Semantic Web: an introduction (2013)

Short seminar about the Semantic Web, given for the "Artificial Intelligence" course at Politecnico di Torino (academic year 2012/2013)

Transcript

1. THE SEMANTIC WEB AN INTRODUCTION LUIGI DE RUSSIS
2. THE WEB IS A WEB OF DOCUMENT FOR PEOPLE, NOT FOR MACHINES
3. THE SEMANTIC WEB IS A WEB OF DATA Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
4. LET’S THINK!
5. EXERCISE: BUILD A MUSIC CATALOG Comprehensive guide to music across the world Web-based With always-updated information about each artist
6. HOW? WHAT ARE THE PROBLEMS? WHAT ABOUT DATA REPLICATION? WHAT ABOUT DATA SYNCHRONIZATION? …?
7. SOLUTION #1 HOW? Site editors roam the Web for new facts and update the site manually WHAT ARE THE PROBLEMS? A lot of people need to continuously roam the Web; the site will get soon out-of-date WHAT ABOUT DATA? Data is replicated and not up-to-date with new facts
8. SOLUTION #2 HOW? Site editors roam the Web for new data and write a program to extract the information WHAT ARE THE PROBLEMS? Code needs to be updated each time a new site is found; the site will get out-of-date, soon or later… WHAT ABOUT DATA? Data is replicated and not up-to-date
9. SOLUTION #3 HOW? Site editors browse the Web for new data via APIs, and write some code to incorporate the information WHAT ARE THE PROBLEMS? Code needs to be updated each time a new site is found and/or an API is changed; the site will get out-of-date, soon or later… WHAT ABOUT DATA? Data is replicated and not up-to-date
10. SOLUTION #4 HOW? Site editors choose to use some external, public datasets (e.g., Wikipedia, MusicBrainz, …) WHAT ARE THE PROBLEMS? No problem WHAT ABOUT DATA? Data is immediately available, not as APIs or hidden on a Web site. Information can be extracted using standard queries or HTTP requests.
11. IN SHORT… Use the Web of Data as a Content Management System Use the community at large as content editor AN EXAMPLE: BBC MUSIC http://www.bbc.co.uk/music SOLUTION #4
12. DATA ON THE WEB IS NOT ENOUGH! we need a proper infrastructure DATA SHOULD BE AVAILABLE ON THE WEB accessible via standard Web technologies DATA SHOULD BE INTERLINKED OVER THE WEB i.e., data can be integrated over the Web THIS IS WHERE SEMANTIC WEB COME IN
13. FUNDAMENTALS
14. To a computer, the Web is a flat, boring world, devoid of meaning. This is a pity, as in fact documents on the Web describe real objects and imaginary concepts. […] Adding semantics to the Web involves two things: allowing documents which have information in machine-readable forms, and allowing links to be created with relationship values. Only when we have this extra level of semantics we will be able to use computer power to help us exploit the information to a greater extent than our own reading. TIM BERNERS-LEE, 1994
15. WHAT IS THE RELATIONSHIP WITH AI? INFLUENCE Some technologies in the Semantic Web benefited a lot from AI research and development (and viceversa) DIFFERENT GOALS Artificial Intelligence approach: build smarter machines, teach computers to infer the meaning of data Semantic Web approach: have smarter data, make data easier for machines to find, access and process
16. RESOURCE AND DESCRIPTION RESOURCE every document “reachable” on the Web no matter the content, format, language, etc. RESOURCE DESCRIPTION independent from the format standard language (metadata)
17. RESOURCE AND DESCRIPTION Resources
18. RESOURCE AND DESCRIPTION Description
19. RESOURCE AND DESCRIPTION Description Title Author Date Topic Quality Title Author Date Topic
20. URIS unambiguous names for resources RDF a common data model to connect and describe resources SPARQL access to the data model RDFS, OWL common description languages OWL, RIF reasoning (mainly logic inference)
21. MODELING DATA
