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The Semantic Web Turns Ten 11.11.2011, Milano Emanuele Della Valle

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1 The Semantic Web Turns Ten , Milano Emanuele Della Valle

2 Search Computing Share, Remix, Reuse Legally This work is licensed under the Creative Commons Attribution 3.0 Unported License. Your are free: to Share to copy, distribute and transmit the work to Remix to adapt the work Under the following conditions Attribution You must attribute the work by inserting –© applied-semantic-web.org at the end of each slide –a credits slide stating -These slides are partially based onThe Semantic Web Turns Ten by Emanuele Della Valle web.org/slides/2011/11/SemanticWebTurns10.ppt To view a copy of this license, visit 2

3 Search Computing Agenda Introduction and Motivation Data Interchange on the Web: RDF Querying the Semantic Web: SPARQL Modelling data and knowledge for the Semantic Web: RDF-S and OWL Conclusions 3

4 Search Computing Introduction The Web Today Large number of integrations - ad hoc - pair-wise Too much information to browse, need for searching and mashing up automatically Each site is understandable for usComputers dont understand much ? Search & Mash-up Engine Millions of Applications

5 Search Computing Introduction The Problem: Semantic Gap Sensor Data Semantic Gap Symbolic Description Missing building No existing streets

6 Search Computing Introduction Understanding Means Bridging the Gap 6 understanding Sensor Data Symbolic Description

7 Search Computing Introduction Do We Really Know What Understanding Means? 7 [ source ]http://www.thefarside.com/

8 Search Computing Introduction Two ways for computer to understand Smart Machine Smart Data 8

9 Search Computing Introduction Smart Machines Working examples found on the Web Image Processing –retrievr: find by sketching Audio Processing –midomi: find by singing […] Natural Language Processing –semantic proxy: bout.html bout.html Sensor Data Symbolic Description Image Processing Audio Processing Natural Language Processing […]

10 Search Computing Introduction Smart Machines alone cannot bridge the gap … Natural Language Processing (NLP) meets Image Processing (IP) NLP: What does your eye see? IP: I see a sea NLP: You see a c? IP: Yes, what else could it be? [Source NLP Related Entertainment Sensor Data Symbolic Description Image Processing Natural Language Processing seac Semantic Gap

11 Search Computing Introduction … smart data are need 11 Sensor Data Symbolic Description Image Processing Natural Language Processing seac smart data Natural Language Processing (NLP) meets Image Processing (IP) NLP: What does your eye see? IP: I see a wordnet:word-seawordnet:word-sea NLP: mmm, I see a wordnet:word-cwordnet:word-c IP: I believe we have different understanding of the world … NLP: So do I The Semantic Web offers a set of standards that lowers the barriers to employ smart data at large scale

12 Search Computing Introduction What a machine understands of the Web What we say to Web agents " For more information visit my company Web site... What they hear " blah blah blah blah blah blah blah blah blah blah... Jet this is enought to train them to achive tasks for us [ source ]http://www.thefarside.com/

13 Search Computing Introduction What does Google understand? Understanding that [page1] links [page2] page2 is interesting Google is able to rank results! The heart of our software is PageRank, a system for ranking web pages […] (that) relies on the uniquely democratic nature of the web by using its vast link structure as an indicator of an individual page's value. 13

14 Search Computing Introduction The Semantic Web 1/4 The Semantic Web is not a separate Web, but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation. The Semantic Web, Scientific American Magazine, Maggio Key concepts an extension of the current Web in which information is given well-defined meaning better enabling computers and people to work in cooperation. –Both for computers and people

15 Search Computing Introduction The Semantic Web 2/4 The Semantic Web is not a separate Web, but an extension of the current one […] 15 Web 1.0 The Web Today

16 Search Computing Introduction The Semantic Web 3/4 The Semantic Web […], in which information is given well-defined meaning […] 16 Human understandable butonly machine-readable Human and machineunderstandable ? Web 1.0 Semantic Web

17 Search Computing Introduction The Semantic Web 4/4 17 Semantic Web Fewer Integration - standard - multi-lateral […] better enabling computers and people to work in cooperation. Even More Applications Easier to understand for peopleMore understandable for computers Semantic Mash-ups & Search

