Corfu.
Managing Metadata for Science and Technology Studies: the RISIS caseRinke Hoekstra
Presentation of our paper at the WHISE workshop at ESWC 2016 on requirements for metadata over non-public datasets for the science & technology studies field.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
Linkitup: Link Discovery for Research DataRinke Hoekstra
Linkitup is a Web-based dashboard for enrichment of research output published via industry grade data repository services. It takes metadata entered through Figshare.com and tries to find equivalent terms, categories, persons or entities on the Linked Data cloud and several Web 2.0 services. It extracts references from publications, and tries to find the corresponding Digital Object Identifier (DOI). Linkitup feeds the enriched metadata back as links to the original article in the repository, but also builds a RDF representation of the metadata that can be downloaded separately, or published as research output in its own right. In this paper, we compare Linkitup to the standard workflow of publishing linked data, and show that it significantly lowers the threshold for publishing linked research data.
A Network Analysis of Dutch Regulations - Using the Metalex Document ServerRinke Hoekstra
In this paper we explore the possibilities of using the Linked Data representation of all Dutch regulations stored in the MetaLex Doc- ument Server for the purposes of network analysis over the citation graph between regulations, both at the document level, and at the article level. We show that this is possible using relatively straightforward SPARQL queries, and present preliminary results of the analysis.
A Network Analysis of Dutch Regulations. Rinke Hoekstra. figshare.
http://dx.doi.org/10.6084/m9.figshare.689880
Retrieved 11:12, Oct 07, 2013 (GMT)
This presentation describes the use by Data2Semantics (http://www.data2semantics.org) of the VIVO portal (http://vivoweb.org) for interlinking researchers contributing to projects within the COMMIT programme (http://www.commit-nl.nl).
The Data2Semantics project (COMMIT P23) is all about enriching research data, and making it more reusable for future research. Using Linked Data for this task is a fairly obvious step to make (surprise!). However, there are several shortcomings the current practices in publishing Linked Data, that calls for a slightly
different approach which (hopefully) bridges a gap between Web 2.0 and Web 3.0. I will present a proof-of-concept service (Linkitup) that works on top of existing scientific data repositories, and allows individual researchers to enrich their data with additional (linked) metadata.
Presentatie voor de Belastingdienst in het kader van een onderzoek naar de (on)mogelijkheden rond het herkennen en extraheren van concepten en hun definities, en het representeren daarvan met Semantic Web standaarden.
History of Knowledge Representation (SIKS Course 2010)Rinke Hoekstra
The goal of AI research is the simulation and approximation of human intelligence by computers. To a large extent this comes down to the development of computational reasoning services that allow machines to solve problems. Robots are the stereotypical example: imagine what a robot needs to know before it is able to interact with the world the way we do? It needs to have a highly accurate internal representation of reality. It needs to turn perception into action, know how to reach its goals, what objects it can use to its advantage, what kinds of objects exist, etc.
The field of knowledge representation (KR) tries to deal with the problems surrounding the incorporation of some body of knowledge (in whatever form) in a computer system, for the purpose of automated, intelligent reasoning. In this sense, knowledge representation is the basic research topic in AI. Any artificial intelligence is dependent on knowledge, and thus on a representation of that knowledge. The history of knowledge representation has been nothing less than turbulent. The roller coaster of promise of the 50's and 60's, the heated debates of the 70's, the decline and realism of the 80's and the ontology and knowledge management hype of the 90's each left a clear mark on contemporary knowledge representation technology and its application.
Presentatie over het publiceren van overheidsdata als linked data. Met nadruk op hoe context-afhankelijkheid hierbij gerespecteerd kan blijven.