22. EXAMPLE: BOOKSTORE Represent the following data about the AI book as a set of relations Title: “Artificial Intelligence: A Modern Approach” Author: Russel, Stuart and Norvig, Peter Publisher: Prentice Hall ISBN: 978-0136042594
23. EXAMPLE: BOOKSTORE http://...isbn/9780136042594 Resource
24. EXAMPLE: BOOKSTORE http://...isbn/9780136042594 Artificial Intelligence: A Modern Approach Literal
25. EXAMPLE: BOOKSTORE http://...isbn/9780136042594 Artificial Intelligence: A Modern Approach title
26. EXAMPLE: BOOKSTORE http://...isbn/9780136042594 Artificial Intelligence: A Modern Approach Prentice Hall Russel, Stuart Norvig, Peter title publisher author author
27. RDF: RESOURCE DESCRIPTION FRAMEWORK STRUCTURED IN STATEMENTS SUBJECT a resource (URI) PREDICATE a verb, property or relationship OBJECT a resource or a literal string
28. EXAMPLE: BOOKSTORE http://...isbn/9780136042594 Artificial Intelligence: A Modern Approach Prentice Hall Russel, Stuart Norvig, Peter title publisher author author Subject Object Object Object Object Predicate Predicate Predicate
29. EXAMPLE: BOOKSTORE http://...isbn/9780136042594 Artificial Intelligence: A Modern Approach title RDF IN XML SYNTAX <rdf:RDF xmlns:rdf=http://www.w3.org/…/22-rdf-syntax-ns#> <rdf:Description about=“http://... isbn/9780136042594”> <title>Artificial Intelligence: A Modern Approach</title> </rdf:Description> </RDF>
30. EXAMPLE: BOOKSTORE http://...isbn/9780136042594 Artificial Intelligence: A Modern Approach title RDF IN TURTLE <http://... isbn/9780136042594> title “Artificial Intelligence: A Modern Approach”
31. LINKIN’ DATA
32. EXAMPLE: BOOKSTORE Represent the following data about the Italian translation of the AI book as a set of relations Title: “Intelligenza artificiale. Un approccio moderno” Author: Russel, Stuart and Norvig, Peter Publisher: Prentice Hall ISBN: 978-8871925936 Original ISBN: 978-0136042594
33. EXAMPLE: BOOKSTORE http://...isbn/9788871925936 Intelligenza Artificiale. Un approccio moderno Prentice Hall Russel, Stuart Norvig, Peter title publisher creator creator http://...isbn/9780136042594 original
34. EXAMPLE: BOOKSTORE http://...isbn/9788871925936 Intelligenza Artificiale. Un approccio moderno Prentice Hall title publisher http://...isbn/9780136042594 original http://...isbn/9780136042594 Artificial Intelligence: A Modern Approach Prentice Hall title publisher
35. EXAMPLE: BOOKSTORE http://...isbn/9788871925936 Intelligenza Artificiale. Un approccio moderno Prentice Hall title publisher http://...isbn/9780136042594 original http://...isbn/9780136042594 Artificial Intelligence: A Modern Approach Prentice Hall title creator same URI, same resource
36. EXAMPLE: BOOKSTORE http://...isbn/9780136042594 Artificial Intelligence: A Modern Approach Prentice Hall Russel, Stuart Norvig, Peter title publisher author author http://...isbn/9788871925936 Intelligenza Artificiale. Un approccio moderno Prentice Hall title publisher original Russel, Stuart Norvig, Peter creator creator
37. EXAMPLE: BOOKSTORE http://...isbn/9780136042594 Artificial Intelligence: A Modern Approach Prentice Hall Russel, Stuart Norvig, Peter title publisher author author http://...isbn/9788871925936 Intelligenza Artificiale. Un approccio moderno Prentice Hall title publisher original Russel, Stuart Norvig, Peter creator creator What about merging creator and author? In RDF, it is not possible!
38. PROBLEM: FIELD NAMES ARE ARBITRARY Synonyms : author or creator or maker or contributor or… Singular or plural: author or authors SOLUTION: STANDARDS general or domain-specific
39. DUBLIN CORE GENERAL VOCABULARY Dublin Core Metadata Initiative (DCMI) http://dublincore.org BUILDING BLOCKS TO DEFINE METADATA FOR THE SEMANTIC WEB Define title, contributor, publisher, license, date, language, etc.
