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Knowledge Technologies: Opportunities and Challenges
The document discusses knowledge technologies and their opportunities and challenges, focusing on the semantic web and knowledge bases. It highlights the principles of the semantic web, including the use of URIs and RDF for data modeling and querying with SPARQL. Additionally, it outlines the advancements in knowledge bases, referencing Wikidata and DBpedia, and illustrates various applications powered by these technologies.
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Knowledge Technologies: Opportunities and Challenges
- 1.KNOWLEDGE TECHNOLOGIES:OPPORTUNITIES ANDCHALLENGESFariz Dararifariz@cs.ui.ac.idDec 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, GermanyBest 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
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- 6.• Knowledge Technologies:Motivations• Semantic Web• Knowledge Bases These Days (= Zaman Now)• Wikidata• DBpedia• Applications• Discussion: Challenges & OpportunitiesMenu
- 7.What if theknowledge in your brains,*can be queried by computers?*notice the plural form
- 8.What if theknowledge in your brains,*can be queried by computers?*notice the plural form
- 9.What if theknowledge in your brains,*can be queried by computers?*notice the plural form
- 10.What if theknowledge in your brains,can be queried by computers?Can you imagine what kind of advancementscan be made to humanity?
- 11.What if theknowledge in your brains,can be queried by computers?Can you imagine what kind of advancementscan be made to humanity?Stay tuned, will present you an answer to this question some slides later!
- 12.... if properlydesigned,the Semantic Web can assistthe evolution of human knowledgeas a whole.– Tim Berners-LeeInventor of the (Semantic) Web
- 13.What is theSemantic Web?
- 14.What is theSemantic Web?
- 15.What is theSemantic 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 things2. Use HTTP** URIs so people can look up those names3. When someone looks up a URI, provide useful knowledgeusing the standards: RDF and SPARQL.4. Include links to other URIs, so they can discover more thingshttps://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 Standardsthedata guythe schema guythe query guy
- 18.RDF in oneslidethe data guy• Data model, based on S-P-O triple structure (Subject, Predicate, Object)• Used for describing things, yes, every, single, thingAnd 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 typedTelkomUniversity somelink BandungTelkomUniversity locatedIn Bandung
- 19.OWL in oneslide• 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 thingsbased on existing facts.• Very simple example:owl SubClassOf bird + bird SubClassOf animal + owl EquivalentClass strigifomesNow, if Bobi is a Strigifomes, do you think Bobi is an animal?the schema guy
- 20.OWL in oneslide• 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 thingsbased on existing facts.• Very simple example:owl SubClassOf bird + bird SubClassOf animal + owl EquivalentClass strigifomesNow, if Bobi is a Strigifomes, do you think Bobi is an animal? OWL will say:the schema guy
- 21.OWL in oneslide• 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 thingsbased on existing facts.• Very simple example:owl SubClassOf bird + bird SubClassOf animal + owl EquivalentClass strigifomesNow, if Bobi is a Strigifomes, do you think Bobi is an animal? OWL will say: "YES!"the schema guy
- 22.SPARQL in oneslidethe 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 BandungBandung headOfGov RidwanKamilTelkomUniversity instanceOf UniversityIt 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 matchwith RDF data!
- 23.Knowledge Bases (KBs)These Days (Zaman Now)KB THEN KB ALMOST NOWKB NOW
- 24.Knowledge Bases (KBs)These Days (Zaman Now)KB NOW
- 25.Knowledge Bases (KBs)These Days (Zaman Now)KB NOWSubjectPredicatePredicatePredicateObjectObjectObjectReminds you of something?
- 26.Knowledge Bases (KBs)These Days (Zaman Now)KB NOWSubjectPredicatePredicatePredicateObjectObjectObjectReminds you of something?the data guy
- 27.Knowledge Bases (KBs)These Days (Zaman Now)KB NOWSubjectPredicatePredicatePredicateObjectObjectObjectReminds you of something?the data guybtw, 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 NOWSubjectPredicatePredicatePredicateObjectObjectObjectReminds you of something?the data guybtw, 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 guyhttp://tinyurl.com/yc6jsmhv
- 30.Knowledge Bases (KBs)These Days (Zaman Now)the query guyhttp://tinyurl.com/y84kyl4d
- 31.Knowledge Bases (KBs)These Days (Zaman Now)the schema guyOwlInWinnieThePooh instanceOf fictionalOwlfictionalOwl subClassOf fictionalBirdfictionalBird 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 millionsubjects! (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 millionsubjects!• 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 millionsubjects!• It loves the Semantic Web• DBpedia Indonesia is available, hosted byFaculty 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 EngineTHEN:Search Engine
- 40.Application: Answer EngineNOW:Answer Engine
- 41.Application: Answer EngineQuestion:When was Soekarno born?