Gehouden voor een groep mensen van (Bureau) Forum Standaardisatie, Novay, ICTU/eOverheid voor burgers, Information Dynamics en de Vrije Universiteit
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Linkitup: Link Discovery for Research DataRinke Hoekstra
Linkitup is a Web-based dashboard for enrichment of research output published via industry grade data repository services. It takes metadata entered through Figshare.com and tries to find equivalent terms, categories, persons or entities on the Linked Data cloud and several Web 2.0 services. It extracts references from publications, and tries to find the corresponding Digital Object Identifier (DOI). Linkitup feeds the enriched metadata back as links to the original article in the repository, but also builds a RDF representation of the metadata that can be downloaded separately, or published as research output in its own right. In this paper, we compare Linkitup to the standard workflow of publishing linked data, and show that it significantly lowers the threshold for publishing linked research data.
A Network Analysis of Dutch Regulations - Using the Metalex Document ServerRinke Hoekstra
In this paper we explore the possibilities of using the Linked Data representation of all Dutch regulations stored in the MetaLex Doc- ument Server for the purposes of network analysis over the citation graph between regulations, both at the document level, and at the article level. We show that this is possible using relatively straightforward SPARQL queries, and present preliminary results of the analysis.
A Network Analysis of Dutch Regulations. Rinke Hoekstra. figshare.
http://dx.doi.org/10.6084/m9.figshare.689880
Retrieved 11:12, Oct 07, 2013 (GMT)
This presentation describes the use by Data2Semantics (http://www.data2semantics.org) of the VIVO portal (http://vivoweb.org) for interlinking researchers contributing to projects within the COMMIT programme (http://www.commit-nl.nl).
The Data2Semantics project (COMMIT P23) is all about enriching research data, and making it more reusable for future research. Using Linked Data for this task is a fairly obvious step to make (surprise!). However, there are several shortcomings the current practices in publishing Linked Data, that calls for a slightly
different approach which (hopefully) bridges a gap between Web 2.0 and Web 3.0. I will present a proof-of-concept service (Linkitup) that works on top of existing scientific data repositories, and allows individual researchers to enrich their data with additional (linked) metadata.
Presentatie voor de Belastingdienst in het kader van een onderzoek naar de (on)mogelijkheden rond het herkennen en extraheren van concepten en hun definities, en het representeren daarvan met Semantic Web standaarden.
History of Knowledge Representation (SIKS Course 2010)Rinke Hoekstra
The goal of AI research is the simulation and approximation of human intelligence by computers. To a large extent this comes down to the development of computational reasoning services that allow machines to solve problems. Robots are the stereotypical example: imagine what a robot needs to know before it is able to interact with the world the way we do? It needs to have a highly accurate internal representation of reality. It needs to turn perception into action, know how to reach its goals, what objects it can use to its advantage, what kinds of objects exist, etc.
The field of knowledge representation (KR) tries to deal with the problems surrounding the incorporation of some body of knowledge (in whatever form) in a computer system, for the purpose of automated, intelligent reasoning. In this sense, knowledge representation is the basic research topic in AI. Any artificial intelligence is dependent on knowledge, and thus on a representation of that knowledge. The history of knowledge representation has been nothing less than turbulent. The roller coaster of promise of the 50's and 60's, the heated debates of the 70's, the decline and realism of the 80's and the ontology and knowledge management hype of the 90's each left a clear mark on contemporary knowledge representation technology and its application.
Presentatie over het publiceren van overheidsdata als linked data. Met nadruk op hoe context-afhankelijkheid hierbij gerespecteerd kan blijven.
Gehouden voor een groep mensen van (Bureau) Forum Standaardisatie, Novay, ICTU/eOverheid voor burgers, Information Dynamics en de Vrije Universiteit
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Accelerate your Kubernetes clusters with Varnish Caching
Linked Census Data
1. Linked Census Data
Rinke Hoekstra
CEDAR Kickoff, 26 January 2012
donderdag 26 januari 12
2. Overview
“Can Linked Data make a difference for historical analysis?”
Problem
Procedure (as I understand it)
Step-by-step
Vocabularies, tools
Conclusion
donderdag 26 januari 12
3. Problem
~519 Excel spreadsheets (more?... I heard 1200)
Want to do analysis over time and space, but...
