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Linked Energy Data Generation
Tutorial
Filip Radulovic, María Poveda Villalón, Raúl García-Castro
{fradulovic,mpoveda,rgar...
License
• This work is licensed under the Creative Commons
Attribution – Non Commercial – Share Alike License
• You are fr...
Table of Contents
1. Introduction
2. Data preparation
3. Ontology development
4. Data generation
5. Discussion and Conclus...
Classic Web
CIA World
FactBook
Wikipedia
Data exposed
to the Web via
HTML
Slide adapted from “5min Introduction to Linked ...
Classic Web
• Typical web page
markup consists of:
• Rendering information
(e.g., font size and
colour)
• Hyper-links to r...
Classic Web
Slide adapted from “5min Introduction to Linked Data”- Olaf Hartig
Information from
single pages
can be found ...
CIA World
FactBook
MovieDB
Classic Web
Slide adapted from “5min Introduction to Linked Data”- Olaf Hartig
What about compl...
CIA World
FactBook
MovieDB
Classic Web
What about complex queries over multiple
pages / data sources?
Show me a picture of...
What do we actually want?
• Use the Web like a single global database
• Move from a Web of documents to a Web of Data
Slid...
Linked Data enables such Web of Data
Slide adapted from Boris Villazón Terrazas and “5min Introduction to Linked Data”- Ol...
The four principles (Tim Berners Lee, 2006)
1. Use URIs as names for things
2. Use HTTP URIs so that people can look up th...
“The Semantic Web is an extension of the current Web in which information is
given well-defined meaning, better enabling c...
Benefits + Cases of success
• Provide semantics  meaningful data & common understanding
•  Interoperability
• Reasoning ...
In this tutorial
“D4.1 Requirements and guidelines for energy data generation”
From READY4SmartCities project available at...
Table of Contents
1. Introduction
2. Data preparation
1. Select data source
2. Obtain access to data source
3. Analyse lic...
Select data source
• Selecting the data source that will be transformed
into Linked Data
• Steps
1. To define the requirem...
Select data source – LCC example
• Limitation to external data sources (search)
1. Requirements
• Real-world scenario in t...
Table of Contents
1. Introduction
2. Data preparation
1. Select data source
2. Obtain access to data source
3. Analyse lic...
Obtain access to data source
• Data access means
• technical means to retrieve the data
• legal rights to use the data
• I...
Obtain access to data source – LCC example
• Data set already available for download
• Available in a CSV file
20
Table of Contents
1. Introduction
2. Data preparation
1. Select data source
2. Obtain access to data source
3. Analyse lic...
Analysing licensing of the data source
• Licenses specify the legal terms under which a data
set can be used and exploited...
Analyse licensing – LCC example
23
Table of Contents
1. Introduction
2. Data preparation
1. Select data source
2. Obtain access to data source
3. Analyse lic...
Analyse data source
• Getting insight into data structure and organization
• Steps
1. To analyse the characteristics of th...
Analyse data source – LCC example
• Electricity, gas and oil consumptions as decimal
values
• 1-year intervals - 2010/11, ...
Table of Contents
1. Introduction
2. Data preparation
3. Ontology development
4. Data generation
5. Discussion and Conclus...
Ontology development - Preparation
• RDF – Resource Description Framework
• Data model
• (subject-predicate-object)
• Reso...
Ontology development
[1] Suárez-Figueroa, M.C. PhD Thesis: NeOn Methodology for Building Ontology Networks:
Specification,...
Ontology development
Ontology Requirements: refers to the activity of collecting the
requirements that the ontology should...
Ontology development – LLC example
LCC example (Data from….)
Non functional requirements specified:
• The ontology will tr...
Ontology development
Ontology term extraction to extract a glossary of terms that
may be developed.
Tools for terminology ...
Ontology development – LLC example
Site
place
Address
PostCode
Electricity
Consumption, utilization
years
time
33
Ontology development
Ontology conceptualization refers to the activity of
organizing and structuring the information (data...