- 42.Question: When wasSoekarno born?http://id.dbpedia.org/page/SoekarnoApplication: DBpedia-powered Answer Engine
- 43.Application: DBpedia-powered AnswerEngineQuestion: When was Soekarno born?Borrow Techniques fromNatural Language ProcessingSELECT ?birthDateWHERE {<http://id.dbpedia.org/resource/Soekarno> <http://dbpedia.org/ontology/birthDate> ?birthDate}SPARQL Query over DBpediahttp://id.dbpedia.org/sparql
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- 45.Application: Timeline InfographicsTask: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 manuallyTotal time spent: 24+ hours!
- 46.Application: Wikidata-powered TimelineInfographicsTask: 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 TimelineInfographics
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- 51.By the way,let's join Jasmerah, for better (data about the) Indonesian history!https://www.wikidata.org/wiki/Wikidata:WikiProject_Jasmerah
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- 53.Application: Wikidata-powered VirtualDoctordr Wikidata: Tell me your symptoms
- 54.Application: Wikidata-powered VirtualDoctordr Wikidata: Tell me your symptomsPatient: I feel like fatigue, headache,joint pain, and vomitting
- 55.Application: Wikidata-powered VirtualDoctordr Wikidata: Tell me your symptomsPatient: I feel like fatigue, headache,joint pain, and vomittingdr Wikidata: From what I know,you most likely get dengue fever!
- 56.Application: Wikidata-powered VirtualDoctorBehind the sceneshttp://tinyurl.com/y96cpbx8
- 57.Application: Wikidata-powered VirtualDoctorBehind the sceneshttp://tinyurl.com/y96cpbx8
- 58.Application: What isthe news about?http://www.thejakartapost.com/news/2017/10/20/telkom-plans-to-acquire-three-foreign-firms.html
- 59.Application: What isthe news about?
- 60.Application: What isthe news about?
- 61.Application: What isthe news about?
- 62.Application: What isthe news about?Cross-dataset knowledge discovery!
- 64.Data QualitySymptoms ofDengue Fever do not include fever!
- 65.Completeness: Is thedata completeenough? Is it of sufficient breadth anddepth?Accuracy: How accurate is the data? Isit reliable and verifiable?Timeliness: Is the data up-to-date? Isthe latest data included?Data Quality
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- 68.Data ConsumptionHow toreduce technologicallearning steep for developersand end-users?Can we build more killer apps?
- 69.Data ScalabilityAre wereadyfor dataexplosion?
- 70.Data FusionHow tocombinestructured dataand unstructureddata (= text)?
- 71.Data AnalyticsWhat canbeanalyzed, andhow fast?
Editor's Notes
- #2 Credits:https://www.pexels.com/search/network/
- #3 Credits:https://www.iconfinder.com/search/?q=award&price=freehttps://www.iconfinder.com/search/?q=baby&price=free
- #7 https://www.pexels.com/photo/arrangement-blur-clear-cutlery-291767/
- #8 https://pixabay.com/p-308580/?no_redirect
- #9 https://www.goodfreephotos.com/albums/vector-images/colorful-brain-map-vector-clipart.png
- #10 https://www.iconfinder.com/search/?q=computer&price=free
- #13 https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Sir_Tim_Berners-Lee.jpg/1200px-Sir_Tim_Berners-Lee.jpghttps://www-sop.inria.fr/acacia/cours/essi2006/Scientific%20American_%20Feature%20Article_%20The%20Semantic%20Web_%20May%202001.pdf
- #17 http://linkeddatabook.com/editions/1.0/
- #21 http://bukuerlangga.co.id/3374-3493-large/pop-up-bobi-si-burung-hantu-.jpg
- #22 http://bukuerlangga.co.id/3374-3493-large/pop-up-bobi-si-burung-hantu-.jpg
- #24 http://www.slate.com/content/dam/slate/articles/technology/technology/2012/03/120315_TECH_encyclopediaB.jpg.CROP.original-original.jpg
- #37 http://lod-cloud.net/
- #38 http://lod-cloud.net/
- #39 http://lod-cloud.net/
- #45 Credits:https://www.showeet.com/wp-content/gallery/2-0070-timeline-infographics-widescreen/04-Timeline-Infographics-PowerPoint.PNG
- #53 https://symptomsbeta.webmd.com/#/symptoms
- #54 https://www.pexels.com/search/doctor/
- #55 https://www.pexels.com/search/doctor/
- #56 https://www.pexels.com/search/doctor/
- #57 https://www.pexels.com/search/doctor/
- #58 https://www.pexels.com/search/doctor/
- #59 Knowledge discovery
- #60 Knowledge discovery
- #61 Knowledge discovery
- #62 Knowledge discovery
- #63 Knowledge discovery
- #71 Machine learning/data mining
- #72 Machine learning/data mining