Structure
Excel sheets cannot be readily imported in a database
Contents
Excel sheets are not normalised (age) nor harmonised (occupations/places)
Excel sheets contain errors (both original and data-entry)
Want to preserve all stages of data cleansing/harmonisation
donderdag 26 januari 12
4. Procedure
Verbatim import of sheets to
Archiving database/triple store
Correcting/ Add missing information (headers)
Documenting
Interpreting Add corrected information (data)
Normalising Interpret and correct objective information
Link information across sheets
Harmonising Link information to other datasets (e.g. locations)
Visualising Build (generic) visualisations of results
donderdag 26 januari 12
5. ... a bit about Linked Data
“Just another Data Model”
RDF ≠ Ontology (OWL)
RDF ≠ Taxonomy (RDFS/SKOS)
Globally Unique Identifiers (URI) for all entities
Dereferencable on the Web (URI = URL)
HTTP-accessible databases (triple stores, SPARQL)
Triples all the way <subject,
predicate,
object>
donderdag 26 januari 12
6. Spreadsheet ≠ Database
Primary Keys are entities
Column names are attributes
Cell values are attribute values
Secondary keys are relations to
other entities
donderdag 26 januari 12
7. Spreadsheet ≠ Database
Primary Keys are entities
Column names are attributes
Cell values are attribute values
Secondary keys are relations to
other entities
donderdag 26 januari 12
8. Spreadsheet ≠ Database
Primary Keys are entities
Column names are attributes
Cell values are attribute values
Secondary keys are relations to
other entities
donderdag 26 januari 12
9. Spreadsheet ≠ Database
No Primary Keys!
Anything can be an entity
Column headers are “types”
Row headers are “types”
Hierarchies!
Cell values are entity “values”
No relations to other entities
donderdag 26 januari 12
10. Anatomy of a Spreadsheet
Workbook
Cell Cell Cell
Sheet Cell Cell Cell
Cell Cell Cell
Cell Cell Cell
Sheet Cell Cell Cell
Cell Cell Cell
donderdag 26 januari 12
11. Anatomy of a Spreadsheet
Workbook1.xls
Sheet1:A1 Sheet1:B1 Sheet1:C1
Sheet1 Sheet1:A2 Sheet1:B2 Sheet1:C2
... ... ...
Sheet2:A1 Sheet2:B1 Sheet2:C1
Sheet2 Sheet2:A2 Sheet2:B2 Sheet2:C2
... ... ...
donderdag 26 januari 12
12. Anatomy of a Spreadsheet
Workbook1.xls
workers agriculture 12
Sheet1 industry 6
... ...
diamond
A 34
cutters
Sheet2 B 67
... ... ...
donderdag 26 januari 12
13. Anatomy of a Spreadsheet
Workbook1.xls
workers agriculture 12
Sheet1 industry 6
... ...
diamond
A 34
cutters
Sheet2 B 67
... ... ...
NB: all URIs scoped to sheet!
donderdag 26 januari 12
14. Data Cube
How to best represent numeric data, in a flexible way?
SDMX (Eurostat, World Bank, CBS, etc.)
Every data item is an observation
Every observation has a value
Every observation has one or more dimensions
donderdag 26 januari 12
15. Data Cube
How to best represent numeric data, in a flexible way?
SDMX (Eurostat, World Bank, CBS, etc.)
Every data item is an observation
Every observation has a value
Every observation has one or more dimensions
donderdag 26 januari 12
16. Data Cube
How to best represent numeric data, in a flexible way? 12
1878
SDMX (Eurostat, World Bank, CBS, etc.) M
O
I
leeftijd
nummer der beroepsklasse geboortejaar
Every data item is an observation geslacht
huwelijkse staat
E pannenbakkers
Every observation has a value beroep
positie
D 1
Every observation has one or more dimensions letter der beroepsklasse
donderdag 26 januari 12
17. Data Cube
How to best represent numeric data, in a flexible way? 12
1878
SDMX (Eurostat, World Bank, CBS, etc.) M
O
I
leeftijd ?
nummer der beroepsklasse ?
geboortejaar
Every data item is an observation ?