Ontology development – LLC example
35
Ontology development
Ontology search refers to the activity of finding candidate
ontologies or ontology modules to be reus...
Ontology development – LLC example
Terms and synonyms
37
Ontology development
Ontology Selection refers to the activity of choosing the most suitable
ontologies or ontology module...
Ontology development – LLC example
• Domain coverage
• Schema.org for public places and provides some additional
terms and...
Ontology development
Ontology Integration. It refers to the activity of including one ontology
in another ontology. (NeOn)...
Ontology development
Ontology Enrichment It refers to the activity of extending an ontology with
new conceptual structures...
time:Interval
schema:City
ssn:Observation
ssn:observation
SamplingTime
ssn:SensorOutput
ssn:ObservationValue
ssn:hasValue
...
Ontology development
Ontology Evaluation it refers to the activity of checking the
technical quality of an ontology agains...
Ontology development – LLC example
Minor, mostly
lack of
annotations
in reused
terms.
44
Table of Contents
1. Introduction
2. Data preparation
3. Ontology development
4. Data generation
1. Data transformation
2....
Data transformation
• Transformation of the data to RDF
• Steps
1. To select the RDF serialization
• RDF/XML, Turtle, N-Tr...
Data transformation - Tools
47
Database to RDF Data streams to RDF
• morph-RDB
• D2R Server
• TopBraid Composer
• morph-st...
Data transformation – LCC example
1. Turtle syntax
2. OpenRefine + RDF extension
48
Data transformation – LCC example: OpenRefine creating project
49
Data transformation – LCC example: OpenRefine adding columns
50
Data transformation – LCC example OpenRefine adding columns
51
Data transformation – LCC example OpenRefine column transformations
52
Data transformation – LCC example OpenRefine RDF extension
53
Data transformation – LCC example OpenRefine RDF extension
54
Data transformation – LCC example OpenRefine RDF extension
55
time:Intervalssn:Observation
ssn:observation
SamplingTime
ss...
Data transformation – LCC example OpenRefine RDF extension
56
Data transformation – LCC example OpenRefine RDF extension
57
Data transformation – LCC example OpenRefine RDF extension
58
Data transformation – LCC example OpenRefine RDF extension
59
Data transformation – LCC example OpenRefine RDF generation
60
Data transformation – LCC example Evaluation
• Syntax evaluation
• Consistency with the ontologies
• Usage evaluation by r...
Table of Contents
1. Introduction
2. Data preparation
3. Ontology development
4. Data generation
1. Data transformation
2....
Data linking
• Ensuring that data are not just “isolated islands”
• Steps
1. To identify classes whose instances can be th...
Data linking – LCC example
1. Classes: City, District
2. Data sets: Dbpedia
3. Tool: OpenRefine
64
Data linking – LCC example OpenRefine reconciliation
65
Data linking – LCC example OpenRefine reconciliation
66
Data linking – LCC example OpenRefine reconciliation
67
Data linking – LCC example OpenRefine reconciliation
68
Data linking – LCC example OpenRefine reconciliation
69
Table of Contents
1. Introduction
2. Data preparation
3. Ontology development
4. Data generation
5. Discussion and Conclus...
Discussion and Conclusions
• The guidelines are based on requirements from
smart city stakeholders
• Address the broad sco...
More information
72
time:Interval
schema:City
ssn:Observation
ssn:observation
SamplingTime
ssn:SensorOutput
ssn:Observatio...
Linked Data is just data
73
01000000
electric1011
01000000
electric1112
01000000
0 20 40 60 80 100
electric1213
Building
E...
Benefits of linking data
74
resPlus$electricTotal
0e+00
2e+06
4e+06
6e+06
Total electric consumption
Original data
+ geolo...
Benefits of reasoning
resPlus
25
50
75
10
75
Total electric consumption in cultural buildings
schema:CivicStructure
Cultur...
Discussion and Conclusions
76
Discussion and Conclusions – Future work
• Development of services for facilitating the usage of
Linked Data technology
• ...
Linked Energy Data Generation
Tutorial
Filip Radulovic, María Poveda Villalón, Raúl García-Castro
{fradulovic,mpoveda,rgar...