40. PROBLEM: FIELD VALUES ARE ARBITRARY Value type: string, date, integer, … Value format: “Norvig, Peter” or “Norvig, P.” or “Peter Norvig” or… Value restrictions: one value or multiple values (how many?) SOLUTIONS Standards Controlled vocabulary (close list of terms) Semantically rich descriptions to support search (RDFS and/or OWL)
41. FRIEND OF A FRIEND (FOAF) GENERAL ONTOLOGY Describe persons, their activities and their relations to other people and objects http://www.foaf-project.org BUILDING BLOCKS TO DEFINE STRUCTURED RELATIONS BETWEEN PEOPLE Define name, familyName, givenName, knows, age, nick, etc.
42. EXAMPLE: BOOKSTORE http://...isbn/9780136042594 Artificial Intelligence: A Modern Approach Prentice Hall Russel, Stuart Norvig, Peter dc:title dc:publisher dc:creator dc:creator foaf: http://xmlns.com/foaf/spec dc: http://purl.org/dc/terms foaf:name foaf:name foaf:name
43. http://...isbn/9780136042594 Norvig, Peter dc:creator foaf:name http://...isbn/9780136042594 Norvig, Peter author WHY?
44. RDF SCHEMA
45. RDF SCHEMA SCHEMA Definition of the nodes and predicates used in a RDF document DOMAIN AND RANGE RDFS describes properties in terms of classes of resource to which they apply (from a “domain” to a “range”)
46. EXAMPLE RDF data http://elite.polito.it/people/derussis teaches http.//bit.ly/lingambmult
47. http://elite.polito.it/people/derussis teaches EXAMPLE RDF data RDF schema http.//bit.ly/lingambmult Teacher Person teaches Course domain range subClassOf type type
48. http://...isbn/9780136042594 Norvig, Peter dc:creator foaf:name http://...isbn/9780136042594 Norvig, Peter author BACK TO THE BOOKSTORE EXAMPLE…
49. http://...isbn/9780136042594 Norvig, Peter dc:creator foaf:name http://...isbn/9780136042594 Norvig, Peter author BACK TO THE BOOKSTORE EXAMPLE… dc:creator has range Agent, i.e. a class (resource), not a literal: we use an anonymous class for this scope. Finally, foaf:Name has range rdfs:Literal. anonymous class
50. RDFS EXPRESSIVITY SIMPLE RELATIONSHIP BETWEEN THINGS RDFS provides a vocabulary to express relationship between things (e.g., subClassOf or type) AVOID COMPLEX RELATIONSHIP RDFS cannot describe data in terms of set of operations (e.g., unionOf), equivalence (e.g., sameAs) or cardinality (e.g., allValueFrom)
51. OWL
52. WEB ONTOLOGY LANGUAGE WHAT? OWL (version 2): a knowledge representation language Designed to formulate, exchange and reason with knowledge about a domain of interest
53. WEB ONTOLOGY LANGUAGE INDIVIDUALS, CLASSES AND PROPERTIES “Politecnico di Torino is a university” “Politecnico di Torino has a professor named Elio Piccolo” “Politecnico di Torino” is a object: an individual in OWL2 “university” is a category: a class in OWL2 “has a professor” is a relation: a property in OWL2 “Elio Piccolo” is an individual, too
54. WEB ONTOLOGY LANGUAGE EXPRESSIVITY Designed to represent rich and complex knowledge about things, group of things, and their relations LOGIC-BASED Knowledge expressed in OWL can be reasoned with a computer program to verify its consistency or to make implicit knowledge explicit
55. WEB ONTOLOGY LANGUAGE LINKED Ontologies in OWL can be published on the Web and may refer or be referred from other OWL ontologies CHOOSE THE SYNTAX YOU LIKE Various syntaxes available for OWL, for different purposes (RDF/XML, Turtle, Manchester, etc.)
