The document discusses developing Linked Data and Semantic Web applications. It begins with key concepts related to Linked Data, the Semantic Web, and applications. It then describes two key steps in developing such applications: publishing data as Linked Data and consuming Linked Data to build applications. Examples are provided of extracting, enriching, and linking different datasets to build a real estate recommendation application that performs semantic searches over the integrated data. Ontologies are created and reused to represent the domains and support interoperability. The document emphasizes integrating the data and software engineering perspectives in developing Semantic Web applications.

1. NEES/IC
Federal University of Alagoas (UFAL)
igbittencourt.com
Ig Ibert Bittencourt
Developing Linked Data and Semantic
Web-based Applications
Expotec 2015

2. Brasil

3. Brasil

4. Maceió

5. Maceió
Maceió, AL

7. NEES/IC
Federal University of Alagoas (UFAL)
igbittencourt.com
Ig Ibert Bittencourt
Developing Linked Data and Semantic
Web-based Applications
Expotec 2015

8. Some key concepts
Linked Data and Semantic Web
Linked Data and Semantic Web Applications

9. Some key concepts
Linked Data and Semantic Web
Linked Data and Semantic Web Applications

10. Data

12. Web Scope
Source: (Gandon et al., 2013)

13. Data
Data on the Web

15. Open means anyone can freely access,
use, modify, and share for any purpose
(subject, at most, to requirements that
preserve provenance and openness)
Source: The Open Definition

16. Da ta
Data on the Web
Open Closed

18. 1. Use URIs as name for things
2. Use HTTP URIs so that people can look up those
names
3. When someone looks up a URI, provide useful
information (RDF, HTML) using content negotiation
4. Include links to other URIs, so that they can discover
more things.
Source: Design Issues

19. Source: http://www.cafepress.com/w3c_shop
Is your data 5 Stars?

20. Day Lowest Temperature (°C)
Saturday, 13 November 2010 2
Sunday, 14 November 2010 4
Monday, 15 November 2010 7
Source: http://5stardata.info/
Example: Is your data 5 Stars?

21. Example: 1 Star Data (Open License)
Day Lowest Temperature (°C)
Saturday, 13 November 2010 2
Sunday, 14 November 2010 4
Monday, 15 November 2010 7
Source: http://5stardata.info/
http://5stardata.info/gtd-1.pdf

22. Example: 2 Stars Data (Machine-readable)
Day Lowest Temperature (°C)
Saturday, 13 November 2010 2
Sunday, 14 November 2010 4
Monday, 15 November 2010 7
Source: http://5stardata.info/
http://5stardata.info/gtd-2.xls

23. Example: 3 Stars Data (non-proprietary format)
Day Lowest Temperature (°C)
Saturday, 13 November 2010 2
Sunday, 14 November 2010 4
Monday, 15 November 2010 7
Source: http://5stardata.info/
http://5stardata.info/gtd-3.csv

24. Example: 4 Stars Data (RDF Standards)
Day Lowest Temperature (°C)
Saturday, 13 November 2010 2
Sunday, 14 November 2010 4
Monday, 15 November 2010 7
Source: http://5stardata.info/
http://5stardata.info/gtd-4.html

26. Web Architecture
Source: http://www.w3.org/TR/webarch/

27. Abstract Representation of RDF

28. RDF Structure
Source: (Gandon 2013)

29. RDF Example
Source: (Schreider et al., 2014)

30. RDF Example
Source: (Schreider et al., 2014)

31. RDF Example (Turtle Serialization)
Source: (Schreider et al., 2014)

32. RDF Serialization Formats
Source: (Wood, 2014)

33. RDF Example (JSON-LD Serialization)
Source: (Schreider et al., 2014)

34. Example: 4 Stars Data (RDF Standards)
Day Lowest Temperature (°C)
Saturday, 13 November 2010 2
Sunday, 14 November 2010 4
Monday, 15 November 2010 7
Source: http://5stardata.info/
http://5stardata.info/gtd-4.html