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Linked Energy Data Generation

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Slides from our tutorial on Linked Data generation in the energy domain, presented at the Sustainable Places 2014 conference on October 2nd in Nice, France

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Linked Energy Data Generation

  1. 1. Linked Energy Data Generation Tutorial Filip Radulovic, María Poveda Villalón, Raúl García-Castro {fradulovic,mpoveda,rgarcia}@fi.upm.es ETSI Informaticos Universidad Politécnica de Madrid Campus de Montegancedo s/n 28660 Boadilla del Monte, Madrid, Spain Twitter: @LD4SC 02.10.2014. Sustainable Places 2014, Nice, France
  2. 2. License • This work is licensed under the Creative Commons Attribution – Non Commercial – Share Alike License • You are free: • to Share — to copy, distribute and transmit the work • to Remix — to adapt the work • Under the following conditions • Attribution — You must attribute the work by inserting • “[source http://www.oeg-upm.net/]” at the footer of each reused slide • a credits slide stating: “These slides are partially based on “Linked Energy Data Generation” by F. Radulovic, M. Poveda-Villalón, R. García-Castro” • Non-commercial • Share-Alike 2
  3. 3. Table of Contents 1. Introduction 2. Data preparation 3. Ontology development 4. Data generation 5. Discussion and Conclusions 3
  4. 4. Classic Web CIA World FactBook Wikipedia Data exposed to the Web via HTML Slide adapted from “5min Introduction to Linked Data”- Olaf Hartig 4
  5. 5. Classic Web • Typical web page markup consists of: • Rendering information (e.g., font size and colour) • Hyper-links to related content • Semantic content is accessible to humans but not (easily) to computers… 5
  6. 6. Classic Web Slide adapted from “5min Introduction to Linked Data”- Olaf Hartig Information from single pages can be found via search engines 6
  7. 7. CIA World FactBook MovieDB Classic Web Slide adapted from “5min Introduction to Linked Data”- Olaf Hartig What about complex queries over multiple pages / data sources? Show me a picture of the tallest building in the country with the highest CO2 emission rate in 2013 Impossible 7
  8. 8. CIA World FactBook MovieDB Classic Web What about complex queries over multiple pages / data sources? Show me a picture of the tallest building in the country with the highest CO2 emission rate in 2013? Impossible 8
  9. 9. What do we actually want? • Use the Web like a single global database • Move from a Web of documents to a Web of Data Slide adapted from Boris Villazón Terrazas and “5min Introduction to Linked Data”- Olaf Hartig Wikipedia CIA World FactBook Shanghai Tower 2013-8-3CC BY-SA 3.0 9
  10. 10. Linked Data enables such Web of Data Slide adapted from Boris Villazón Terrazas and “5min Introduction to Linked Data”- Olaf Hartig Global Identifier: URI (Uniform Resource Identifier) identifies a resource on the Internet. Data Model: RDF (Resource Description Framework) standard model for data interchange on the Web. Access Mechanism: HTTP Connection: Typed Links Wikipedia CIA World FactBook Shanghai Tower 2013-8-3CC BY-SA 3.0 http://cia.../China 10000…http://...wikipedia.../data/s hangaiTower http://.../co2emission http://.../depiction 2013 http://.../co2emissionPerYearhttp://.../location http://.../location http://.../year http://…#sameAs 10
  11. 11. The four principles (Tim Berners Lee, 2006) 1. Use URIs as names for things 2. Use HTTP URIs so that people can look up those names. 3. When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL) 4. Include links to other URIs, so that they can discover more things. http://www.w3.org/DesignIssues/LinkedData.html 11
  12. 12. “The Semantic Web is an extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. It is based on the idea of having data on the Web defined and linked such that it can be used for more effective discovery, automation, integration, and reuse across various applications.” Hendler, J., Berners-Lee, T., and Miller, E. Integrating Applications on the Semantic Web, 2002, http://www.w3.org/2002/07/swint.html Semantic Web definition 12
  13. 13. Benefits + Cases of success • Provide semantics  meaningful data & common understanding •  Interoperability • Reasoning power • Infer more data • Find mistakes in the original data? • Enrich your data (with what is already out there) • Search engines are indexing some schemas • Increase visibility • Multilingual information 13
  14. 14. In this tutorial “D4.1 Requirements and guidelines for energy data generation” From READY4SmartCities project available at http://goo.gl/IWDmYy 14
  15. 15. Table of Contents 1. Introduction 2. Data preparation 1. Select data source 2. Obtain access to data source 3. Analyse licensing of the data source 4. Analyse data source 3. Ontology development 4. Data generation 5. Discussion and Conclusions 15