56. EXAMPLE: BOOKSTORE Intelligenza Artificiale. Un approccio moderno Prentice Hall dc:title dc:publisher Artificial Intelligence: A Modern Approach Prentice Hall dc:title dc:publisher Libro Book rdfs:type rdf:type owl:sameAs http://...isbn/9788871925936 http://...isbn/9780136042594
57. HANDS ON OWL
58. EXAMPLE: BOOKSTORE It is time to sell the books we modeled. Users must have the possibility to search in our book catalog. We need to describe our store and add some other information about the books. GoodRelations helps in realizing such an example: http://www.heppnetz.de/projects/goodrelations/
59. Bookstore_1 Offering_1 TypeAndQuantity Node_1 UnitPriceSpecification_1 AIBook_en item:Book QuantitativeValue Integer_1 gr:Sell 120.0 “EUR” 1.0 1132 gr:ActualProductOrServiceInstance gr:ProductOrService gr:offers gr:includeObject gr:hasBusinessFunction gr:hasPriceSpecification gr:hasCurrency gr:hasCurrencyValue gr:amountOfThisGood gr:typeOfGood rdf:type rdfs:subClassOf rdf:type item:hasTotalPages gr:hasValue gr:Offering rdf:type
60. DESCRIBE THE BUSINESS ENTITY default:BookStore_1 a gr:BusinessEntity ; gr:legalName “bookstore.com Ltd.”^^xsd:string . Bookstore_1
61. DESCRIBE THE OFFERED ITEMS default:AIBook_en a item:Book, gr:ActualProductOrServiceInstance ; item:hasTotalPages default:QuantitativeValueInteger_1 . AIBook_en default:QuantitativeValueInteger_1 a gr:QuantitativeValueInteger ; gr:hasValue “1132”^^xsd:integer . QuantitativeValue Integer_1
62. DESCRIBE THE OFFER default:Offering_1 a gr:Offering ; gr:hasBusinessFunction gr:Sell ; gr:hasPriceSpecification default:UnityPriceSpecification_1 ; gr:includeObject default:TypeAndQuantityNode_1 . Offering_1 LINK THE OFFER TO THE BUSINESS ENTITY default:BookStore_1 gr:offers default:Offering_1
63. DESCRIBE THE OFFER default:TypeAndQuantityNode_1 a gr:TypeAndQuantityNode ; gr:amountOfThisGood “1.0”^^xsd:float ; gr:typeOfGood default:AIBook_en . default:UnitPriceSpecification_1 a gr:UnitPriceSpecification ; gr:hasCurrency “EUR”^^xsd:string ; gr:hasCurrencyValue “120.0”^^xsd:float . TypeAndQuantity Node_1 UnitPriceSpecification_1
64. QUERY THE WHOLE! PREFIX gr: <http://purl.org/goodrelations/v1#> PREFIX item: <http://www.elite.polito.it/ontologies/example/item#> SELECT ?offering WHERE { ?offering rdf:type gr:Offering . ?offering gr:includesObject ?object . ?object gr:typeOfGood ?item . ?item rdf:type item:Book . } How to get all the available offer for the book? SPARQL
65. QUERY THE WHOLE! ?item rdf:type item:Book . SPARQL ?item item:Book rdf:type
66. QUERY THE WHOLE! ?object gr:typeOfGood ?item . SPARQL ?object AIBook_en item:Book gr:typeOfGood rdf:type
67. QUERY THE WHOLE! ?offering gr:includesObject ?object . SPARQL ?offering TypeAndQuantity Node_1 AIBook_en item:Book gr:includeObject gr:typeOfGood rdf:type
68. QUERY THE WHOLE! ?offering rdf:type gr:Offering . SPARQL ?offering TypeAndQuantity Node_1 AIBook_en item:Book gr:includeObject gr:typeOfGood rdf:type gr:Offering rdf:type
69. QUERY THE WHOLE! SELECT ?offering SPARQL Offering_1 TypeAndQuantity Node_1 AIBook_en item:Book gr:includeObject gr:typeOfGood rdf:type gr:Offering rdf:type
70. REFERENCES Semantic Web standards: http://w3c.org/standards/semanticweb Semantic Web Wiki: http://semanticweb.org Semantic Web FAQ: http://www.w3c.org/2001/sw/SW-FAQ Book: A Semantic Web Primer (http://www.semanticwebprimer.org) Book: Semantic Web Programming (http://semwebprogramming.org) Last access: 04 June 2013
71. THANKS! Luigi De Russis http://elite.polito.it
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