35. Example: 4 Stars Data (RDF Standards)
Source: http://5stardata.info/

36. Example: 4 Stars Data (RDF Standards)
Source: http://5stardata.info/

37. Example: 5 Stars Data (Linked RDF)
Day Lowest Temperature (°C)
Saturday, 13 November 2010 2
Sunday, 14 November 2010 4
Monday, 15 November 2010 7
Source: http://5stardata.info/
http://5stardata.info/gtd-5.html

38. Example: 5 Stars Data (Linked RDF)
Source: http://5stardata.info/

39. RDF Example
Source: http://en.wikipedia.org/wiki/London

40. RDF Example
Source: (Chen, 2007)

41. RDF Example
Source: (Chen, 2007)

42. RDF Example
Source: http://dbpedia.org/page/London

43. LOD Cloud
Source: http://dbpedia.org/page/London

44. The 5 Stars Schema
Source: http://5stardata.info/

45. Da ta
Data on the Web
Open Closed
Linked Data

48. I have a dream for the Web [in which computers] become
capable of analyzing all the data on the Web – the content,
links, and transactions between people and computers.

49. Semantic Web “layer cake”
Source: (Berners-Lee, 2000)

50. Semantic Web “layer cake” updated
Source: (Berners-Lee, 2006)

51. Source: (Gandon, 2014)
Semantic Web and Linked Data

52. Source: (Gandon, 2014)
Semantic Web and Linked Data

54. Pessoal
Campanha
Organização
Prestação de Serviço

55. Ontologias
Understand the
structure of the things
Support
communication
between people
Interoperate
systems
Improve IT
services
Philosophy
Linguistics
Logic
Computer
Science
Computational
Ontology
Computational Ontology

57. Da ta
Data on the Web
Open Closed
Linked Data
Ontologies

58. Semantic Web “layer cake” updated
Source: (Berners-Lee, 2006)

59. Source: (Gandon, 2014)
The Semantics of the Semantic Web

60. Example: BBC

61. Da ta
Data on the Web
Open Closed
Linked Data
Ontologies
Semantic Web

62. Some key concepts
Linked Data and Semantic Web
Linked Data and Semantic Web Applications

63. Some key concepts
Linked Data and Semantic Web
Linked Data and Semantic Web Applications

64. Developing LD and SW Apps: Two Key Steps
Publish Consume

65. Publication Process
Source: (Gandon, 2014)

66. Consumption Process:
Different Types of applications
Browsers
Search Engines and
Indexes
Domain-specific
Applications
Linked
Data
Semantics
Linked Data
and Semantic
Web
Applications

67. Consumption Process:
A traditional Software Engineering perspective

68. Source: (Joice et al, 2014)
Integrated Process

70. Domain
Domain
X
User
Learner
X
Learner
(Interaction)
Learner X
Instances
Domain
X
Instances
CP Resource Curriculum
Pedagogical
Pedagogical
X
Instances
Organization
Teacher
(School)
Director
(Interaction)
Coordinator
(Interaction)
School
School
Instances
Learner
(School)
Teacher
(Interaction)

75. Real States Application
How to create an application to
recommend real states
according to users’ interests?

76. Real States Application

79. Education Health Security
City Real Estates User Data

80. Source: (Joice et al, 2014)
Integrated Process

81. Boa Moradia Application (Key Backlog Items)
Data Perspective
Choose Datasets
Create and Reuse Ontologies
Enrich the Data (RDF, OWL)
Link the Data
Software Perspective
Extract Real States
Enrich Real States Data
Classify Real States
Extract OSM Data
Classify OSM Data
Make Semantic Search

82. Boa Moradia Application (Datasets)
Schools and Universities
Hospitals and Public Health Places
Transportation
Regions, neighborhood, streets, zip code
Police Stations
OSM Nodes