  16. 16. Select data source • Selecting the data source that will be transformed into Linked Data • Steps 1. To define the requirements 2. To select one or several data sources • Alternatives: • Data set from your own organization • Data sourced not owned by your organization (external data sources) 16
  17. 17. Select data source – LCC example • Limitation to external data sources (search) 1. Requirements • Real-world scenario in the energy domain • Available for use • Available in machine-processable format (the more structured the data are, the better) • Can be linked with generic entities (e.g., location) 2. Leeds City Council – energy consumption (http://data.gov.uk/dataset/council-energy-consumption) 17
  18. 18. Table of Contents 1. Introduction 2. Data preparation 1. Select data source 2. Obtain access to data source 3. Analyse licensing of the data source 4. Analyse data source 3. Ontology development 4. Data generation 5. Discussion and Conclusions 18
  19. 19. Obtain access to data source • Data access means • technical means to retrieve the data • legal rights to use the data • In some cases, data source might not be accessible • Steps 1. To identify the person to contact 2. To request the access 3. To obtain access and to retrieve the data • Access alternatives: files, programming interface, database, data streams, etc. 19
  20. 20. Obtain access to data source – LCC example • Data set already available for download • Available in a CSV file 20
  21. 21. Table of Contents 1. Introduction 2. Data preparation 1. Select data source 2. Obtain access to data source 3. Analyse licensing of the data source 4. Analyse data source 3. Ontology development 4. Data generation 5. Discussion and Conclusions 21
  22. 22. Analysing licensing of the data source • Licenses specify the legal terms under which a data set can be used and exploited • Steps 1. To identify the publisher 2. To find the applicable license • Web page, data set metadata, data itself • Contact the publisher 3. To read the license and determine legal terms • Tips • Analysis should be performed upon all available copies of the data • Ensure compatible licences between several data sources 22
  23. 23. Analyse licensing – LCC example 23
  24. 24. Table of Contents 1. Introduction 2. Data preparation 1. Select data source 2. Obtain access to data source 3. Analyse licensing of the data source 4. Analyse data source 3. Ontology development 4. Data generation 5. Discussion and Conclusions 24
  25. 25. Analyse data source • Getting insight into data structure and organization • Steps 1. To analyse the characteristics of the data • Data values, data ranges, etc. 2. To obtain the schema of the data • Description of concepts and their relationships • Data format alternatives: • Structured data • Unstructured data • Tip: Use standard modeling language for data schema (e.g., UML) 25
  26. 26. Analyse data source – LCC example • Electricity, gas and oil consumptions as decimal values • 1-year intervals - 2010/11, 2011/12, 2012/13 • Different types of council sites (mostly buildings) • Full address provided (street, city, district) • Correspondence with people from LCC open data 26
  27. 27. Table of Contents 1. Introduction 2. Data preparation 3. Ontology development 4. Data generation 5. Discussion and Conclusions 27
  28. 28. Ontology development - Preparation • RDF – Resource Description Framework • Data model • (subject-predicate-object) • Resource naming strategy • For terms • Pattern: http://smartcity.linkeddata.es/lcc/ontology/EnergyConsumption#myter m • Example: http://smartcity.linkeddata.es/lcc/ontology/EnergyConsumption#hasQu antitiveValue • For individuals • Pattern: http://smartcity.linkeddata.es/lcc/resource/LeisureCentre/myIndividual • Example: http://smartcity.linkeddata.es/lcc/resource/LeisureCentre/LeisureCentr eWetJohnCharlesCentreforSport • RDF syntaxes • RDF/XML, ttl, N3, N quads 28
  29. 29. Ontology development [1] Suárez-Figueroa, M.C. PhD Thesis: NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse. Spain. June 2010. Activity definition taken from [1] Focus of each activity Existing tools to carry out the activity Tips, alternatives and references 29
  30. 30. Ontology development Ontology Requirements: refers to the activity of collecting the requirements that the ontology should fulfil (for example, reasons to build the ontology, identification of target groups and intended uses). (NeOn) 30 Proposed references: - NeOn Guidelines for non functional requirements. - Competency Questions technique [1] Tools: mind map, text editor, etc [1] Gruninger, M., Fox, M. S. The role of competency questions in enterprise engineering. In Proceedings of the IFIP WG5.7 Workshop on Benchmarking - Theory and Practice, Trondheim, Norway, 1994.
