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PRESENTED BY: ANDRÉ MAZAYEV
ANDRE.MAZ.90[ AT]GMAIL[DOT]COM](https://image.slidesharecdn.com/semanticweb-160908121036/85/Semantic-web-An-overview-1-320.jpg)
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Semantic web: An overview
- 1. Semantic Web PRESENTED BY: ANDRÉ MAZAYEV ANDRE.MAZ.90[ AT]GMAIL[DOT]COM
- 2. Semantic Web? Whaaat? ◦ What semantic web means? ◦ Smarter web!! Duuuh!
- 3. Semantic Web? Whaaat? ◦ What semantic web means? ◦ Smarter web!! Duuuh! ◦ Ok. But more specifically? ◦ It’s a web where it is easier to find stuff on internet
- 4. Semantic Web? Whaaat? ◦ What semantic web means? ◦ Smarter web!! Duuuh! ◦ Ok. But more specifically? ◦ It’s a web where it is easier to find stuff on internet ◦ Yeah! But how? ◦ Hmmmmm……
- 5. Web 2.0 ◦ Search Process ◦ Refine search as you go ◦ The user is guiding the search accordingly to the results that are shown ◦ Search engine is only performing syntax based pattern match ◦ Plus some features to improve performance and accuracy ◦ Semantics are not used or used in a limited way during the search process
- 6. Syntax and Semantics ◦ Syntax ◦ About form ◦ Semantics ◦ About meaning
- 7. Syntax and Semantics ◦ Syntax ◦ Green, Yellow, Red ◦ Semantics ◦ Green = Go ◦ Yellow = Better stop ◦ Red = Stop Traffic Light Adapted from: Semantic Web from the 2013 Perspective
- 8. User’s Web Example Example of dumb web ◦ Goal ◦ Find the telephone number of James Bond
- 9. User’s Web Example Example of dumb web ◦ Goal ◦ Find the telephone number of James Bond ◦ For humans the answer is easy to find ◦ James Bond’s telephone number is 1-800-555-0199 ◦ James Bond is a fictional MI6 agent ◦ Since it’s a fictional agent we can infer that the number must be fake
- 10. Machine’s Web Example Example of dumb web Source code of dumb web ◦ For machines find Bond’s number is a hard task ◦ No machine “readable” semantics ◦ Current Web ◦ Created for document sharing ◦ Instead of data sharing ◦ Adapted for Human to Human ◦ Machine to Machine communication is difficult
- 11. Smart vs Dumb Web Example of dumb web Example of smart web
- 12. Smart vs Dumb Web Visually both pages are identical Smart page carries much more “meaning” Example of dumb web Example of smart web
- 13. Smart vs Dumb Web Source code of smart webSource code of dumb web
- 14. Source code analysis Contains more machine friendly structure ◦ Vocabulary is defined ◦ Data is structured ◦ Data is enriched The data can be represented as a graph Source code of smart web
- 15. James Bond 1-800-555-0199 James Bond typeof name telephone Person Source code analysis Source code of smart web
- 16. James Bond 1-800-555-0199 James Bond typeof name telephone Person Source code analysis With structured data it’s easy for a machine to find Bond’s telephone number Source code of smart web
- 17. Graph analysis ◦ Simple statements ◦ Subject – Predicate – Object ◦ All elements have their own URL ◦ Data is structured ◦ Data can be explored by machines James Bond 1-800-555-0199 James Bond typeof name telephone Person URL URL URL
- 18. Structured Data Tool Source code of a page with semantic markup Extracted data
- 19. Structured Data Tool Extracted data ◦ Data recognized by Google’s web crawler ◦ With structured data answers are easy to get ◦ What? ◦ Where? ◦ When Open?
- 20. Semantic Web Present Future Web of Documents Web of Data Small Change Big Difference ◦ Data is explicit ◦ Data is connected ◦ Data can be explored by machines ◦ Nontrivial connections can be found ◦ Demo ◦ RelFinder
- 22. RDF
- 23. RDF ◦ Resource Description Framework ◦ Simple statements (triples) ◦ Subject – Predicate – Object ◦ Building block of RDFS and OWL ◦ Multiple serialization formats ◦ RDF/XML ◦ Turtle ◦ N-Triples Bond example in Turtle
- 24. RDFS
- 25. RDFS ◦ RDF Schema ◦ Limited expressivity ◦ Describes classes, subclasses and properties ◦ Primary focused on “is a” and “sub class of” relationships Vocabulary Canine Animal Human Mammal Feline Reptile Taxonomy Animal MammalReptile Human Canine Feline subClassOf subClassOf subClassOf subClassOf subClassOf
- 26. SPARQL
- 27. SPARQL ◦ SPARQL Protocol And RDF Query Language ◦ SQL-Like structure James Bond 1-800-555-0199 James Bond typeof name telephone Person Graph
- 28. SPARQL ◦ SPARQL Protocol And RDF Query Language ◦ SQL-Like structure James Bond 1-800-555-0199 James Bond typeof name telephone Person Graph Goal: Find Bond’s Number
- 29. SPARQL ◦ SPARQL Protocol And RDF Query Language ◦ SQL-Like structure James Bond 1-800-555-0199 James Bond typeof name telephone Person Graph Query Goal: Find Bond’s Number
- 30. SPARQL ◦ SPARQL Protocol And RDF Query Language ◦ SQL-Like structure James Bond 1-800-555-0199 James Bond typeof name telephone Person Graph Answer Query Goal: Find Bond’s Number
- 31. OWL
- 32. OWL ◦ Web Ontology Language ◦ Highly expressive ◦ Brings expressivity of logic to Semantic Web ◦ More expressive than RDFS ◦ Allows to express ◦ Constraints ◦ Cardinality ◦ Unions ◦ Intersections ◦ Etc. Resource that has property hasParent with value Bond belongs to a class named BondChild OWL Restriction Note: Often the concepts of taxonomies and ontologies overlap and used to describe same thing
- 33. SWRL
- 34. SWRL ◦ Semantic Web Rule Language ◦ Combines parts from OWL and Datalog ◦ Rule syntax ◦ If body (antecedent) then assert head (consequent) x3 is x1’s uncle
- 35. Under Development ◦ Pending questions ◦ How to ensure security of data? ◦ How to validate new data? ◦ Is source data reliable?