83. Boa Moradia Application (Key Backlog Items)
Data Perspective
Choose Datasets
Create and Reuse Ontologies
Enrich the Data (RDF, OWL)
Link the Data
Software Perspective
Extract Real States
Enrich Real States Data
Classify Real States
Extract OSM Data
Classify OSM Data
Make Semantic Search

84. Boa Moradia Application (Ontologies)

85. Boa Moradia Application (Ontologies)

86. Boa Moradia Application (Ontologies)

87. Boa Moradia Application (Ontologies)

88. Boa Moradia Application (Ontologies)

89. Boa Moradia Application (Ontologies)

90. Boa Moradia Application (Ontologies)

91. Boa Moradia Application (Key Backlog Items)
Data Perspective
Choose Datasets
Create and Reuse Ontologies
Enrich the Data (RDF, OWL)
Link the Data
Software Perspective
Extract Real States
Enrich Real States Data
Classify Real States
Extract OSM Data
Classify OSM Data
Make Semantic Search

92. Boa Moradia Application (Enrich the Data)

93. Boa Moradia Application (Enrich the Data)

94. Boa Moradia Application (Key Backlog Items)
Data Perspective
Choose Datasets
Create and Reuse Ontologies
Enrich the Data (RDF, OWL)
Link the Data
Software Perspective
Extract Real States
Enrich Real States Data
Classify Real States
Extract OSM Data
Classify OSM Data
Make Semantic Search

95. Boa Moradia Application (Extract OSM Data)

96. Boa Moradia Application (Extract OSM Data)

97. Boa Moradia Application (Extract OSM Data)

98. Boa Moradia Application (Extract OSM Data)

99. Boa Moradia Application (Key Backlog Items)
Data Perspective
Choose Datasets
Create and Reuse Ontologies
Enrich the Data (RDF, OWL)
Link the Data
Software Perspective
Extract Real States
Enrich Real States Data
Classify Real States
Extract OSM Data
Classify OSM Data
Make Semantic Search

100. Boa Moradia Application (Classify OSM Data)

101. Boa Moradia Application (Classify OSM Data)

102. Boa Moradia Application (Key Backlog Items)
Data Perspective
Choose Datasets
Create and Reuse Ontologies
Enrich the Data (RDF, OWL)
Link the Data
Software Perspective
Extract Real States
Enrich Real States Data
Classify Real States
Extract OSM Data
Classify OSM Data
Make Semantic Search

103. Boa Moradia Application (Extract Real Estates)

104. Boa Moradia Application (Extract Real Estates)

105. Boa Moradia Application (Key Backlog Items)
Data Perspective
Choose Datasets
Create and Reuse Ontologies
Enrich the Data (RDF, OWL)
Link the Data
Software Perspective
Extract Real States
Enrich Real States Data
Classify Real States
Extract OSM Data
Classify OSM Data
Make Semantic Search

106. Boa Moradia Application (Enrich Real Estates)

107. Boa Moradia Application (Key Backlog Items)
Data Perspective
Choose Datasets
Create and Reuse Ontologies
Enrich the Data (RDF, OWL)
Link the Data
Software Perspective
Extract Real States
Enrich Real States Data
Classify Real States
Extract OSM Data
Classify OSM Data
Make Semantic Search

108. Boa Moradia Application (Classify Real Estates)

109. Boa Moradia Application (Classify Real Estates)

110. Boa Moradia Application (Key Backlog Items)
Data Perspective
Choose Datasets
Create and Reuse Ontologies
Enrich the Data (RDF, OWL)
Link the Data
Software Perspective
Extract Real States
Enrich Real States Data
Classify Real States
Extract OSM Data
Classify OSM Data
Make Semantic Search

112. Maceió
Maceió, Alagoas, Brazil
ig.ibert@ic.ufal.br
Igbittencourt.com

113. NEES/IC
Federal University of Alagoas (UFAL)
igbittencourt.com
Ig Ibert Bittencourt
Developing Linked Data and Semantic
Web-based Applications
Expotec 2015