How to be one step ahead of leveraging knowledge technologies for your apps! When: Dec 8, 2017 Where: Fl. 6, Multimedia Tower, Central Jakarta Thanks to Ragil for the invitation!

1. KNOWLEDGE TECHNOLOGIES:
OPPORTUNITIES AND CHALLENGES
Fariz Darari
fariz@cs.ui.ac.id
Dec 8, 2017 Hosted by

2. Fariz Darari
• 1988: Born in Malang
• 2010: BSc in Computer Science at Universitas Indonesia
• 2013: MSc in Computational Logic at University of Bolzano,
Italy and TU Dresden, Germany
Best Thesis Award and Enno-Heidebroek Award
• 2017: PhD in Computational Logic at University of Bolzano,
Italy and TU Dresden, Germany
• 2017: Lecturer at Faculty of CS, Universitas Indonesia

3. Bolzano

4. Dresden

5. Universitas Indonesia

6. • Knowledge Technologies: Motivations
• Semantic Web
• Knowledge Bases These Days (= Zaman Now)
• Wikidata
• DBpedia
• Applications
• Discussion: Challenges & Opportunities
Menu

7. What if the knowledge in your brains,*
can be queried by computers?
*notice the plural form

8. What if the knowledge in your brains,*
can be queried by computers?
*notice the plural form

9. What if the knowledge in your brains,*
can be queried by computers?
*notice the plural form

10. What if the knowledge in your brains,
can be queried by computers?
Can you imagine what kind of advancements
can be made to humanity?

11. What if the knowledge in your brains,
can be queried by computers?
Can you imagine what kind of advancements
can be made to humanity?
Stay tuned, will present you an answer to this question some slides later!

12. ... if properly designed,
the Semantic Web can assist
the evolution of human knowledge
as a whole.
– Tim Berners-Lee
Inventor of the (Semantic) Web

13. What is the Semantic Web?

14. What is the Semantic Web?

15. What is the Semantic Web?
The set of technologies to put knowledge on the Web,
that is based on the following four principles:
1. Use URIs (Universal Resource Identifiers)* for identifying things
2. Use HTTP** URIs so people can look up those names
3. When someone looks up a URI, provide useful knowledge
using the standards: RDF and SPARQL.
4. Include links to other URIs, so they can discover more things
https://www.w3.org/DesignIssues/LinkedData.html
* URI = just like URL (web address), but you use it to identify things just like barcode for supermarket stuff!
** HTTP = the mechanism you use every time you access the Web!

16. Semantic Web IRL (In Real Life)

17. Semantic Web Standards
the data guy
the schema guy
the query guy

18. RDF in one slide
the data guy
• Data model, based on S-P-O triple structure (Subject, Predicate, Object)
• Used for describing things, yes, every, single, thing
And anyway, RDF = Resource Description Framework
• Key features:
• RDF data can be exported in JSON and XML
• RDF links things, not just documents
• RDF links are typed
TelkomUniversity somelink Bandung
TelkomUniversity locatedIn Bandung

19. OWL in one slide
• Schema (=Ontology) language, describing vocabularies
• Yes, it is on the meta-level!
• Short for: Web Ontology Language (WOL? No, it is OWL!)
• Key features:
• Reasoning: you can check if your knowledge is consistent/not!
• Reasoning again: you can conclude new things
based on existing facts.
• Very simple example:
owl SubClassOf bird + bird SubClassOf animal + owl EquivalentClass strigifomes
Now, if Bobi is a Strigifomes, do you think Bobi is an animal?
the schema guy

20. OWL in one slide
• Schema (=Ontology) language, describing vocabularies
• Yes, it is on the meta-level!
• Short for: Web Ontology Language (WOL? No, it is OWL!)
• Key features:
• Reasoning: you can check if your knowledge is consistent/not!
• Reasoning again: you can conclude new things
based on existing facts.
• Very simple example:
owl SubClassOf bird + bird SubClassOf animal + owl EquivalentClass strigifomes
Now, if Bobi is a Strigifomes, do you think Bobi is an animal? OWL will say:
the schema guy