geslacht
?
huwelijkse staat
E pannenbakkers
Every observation has a value beroep
positie
D 1
Every observation has one or more dimensions letter der beroepsklasse
donderdag 26 januari 12
18. Anatomy of a Spreadsheet
Properties Headers
RowHeaders Data
donderdag 26 januari 12
19. Anatomy of a Spreadsheet
Properties Headers
RowHeaders Data
donderdag 26 januari 12
20. Anatomy of a Spreadsheet
Properties Headers
RowHeaders Data
http://github.com/Data2Semantics/TabLinker
donderdag 26 januari 12
23. What TabLinker can’t do
Annotations
“footnote”-style on separate sheet
Interpret functions
e.g. automatic sums
Integrate/harmonise across sheets/files
Additional useful functionality:
“checksum” functionality
Export to database tables
donderdag 26 januari 12
27. Harmonising
I
skos:broader
skos:broader
skos:broader
D E A
skos:broader skos:broader skos:broader
skos:broader
Fabricage van
Fabricage van steen aardewerk (incl.
Fabricage van Fabricage van dakpannen
(molensteen, steenbakkers, porcelein, terracotta,
kalk (pannenbakkers)
tegelbakkers) kachelbakkers,
pottenbakkers, enz.)
donderdag 26 januari 12
28. Harmonising
I
skos:broader
skos:broader
skos:broader
D E A
skos:broader skos:broader skos:broader
skos:broader
Fabricage van
Fabricage van steen aardewerk (incl.
Fabricage van Fabricage van dakpannen
(molensteen, steenbakkers, porcelein, terracotta,
kalk (pannenbakkers)
tegelbakkers) kachelbakkers,
pottenbakkers, enz.)
skos:exactMatch skos:broadMatch skos:broadMatch skos:closeMatch
skos:exactMatch skos:exactMatch
skos:exactMatch
HISCO:23811 HISCO:25281 HISCO:25281 HISCO:26345
HISCO:23810 HISCO:25281 HISCO:26340
donderdag 26 januari 12
29. Harmonising
I
skos:broader
skos:broader
skos:broader
D E A
skos:broader skos:broader skos:broader
skos:broader
Fabricage van
Fabricage van steen aardewerk (incl.
Fabricage van Fabricage van dakpannen
(molensteen, steenbakkers, porcelein, terracotta,
kalk (pannenbakkers)
tegelbakkers) kachelbakkers,
pottenbakkers, enz.)
Sheet1:I
skos:broader skos:broader
skos:broader
Sheet1:D Sheet1:E Sheet1:A
skos:broader skos:broader skos:broader
skos:broader
Sheet1:Fabricage van
Sheet1:Fabricage van steen Sheet1:Fabricage van aardewerk (incl.
Sheet1:Fabricage
(molensteen, steenbakkers, dakpannen porcelein, terracotta,
van kalk
tegelbakkers) (pannenbakkers) kachelbakkers,
pottenbakkers, enz.)
donderdag 26 januari 12
30. I
skos:broader
skos:broader
skos:broader
D E A
1889 skos:broader
skos:broader skos:broader skos:broader
Fabricage van
Fabricage van steen aardewerk (incl.
Fabricage van Fabricage van dakpannen
(molensteen, steenbakkers, porcelein, terracotta,
kalk (pannenbakkers)
tegelbakkers) kachelbakkers,
pottenbakkers, enz.)
skos:narrowMatch I skos:closeMatch
skos:exactMatch
skos:narrowMatch
skos:broader
skos:broader
skos:broader
D E A
skos:broader skos:broader skos:broader 1899
skos:broader
Fabricage van
Fabricage van steen aardewerk (incl.
Fabricage van Fabricage van dakpannen
(steenbakkers, porcelein,
kalk (pannenbakkers)
tegelbakkers) kachelbakkers,
pottenbakkers, enz.)
donderdag 26 januari 12
31. I
Is SKOS sufficient?
skos:broader
skos:broader
skos:broader
D E A
1889 skos:broader
skos:broader skos:broader skos:broader
Fabricage van
Fabricage van steen aardewerk (incl.
