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Knowledge Technologies: Opportunities and Challenges

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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!

Published in: Internet
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Knowledge Technologies: Opportunities and Challenges

  1. 1. KNOWLEDGE TECHNOLOGIES: OPPORTUNITIES AND CHALLENGES Fariz Darari fariz@cs.ui.ac.id Dec 8, 2017 Hosted by
  2. 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. 3. Bolzano
  4. 4. Dresden
  5. 5. Universitas Indonesia
  6. 6. • Knowledge Technologies: Motivations • Semantic Web • Knowledge Bases These Days (= Zaman Now) • Wikidata • DBpedia • Applications • Discussion: Challenges & Opportunities Menu
  7. 7. What if the knowledge in your brains,* can be queried by computers? *notice the plural form
  8. 8. What if the knowledge in your brains,* can be queried by computers? *notice the plural form
  9. 9. What if the knowledge in your brains,* can be queried by computers? *notice the plural form
  10. 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. 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. 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. 13. What is the Semantic Web?
  14. 14. What is the Semantic Web?
  15. 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. 16. Semantic Web IRL (In Real Life)
  17. 17. Semantic Web Standards the data guy the schema guy the query guy
  18. 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. 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. 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. 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. 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. 23. Knowledge Bases (KBs) These Days (Zaman Now) KB THEN KB ALMOST NOW KB NOW
  24. 24. Knowledge Bases (KBs) These Days (Zaman Now) KB NOW
  25. 25. Knowledge Bases (KBs) These Days (Zaman Now) KB NOW Subject Predicate Predicate Predicate Object Object Object Reminds you of something?
  26. 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. 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. 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. 29. Knowledge Bases (KBs) These Days (Zaman Now) the query guy http://tinyurl.com/yc6jsmhv
  30. 30. Knowledge Bases (KBs) These Days (Zaman Now) the query guy http://tinyurl.com/y84kyl4d
  31. 31. Knowledge Bases (KBs) These Days (Zaman Now) the schema guy OwlInWinnieThePooh instanceOf fictionalOwl fictionalOwl subClassOf fictionalBird fictionalBird subClassOf fictionalAnimal
  32. 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. 33. Knowledge Bases (KBs) These Days (Zaman Now)
  34. 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. 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. 36. Knowledge Bases (KBs) From Time to Time (Semantic) Knowledge Bases in 2007
  37. 37. Knowledge Bases (KBs) From Time to Time (Semantic) Knowledge Bases in 2017
  38. 38. Knowledge Bases (KBs) From Time to Time (Semantic) Knowledge Bases in 2017
  39. 39. Application: Answer Engine THEN: Search Engine
  40. 40. Application: Answer Engine NOW: Answer Engine
  41. 41. Application: Answer Engine Question: When was Soekarno born?
  42. 42. Question: When was Soekarno born? http://id.dbpedia.org/page/Soekarno Application: DBpedia-powered Answer Engine
  43. 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. 44. Application: Timeline Infographics
  45. 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. 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. 47. Application: Wikidata-powered Timeline Infographics
  48. 48. bit.ly/timelinePahlawanNasional
  49. 49. Bonus: Wikidata-powered Table https://www.wikidata.org/wiki/Wikidata:WikiProject_Jasmerah/List/IndonesianNationalHeroes
  50. 50. Bonus: Wikidata-powered Table https://www.wikidata.org/wiki/Wikidata:WikiProject_Jasmerah/List/IndonesianNationalHeroes
  51. 51. By the way, let's join Jasmerah, for better (data about the) Indonesian history! https://www.wikidata.org/wiki/Wikidata:WikiProject_Jasmerah
  52. 52. Application: Virtual Doctor
  53. 53. Application: Wikidata-powered Virtual Doctor dr Wikidata: Tell me your symptoms
  54. 54. Application: Wikidata-powered Virtual Doctor dr Wikidata: Tell me your symptoms Patient: I feel like fatigue, headache, joint pain, and vomitting
  55. 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. 56. Application: Wikidata-powered Virtual Doctor Behind the scenes http://tinyurl.com/y96cpbx8
  57. 57. Application: Wikidata-powered Virtual Doctor Behind the scenes http://tinyurl.com/y96cpbx8
  58. 58. Application: What is the news about? http://www.thejakartapost.com/news/2017/10/20/telkom-plans-to-acquire-three-foreign-firms.html
  59. 59. Application: What is the news about?
  60. 60. Application: What is the news about?
  61. 61. Application: What is the news about?
  62. 62. Application: What is the news about? Cross-dataset knowledge discovery!
  63. 63. Data Quality Symptoms of Dengue Fever do not include fever!
  64. 64. 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
  65. 65. Data Quality http://d-nb.info/1136571418
  66. 66. Data Population How to create data?
  67. 67. Data Consumption How to reduce technological learning steep for developers and end-users? Can we build more killer apps?
  68. 68. Data Scalability Are we ready for data explosion?
  69. 69. Data Fusion How to combine structured data and unstructured data (= text)?
  70. 70. Data Analytics What can be analyzed, and how fast?

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