21. OWL in one slide
• Schema (=Ontology) language, describing vocabularies
• Yes, it is on the meta-level!
• Short for: Web Ontology Language (WOL? No, it is OWL!)
• Key features:
• Reasoning: you can check if your knowledge is consistent/not!
• Reasoning again: you can conclude new things
based on existing facts.
• Very simple example:
owl SubClassOf bird + bird SubClassOf animal + owl EquivalentClass strigifomes
Now, if Bobi is a Strigifomes, do you think Bobi is an animal? OWL will say: "YES!"
the schema guy

22. SPARQL in one slide
the query guy
• Query language: If RDF captures knowledge,
SPARQL asks questions about knowledge!
• Short for: SPARQL Protocol and RDF Query Language
• Key features: Asking for knowledge, is a KEY feature!
• Very simple example:
TelkomUniversity locatedIn Bandung
Bandung headOfGov RidwanKamil
TelkomUniversity instanceOf University
It is SPARQLing!
SELECT ?university WHERE {
?university instanceOf University .
?university locatedIn ?city .
?city headOfGov RidwanKamil }
Guess what this query is asking for?
HINT: Question mark (?) represents variables to match
with RDF data!

23. Knowledge Bases (KBs) These Days (Zaman Now)
KB THEN KB ALMOST NOW
KB NOW

24. Knowledge Bases (KBs) These Days (Zaman Now)
KB NOW

25. Knowledge Bases (KBs) These Days (Zaman Now)
KB NOW
Subject
Predicate
Predicate
Predicate
Object
Object
Object
Reminds you of something?

26. Knowledge Bases (KBs) These Days (Zaman Now)
KB NOW
Subject
Predicate
Predicate
Predicate
Object
Object
Object
Reminds you of something?
the data guy

27. Knowledge Bases (KBs) These Days (Zaman Now)
KB NOW
Subject
Predicate
Predicate
Predicate
Object
Object
Object
Reminds you of something?
the data guy
btw, every subject in Wikidata has its own identifier, the URI is made by: Wikidata domain + identifier

28. Knowledge Bases (KBs) These Days (Zaman Now)
KB NOW
Subject
Predicate
Predicate
Predicate
Object
Object
Object
Reminds you of something?
the data guy
btw, every subject in Wikidata has its own identifier, the URI is made by: Wikidata domain + identifier
= P31
= P571
= Q4830453
= Q10389

29. Knowledge Bases (KBs) These Days (Zaman Now)
the query guy
http://tinyurl.com/yc6jsmhv

30. Knowledge Bases (KBs) These Days (Zaman Now)
the query guy
http://tinyurl.com/y84kyl4d

31. Knowledge Bases (KBs) These Days (Zaman Now)
the schema guy
OwlInWinnieThePooh instanceOf fictionalOwl
fictionalOwl subClassOf fictionalBird
fictionalBird subClassOf fictionalAnimal

32. Knowledge Bases (KBs) These Days (Zaman Now)
Wikidata key features:
• It is like Wikipedia but for data!
• It is under Wikimedia foundation
• It is crowdsourced, anyone can add data
• It is free
• It's got 326 million facts about 40 million
subjects! (Wikipedia only has 5 million subjects!)
• It loves the Semantic Web

33. Knowledge Bases (KBs) These Days (Zaman Now)

34. Knowledge Bases (KBs) These Days (Zaman Now)
DBpedia key features:
• It extracts data from Wikipedia infoboxes
(summary box on top right corner).
• It is free
• It's got 13 BILLION facts about 7 million
subjects!
• It loves the Semantic Web

35. Knowledge Bases (KBs) These Days (Zaman Now)
DBpedia key features:
• It extracts data from Wikipedia infoboxes
(summary box on top right corner).
• It is free
• It's got 13 BILLION facts about 7 million
subjects!
• It loves the Semantic Web
• DBpedia Indonesia is available, hosted by
Faculty of Computer Science, Univ. Indonesia