  31. 31. Ontology development – LLC example LCC example (Data from….) Non functional requirements specified: • The ontology will try to adopt concepts and design patterns in other ontologies where possible • The ontology should be implemented in OWL 2 DL 31
  32. 32. Ontology development Ontology term extraction to extract a glossary of terms that may be developed. Tools for terminology extraction: • Identify nouns, verbs, etc. • Tools: Freeling for free text Focus: • Extract terminology from Competency Questions (NeOn) • Extract terminology directly from the data • Expert advise || Done by experts 32 Complete the list with synonyms
  33. 33. Ontology development – LLC example Site place Address PostCode Electricity Consumption, utilization years time 33
  34. 34. Ontology development Ontology conceptualization refers to the activity of organizing and structuring the information (data, knowledge, etc.), obtained during the acquisition process, into meaningful models at the knowledge level and according to the ontology requirements specification document. (NeOn) Drawing tools, including paper and pencil Focus drafting (optional): • Identify main domains and top concept • Establish relations between concepts and domains Focus detail model: • Establish hierarchies • Establish specific relationships among defined elements, rules, axioms, etc. 34 Do not try to define everything. You might change your mind during the implementation.
  35. 35. Ontology development – LLC example 35
  36. 36. Ontology development Ontology search refers to the activity of finding candidate ontologies or ontology modules to be reused (NeOn). Search tools: • General purpose: • LOV: http://lov.okfn.org • LOD2Stats: http://stats.lod2.eu/vocabularies • Google • Others: ODP Portal http://ontologydesignpatterns.org • Domain base: • Smart cities: http://smartcity.linkeddata.es/ Focus: • Terms already used in LOD • Save time and resources • Increase interoperability Use domain terms and synonyms Do not spend too much time trying to find terms for everything. You might need to create them. 36
  37. 37. Ontology development – LLC example Terms and synonyms 37
  38. 38. Ontology development Ontology Selection refers to the activity of choosing the most suitable ontologies or ontology modules among those available in an ontology repository or library, for a concrete domain of interest and associated tasks. (NeOn) Evaluation tools: • OOPS! – OntOlogy pitfalls scanner [1] http://www.oeg- upm.net/oops/ • Triple checker http://graphite.ecs.soton.ac.uk/checker/ (already included in OOPS!) • Vapour http://validator.linkeddata.org/vapour (to be included in OOPS!) Also it should be considered: • Modelling issues (OOPS!, reasoners, manually review, etc.) • Domain coverage (based on the data to be represented) • Used in Linked Data (LOD2Stats, Sindice, etc) Focus: • Assessment by Linked Data principles • Modelling issues • Domain coverage: data driven [1] Poveda-Villalón, M., Suárez-Figueroa, M. C., & Gómez-Pérez, A. (2012). Validating ontologies with oops!. In Knowledge Engineering and Knowledge Management (pp. 267-281). Springer Berlin Heidelberg. Further reference: NeOn Guidelines 38
  39. 39. Ontology development – LLC example • Domain coverage • Schema.org for public places and provides some additional terms and properties that can be used(e.g., PostalAddress and City) • Also widely-known and accepted vocabulary  interoperability • Closer semantics • ero:FinalEnergy class from the Energy Resource and the ssn:Property class from the SSN ontology in order to represent specific indicator for which the consumption is related to 39