Fabricage van Fabricage van dakpannen
(molensteen, steenbakkers, porcelein, terracotta,
kalk (pannenbakkers)
tegelbakkers) kachelbakkers,
pottenbakkers, enz.)
skos:narrowMatch I skos:closeMatch
skos:exactMatch
skos:narrowMatch
skos:broader
skos:broader
skos:broader
D E A
skos:broader skos:broader skos:broader 1899
skos:broader
Fabricage van
Fabricage van steen aardewerk (incl.
Fabricage van Fabricage van dakpannen
(steenbakkers, porcelein,
kalk (pannenbakkers)
tegelbakkers) kachelbakkers,
pottenbakkers, enz.)
NB: These are not strings, but globally unique URIs, scoped within their spreadsheet (graph!) of origin.
donderdag 26 januari 12
32. I
Is SKOS sufficient?
skos:broader
skos:broader
skos:broader
D E A
1889 skos:broader
skos:broader skos:broader skos:broader
Fabricage van
Fabricage van steen aardewerk (incl.
Fabricage van Fabricage van dakpannen
(molensteen, steenbakkers, porcelein, terracotta,
kalk (pannenbakkers)
tegelbakkers) kachelbakkers,
pottenbakkers, enz.)
skos:narrowMatch I skos:closeMatch
skos:exactMatch
skos:narrowMatch
skos:broader
skos:broader
skos:broader
D E A
skos:broader skos:broader skos:broader 1899
skos:broader
Fabricage van
Fabricage van steen aardewerk (incl.
Fabricage van Fabricage van dakpannen
(steenbakkers, porcelein,
kalk (pannenbakkers)
tegelbakkers) kachelbakkers,
pottenbakkers, enz.)
NB: These are not strings, but globally unique URIs, scoped within their spreadsheet (graph!) of origin.
donderdag 26 januari 12
33. I
Is SKOS sufficient?
skos:broader
skos:broader
skos:broader
D E A
1889 skos:broader
skos:broader skos:broader skos:broader
Fabricage van
Fabricage van steen aardewerk (incl.
Fabricage van Fabricage van dakpannen
(molensteen, steenbakkers, porcelein, terracotta,
kalk (pannenbakkers)
tegelbakkers) kachelbakkers,
pottenbakkers, enz.)
skos:narrowMatch I skos:closeMatch
skos:exactMatch
skos:narrowMatch
skos:broader
skos:broader
skos:broader
D E A
skos:broader skos:broader skos:broader 1899
skos:broader
Fabricage van
Fabricage van steen aardewerk (incl.
Fabricage van Fabricage van dakpannen
(steenbakkers, porcelein,
kalk (pannenbakkers)
tegelbakkers) kachelbakkers,
pottenbakkers, enz.)
NB: These are not strings, but globally unique URIs, scoped within their spreadsheet (graph!) of origin.
donderdag 26 januari 12
34. Vocabularies, Tools
Vocabularies
Data Cube, SKOS, W3C Time, PROV-O
Excel + TabLinker
Semi-automatic conversion of Excel sheets to RDF
ProvTracer
Create PROV-O provenance trail for shell/python scripts
Visualization Prototype
SGVizler (SPARQL + Google Graph API)
donderdag 26 januari 12
35. Discussion
Advantages of Linked Data approach
Straightforward transformation from spreadsheets
Seamless integration of original, corrected and harmonised data
Ingestion of external (linked) data
Powerful documentation (provenance)
Everything is transparently query-able (SPARQL)
.... on the Web
donderdag 26 januari 12
36. Discussion
Disadvantages of Linked Data approach (subject to research)
Size? (300k * 519 sheets = 156M triples)
Only rudimentary support for arithmetical operations in queries
No dynamic/conditional ‘view’-like graphs
donderdag 26 januari 12
37. SPARQL vs. SQL?
Middle ground?
Expose database through D2RQ
donderdag 26 januari 12