- 36. Data Silos ◦ Each application has its own ◦ Goals ◦ Vocabularies ◦ Knowledge base ◦ Not integrated with other data systems ◦ May have overlapping data Application 1 Application 2 Application 3 Sensor Network Gateway Server Application Data Source Relational DB Relational DB
- 37. Semantic Bridges Sensor Network Gateway Server Data Source Relational DB Relational DB RDB Parser CSV Parser WEB Parser RDF Interfaces Combined RDF Model Combined Knowledge Model Application 1 Application 2 Application 3 Application RDB Parser CSV Parser WEB Parser Model Knowledge Model Application 1 Application 2 Application 3 er RDB Parser CSV Parser WEB Parser RDF Interfaces Combined RDF Model Combined KnowledgeModel Application 1 Application 2 Application 3 Application CSV Parser WEB Parser Application 2 Application 3
- 38. Semantic Bridges Sensor Network Gateway Server Data Source Relational DB Relational DB RDB Parser CSV Parser WEB Parser RDF Interfaces Combined RDF Model Combined Knowledge Model Zoom Application 1 Application 2 Application 3 Application RDB Parser CSV Parser WEB Parser Model Knowledge Model Application 1 Application 2 Application 3 er RDB Parser CSV Parser WEB Parser RDF Interfaces Combined RDF Model Combined KnowledgeModel Application 1 Application 2 Application 3 Application CSV Parser WEB Parser Application 2 Application 3
- 39. Data Integration Data Sets Combined RDF Model Combined Knowledge Model ◦ Data from different sources is combined into a common model ◦ The whole is greater than the sum of its parts ◦ New knowledge can be obtained
- 40. Data Integration Animal MammalReptile Human Canine Feline subClassOf subClassOf subClassOf subClassOf subClassOf Wolves Terriers Hounds subClassOf subClassOf subClassOf Foundation Ontology Extended Ontology ◦ Foundation ontologies transcend boundaries of single knowledge domain ◦ Common environment for ◦ Different terminologies ◦ Different knowledge domains ◦ Makes data integration easier ◦ Can be done (semi) automatically ◦ Easier to obtain new knowledge
- 41. M3 Framework ◦ Four data sources ◦ Different domains ◦ Overlapping data ◦ Same vocabulary ◦ Combined knowledge model Adapted from: Machine-to-Machine Measurement (M3) Framework
- 42. M3 Framework ◦ Smart Band sends a set of measurements about user ◦ One of the measurements is body temperature Adapted from: Machine-to-Machine Measurement (M3) Framework
- 43. M3 Graph View Adapted from: Machine-to-Machine Measurement (M3) Framework
- 44. M3 Framework ◦ Naturopathy expert describes lemon and it’s properties ◦ Lemon is good to treat cold Adapted from: Machine-to-Machine Measurement (M3) Framework
- 45. M3 Graph View Adapted from: Machine-to-Machine Measurement (M3) Framework
- 46. M3 Framework ◦ Doctor describes High Fever as symptom of Cold ◦ Given ◦ Doctor’s info ◦ Lemon’s properties ◦ Framework can infer that ◦ Lemon is good to treat High Fever Adapted from: Machine-to-Machine Measurement (M3) Framework
- 47. M3 Graph View Adapted from: Machine-to-Machine Measurement (M3) Framework
- 48. M3 Framework ◦ User creates a rule: ◦ If body temperature is higher than 38 ◦ Then user has High Fever ◦ Given ◦ Sensor measurement ◦ User’s rule ◦ Doctor’s info ◦ Framework can infer that ◦ User has Cold Adapted from: Machine-to-Machine Measurement (M3) Framework
- 49. M3 Graph View Adapted from: Machine-to-Machine Measurement (M3) Framework
- 50. M3 Framework ◦ Given all the data ◦ Framework can recommend to the user a lemon tea to treat the cold Adapted from: Machine-to-Machine Measurement (M3) Framework
- 51. M3 Graph View Adapted from: Machine-to-Machine Measurement (M3) Framework
- 52. Linked Open World ◦ Linked Open Data ◦ Data repositories (DataHub, Data.gov, etc.) ◦ Share data to generate new data ◦ Linked Open Vocabularies ◦ Vocabularies repositories ◦ Facilitates data integration ◦ Linked Open Rules ◦ Rules repositories ◦ Concept only ◦ Linked Open Services ◦ Service repositories ◦ Concept only
- 53. Thanks for your attention