  40. 40. Ontology development Ontology Integration. It refers to the activity of including one ontology in another ontology. (NeOn) Tools: • Ontology editors: Protégé, NeOn Toolkit, etc. • Plug-ins: Ontology Module Extraction and Partition • Text editors for manual approach Focus: • How much information should I reuse? • How to reuse the elements or vocabs? Preliminary analysis [1] • Should I import another ontology? • Should I reference other ontology element URIs? • ... replicating manually the URI? • ... merging ontologies? • How to link them? Techniques: • Import the ontology as a whole • Reuse some parts of the ontology (or ontology module) • Reuse statements [1] Poveda-Villalón, M., Suárez-Figueroa, M. C., & Gómez-Pérez, A. The Landscape of Ontology Reuse in Linked Data. 1st Ontology Engineering in a Data-driven World (OEDW 2012) Workshop at the18th International Conference on Knowledge Engineering and Knowledge Management . Galway, Ireland, 9th October 2012. http://www.slideshare.net/MariaPovedaVillalon/mpoveda-oedw2012v1 40
  41. 41. Ontology development Ontology Enrichment It refers to the activity of extending an ontology with new conceptual structures (e.g., concepts, roles and axioms). (NeOn) Focus: • How should I create terms according to ontological foundations and Linked Data principles? Ontology development: • Ontology Development 101: A Guide to Creating Your First Ontology [2] • Ontology Engineering Patterns http://www.w3.org/2001/sw/BestPractices/ • Extracting ontology conceptualization, formalization techniques from existing methodologies Recommendation • Link to existing entities • Provide human readable documentation • Keep the semantics of the reused elements [1] Natalya F. Noy and Deborah L. McGuinness. Ontology Development 101: A Guide to Creating Your First Ontology’. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, March 2001. Tools: • Ontology editors: Protégé, NeOn Toolkit, etc. 41
  42. 42. time:Interval schema:City ssn:Observation ssn:observation SamplingTime ssn:SensorOutput ssn:ObservationValue ssn:hasValue ssn:FeatureOf Interest ssn:featureOf Interest lcc:hasQuantityValue :: xsd:decimal ssn:Property ero:FinalEnergy ssn:observed Property ssn:observation Result Legend Class datatype property :: datatype object property subclass of relation schema:CivicStructure lcc:uprn :: xsd:String dc:title :: xsd:String schema:PostalAddress schema:addressLocality :: xsd:String schema:addressRegion :: xsd:String schema:streetAddress :: xsd:String schema:postalCode :: xsd:String schema:address admingeo:District admingeo:district time:Instant time:inXSDDateTime :: xsd:dateTime time:hasBeginning time:hasEnd ero:Energy ConsumerFacility ero:consumes EnergyType om:Unit_of_measure lcc:hasQuantityUnitOf Measurement SupplyOrStorageSite OpenAirSite AccomodationSite AdministrativeSite OfficeSite EducationalSite SocialSite OtherSite CulturalSite schema:containedIn schema:Place schema:Administrative AreaLeisureSite Ontology development – LLC example 42
  43. 43. Ontology development Ontology Evaluation it refers to the activity of checking the technical quality of an ontology against a frame of reference. (NeOn) Evaluation tools related to Linked Data principles: • OOPS! – OntOlogy pitfalls scanner [2] http://www.oeg- upm.net/oops/ • Triple checker http://graphite.ecs.soton.ac.uk/checker/ (already included in OOPS!) Evaluation tools/techniques other aspects: • Modelling issues (OOPS!, reasoners, manually review, etc.) • Domain coverage (based on the data to be represented) • Application based (queries) • Syntax issues: validators Focus: • Assessment by Linked Data principles • Modelling issues • Domain coverage: data driven [1] Poveda-Villalón, M., Suárez-Figueroa, M. C., & Gómez-Pérez, A. (2012). Validating ontologies with oops!. In Knowledge Engineering and Knowledge Management (pp. 267-281). Springer Berlin Heidelberg. 43