36. Knowledge Bases (KBs) From Time to Time
(Semantic) Knowledge Bases in 2007

37. Knowledge Bases (KBs) From Time to Time
(Semantic) Knowledge Bases in 2017

38. Knowledge Bases (KBs) From Time to Time
(Semantic) Knowledge Bases in 2017

39. Application: Answer Engine
THEN: Search Engine

40. Application: Answer Engine
NOW: Answer Engine

41. Application: Answer Engine
Question: When was Soekarno born?

42. Question: When was Soekarno born?
http://id.dbpedia.org/page/Soekarno
Application: DBpedia-powered Answer Engine

43. Application: DBpedia-powered Answer Engine
Question: When was Soekarno born?
Borrow Techniques from
Natural Language Processing
SELECT ?birthDate
WHERE {
<http://id.dbpedia.org/resource/Soekarno> <http://dbpedia.org/ontology/birthDate> ?birthDate
}
SPARQL Query over DBpedia
http://id.dbpedia.org/sparql

44. Application: Timeline Infographics

45. Application: Timeline Infographics
Task: Create timeline of Indonesian national heroes based on their birthdates!
Without Wikidata:
- Read by eyes websites about national heroes (there are all 173 heroes!)
- Gather information manually
- Visualize information manually
Total time spent: 24+ hours!

46. Application: Wikidata-powered Timeline Infographics
Task: Create timeline of Indonesian national heroes based on their birthdates!
With Wikidata (and Histropedia):
- Formulate and evaluate the query
- VOILA: Beautiful timeline infographics created!
Total time spent: 10 minutes!

47. Application: Wikidata-powered Timeline Infographics

48. bit.ly/timelinePahlawanNasional

49. Bonus: Wikidata-powered Table
https://www.wikidata.org/wiki/Wikidata:WikiProject_Jasmerah/List/IndonesianNationalHeroes

50. Bonus: Wikidata-powered Table
https://www.wikidata.org/wiki/Wikidata:WikiProject_Jasmerah/List/IndonesianNationalHeroes

51. By the way, let's join Jasmerah, for better (data about the) Indonesian history!
https://www.wikidata.org/wiki/Wikidata:WikiProject_Jasmerah

52. Application: Virtual Doctor

53. Application: Wikidata-powered Virtual Doctor
dr Wikidata: Tell me your symptoms

54. Application: Wikidata-powered Virtual Doctor
dr Wikidata: Tell me your symptoms
Patient: I feel like fatigue, headache,
joint pain, and vomitting

55. Application: Wikidata-powered Virtual Doctor
dr Wikidata: Tell me your symptoms
Patient: I feel like fatigue, headache,
joint pain, and vomitting
dr Wikidata: From what I know,
you most likely get dengue fever!

56. Application: Wikidata-powered Virtual Doctor
Behind the scenes
http://tinyurl.com/y96cpbx8

57. Application: Wikidata-powered Virtual Doctor
Behind the scenes
http://tinyurl.com/y96cpbx8

58. Application: What is the news about?
http://www.thejakartapost.com/news/2017/10/20/telkom-plans-to-acquire-three-foreign-firms.html

59. Application: What is the news about?

60. Application: What is the news about?

61. Application: What is the news about?

62. Application: What is the news about?
Cross-dataset knowledge discovery!

64. Data Quality
Symptoms of Dengue Fever do not include fever!

65. Completeness: Is the data complete
enough? Is it of sufficient breadth and
depth?
Accuracy: How accurate is the data? Is
it reliable and verifiable?
Timeliness: Is the data up-to-date? Is
the latest data included?
Data Quality

66. Data Quality
http://d-nb.info/1136571418

67. Data Population
How to
create
data?

68. Data Consumption
How to reduce technological
learning steep for developers
and end-users?
Can we build more killer apps?

69. Data Scalability
Are we ready
for data
explosion?

70. Data Fusion
How to combine
structured data
and unstructured
data (= text)?

71. Data Analytics
What can be
analyzed, and
how fast?