  44. 44. Ontology development – LLC example Minor, mostly lack of annotations in reused terms. 44
  45. 45. Table of Contents 1. Introduction 2. Data preparation 3. Ontology development 4. Data generation 1. Data transformation 2. Data linking 5. Discussion and Conclusions 45
  46. 46. Data transformation • Transformation of the data to RDF • Steps 1. To select the RDF serialization • RDF/XML, Turtle, N-Triples, JSON-LD 2. To select a tool 3. To transform the data 4. To evaluate the obtained RDF data • Syntax evaluation • Accuracy • Usage 46
  47. 47. Data transformation - Tools 47 Database to RDF Data streams to RDF • morph-RDB • D2R Server • TopBraid Composer • morph-streams • D2R Server Spreadsheets to RDF XML to RDF • TopBraid Composer • Excel2RDF • RDF123 • XLWrap • OpenRefine • XML2RDF • TopBraid Composer • OpenRefine (GoogleRefine, LODRefine)
  48. 48. Data transformation – LCC example 1. Turtle syntax 2. OpenRefine + RDF extension 48
  49. 49. Data transformation – LCC example: OpenRefine creating project 49
  50. 50. Data transformation – LCC example: OpenRefine adding columns 50
  51. 51. Data transformation – LCC example OpenRefine adding columns 51
  52. 52. Data transformation – LCC example OpenRefine column transformations 52
  53. 53. Data transformation – LCC example OpenRefine RDF extension 53
  54. 54. Data transformation – LCC example OpenRefine RDF extension 54
  55. 55. Data transformation – LCC example OpenRefine RDF extension 55 time:Intervalssn:Observation ssn:observation SamplingTime ssn:SensorOutput ssn:ObservationValue ssn:hasValue ssn:FeatureOf Interest ssn:featureOf Interest lcc:hasQuantityValue :: xsd:decimal ssn:Property ero:FinalEnergy ssn:observed Property ssn:observation Result schema:CivicStructure lcc:uprn :: xsd:String dc:title :: xsd:String schema:PostalAddress schema:addressLocality :: xsd:String schema:addressRegion :: xsd:String schema:streetAddress :: xsd:String schema:postalCode :: xsd:String schema:address time:Instant time:inXSDDateTime :: xsd:dateTime time:hasBeginning time:hasEnd ero:Energy ConsumerFacility ero:consumes EnergyType om:Unit_of_measure lcc:hasQuantityUnitOf Measurement OpenAirSite OfficeSite EducationalSite SocialSite CulturalSite schema:containedIn schema:Place schema:Administrative AreaLeisureSite
  56. 56. Data transformation – LCC example OpenRefine RDF extension 56
  57. 57. Data transformation – LCC example OpenRefine RDF extension 57
  58. 58. Data transformation – LCC example OpenRefine RDF extension 58
  59. 59. Data transformation – LCC example OpenRefine RDF extension 59
  60. 60. Data transformation – LCC example OpenRefine RDF generation 60
  61. 61. Data transformation – LCC example Evaluation • Syntax evaluation • Consistency with the ontologies • Usage evaluation by running SPARQL queries • show all electricity consumptions and related time periods for all council sites related to culture • show all energy consumptions and related time period of council sites from Wakefield district 61
  62. 62. Table of Contents 1. Introduction 2. Data preparation 3. Ontology development 4. Data generation 1. Data transformation 2. Data linking 5. Discussion and Conclusions 62
  63. 63. Data linking • Ensuring that data are not just “isolated islands” • Steps 1. To identify classes whose instances can be the subject of linking 2. To identify data sets that may contain instances for the previously-identified classes 3. To select the tools for performing the task 4. To use the tool in order to obtain links • Tools: LN2R, LD mapper, Silk, LIMES, RDF-AI, Serimi, OpenRefine 63
  64. 64. Data linking – LCC example 1. Classes: City, District 2. Data sets: Dbpedia 3. Tool: OpenRefine 64
  65. 65. Data linking – LCC example OpenRefine reconciliation 65
  66. 66. Data linking – LCC example OpenRefine reconciliation 66
  67. 67. Data linking – LCC example OpenRefine reconciliation 67
  68. 68. Data linking – LCC example OpenRefine reconciliation 68
  69. 69. Data linking – LCC example OpenRefine reconciliation 69
  70. 70. Table of Contents 1. Introduction 2. Data preparation 3. Ontology development 4. Data generation 5. Discussion and Conclusions 70
  71. 71. Discussion and Conclusions • The guidelines are based on requirements from smart city stakeholders • Address the broad scope of scenarios • Different data formats (databases, CSV, Excel, XML, etc.) • Update frequencies (static and dynamic data) • Legal and licensing issues • Introduces a complete example 71 Radulovic, F., García-Castro, R., Poveda-Villalón, M., Weise, M., Tryferdis, T.: D4.1: Requirements and guidelines for energy data generation. Technical report, READY4SmartCities Consortium, May 2014
  72. 72. More information 72 time:Interval schema:City ssn:Observation ssn:observation SamplingTime ssn:SensorOutput ssn:ObservationValue ssn:hasValue ssn:FeatureOf Interest ssn:featureOf Interest lcc:hasQuantityValue :: xsd:decimal ssn:Property ero:FinalEnergy ssn:observed Property ssn:observation Result Legend Class datatype property :: datatype object property subclass of relation schema:CivicStructure lcc:uprn :: xsd:String dc:title :: xsd:String schema:PostalAddress schema:addressLocality :: xsd:String schema:addressRegion :: xsd:String schema:streetAddress :: xsd:String schema:postalCode :: xsd:String schema:address admingeo:District admingeo:district time:Instant time:inXSDDateTime :: xsd:dateTime time:hasBeginning time:hasEnd ero:Energy ConsumerFacility ero:consumes EnergyType om:Unit_of_measure lcc:hasQuantityUnitOf Measurement SupplyOrStorageSite OpenAirSite AccomodationSite AdministrativeSite OfficeSite EducationalSite SocialSite OtherSite CulturalSite schema:containedIn schema:Place schema:Administrative AreaLeisureSite
  73. 73. Linked Data is just data 73 01000000 electric1011 01000000 electric1112 01000000 0 20 40 60 80 100 electric1213 Building Electrical consumption 0e+00 2e+06 4e+06 6e+06 8e+06 0 500000 1000000 1500000 2000000 Electricity Gas Electricity vs gas consumption 12/13 0.0e+00 4.0e+06 8.0e+06 1.2e+07 0 500000 1000000 1500000 2000000 Electricity Oil Electricity vs oil consumption 12/13
  74. 74. Benefits of linking data 74 resPlus$electricTotal 0e+00 2e+06 4e+06 6e+06 Total electric consumption Original data + geolocation resP Total electric consumption in locations with population > 20.000 Original data + geolocation + population
  75. 75. Benefits of reasoning resPlus 25 50 75 10 75 Total electric consumption in cultural buildings schema:CivicStructure CulturalSite Museum Library
  76. 76. Discussion and Conclusions 76
  77. 77. Discussion and Conclusions – Future work • Development of services for facilitating the usage of Linked Data technology • Support in adopting Linked Data technology • Guidelines for publication and exploitation of Linked Data • Summer school for 2015 • Other training? 77
  78. 78. Linked Energy Data Generation Tutorial Filip Radulovic, María Poveda Villalón, Raúl García-Castro {fradulovic,mpoveda,rgarcia}@fi.upm.es ETSI Informaticos Universidad Politécnica de Madrid Campus de Montegancedo s/n 28660 Boadilla del Monte, Madrid, Spain Twitter: @LD4SC 02.10.2014. Sustainable Places 2014, Nice, France

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