18 Search Computing Introduction Linked Data Standards 18 WebMGS 2010, View the full talk at !http://www.ted.com/talks/view/id/484

19 Search Computing Introduction Linking Open Data Project Goal: extend the Web with data commons by publishing open data sets using Semantic Web techs 19 Visit !http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData

20 Search Computing Introduction Example: BIO2RDF 20 Peter Ansell, Model and prototype for querying multiple linked scientific datasets, Future Generation Computer Systems, Volume 27, Issue 3, March 2011, Pages

21 Search Computing Introduction data.gov and data.gov.uk 21 [source: J. Hendler WIMS11 Key Note – ]http://wims.vestforsk.no/slides/jimhendler.pdf

22 Search Computing Introduction 22 And a growing number of apps is appearing :-) atalog/apps/http://explore.data.gov/c atalog/apps/

23 Search Computing Introduction Industrial Uptake: BBCs Artist as Linked Data Description of the artist U2 U2 23 HTML: RDF :

24 Search Computing Introduction Industrial Uptake: Best Buy Since Fall products Using RDFa (= embedded in HTML) Pages with RDFa higher in Google ranking BestBuy claims 30% more traffic! Yahoo reports 15% higher click-through rat 24

25 Search Computing Introduction Industrial Uptake: Best Buy - example of of 5 RDFa

26 Search Computing Introduction Industrial Uptake: Best Buy – SEO on Google https://www.google.com/search?q=Nikon Megapixel+Digital+SLR+Camera 26 Sponsored Links

27 Search Computing Introduction Industrial Uptake: Facebook Open Graph 1/2 Use RDFA with some FB specific vocabulary og:title - The title of your object, e.g., "The Rock". og:type - The type of your object, e.g., "movie". og:image - An image URL og:url - The permanent ID of your object og:description - A one to two sentence description of your object. og:site_name - If your object is part of a larger web site, the name which should be displayed for the overall site. e.g., "IMDb". 27

28 Search Computing Introduction Industrial Uptake: Open Graph Usage Statistics sites are using Open Graph! 28 [Source:

29 Search Computing Introduction Semantic Web layer cake 29 Standardized Under Investigation Already Possible [ source ]http://www.w3.org/2007/03/layerCake.png

30 Search Computing Data Interchange: RDF 30

31 Search Computing RDF in a nutshell Looking for a flexible data model Why Application are always changing (competitive environment) People are always adding more features Graceful evolution is important Optimal: relational model Relational model is remarkably flexible Supports graceful evolution –Change => Add another table –Existing queries are unaffected Easily accommodates new data –Without affecting existing queries Allows data to be easily combined ("joined") in new ways 25+ years of relational database experience © E. Della Valle - CEFRIEL

32 Search Computing RDF in a nutshell Resource Description Framework The adaptation of the relational model to the Web give rise to RDF From T-tuples to Triples Any relational data can be represented as triples Row Key --> Subject Column --> Property Value --> Value 32

33 Search Computing RDF in a nutshell Representing relational data in RDF (almost) E.g., geographical data Represented in RDF (almost) 33 CityCountryPopulation IT.2Italy CityName IT.2Milano IT.2Milan IT.2Mailand IT.2 Italy MilanoMilanMailand Country Population Is a City Legend resource literal Name

34 Search Computing RDF in a nutshell Representing relational data in RDF (almost) Two important problems Once out of the database internal ID (e.g., IT.2) becomes useless Once out of the database internal names of schema element (e.g., City) becomes useless as well RDF solves it by using URI Internal ID should be replaced by URI Internal schema names should be replaced by URI Values do (always) not need to be URI-fied MilanoMilan Mailand Legend resource literal

35 Search Computing Which URI should we use? Popular ones! Data merge will take place automatically! RDF in a nutshell Representing data in RDF Q/A 1/ =

36 Search Computing Where do I find popular URIs? A difficult question with no clear answer The best place to keep an eye on is –the Linking Open Data Project ojects/LinkingOpenData ojects/LinkingOpenData –and in particular the following pages of the Wiki -Data Sets ngOpenData/DataSets ngOpenData/DataSets -Semantic Web Search Engines ngOpenData/SemanticWebSearchEngines ngOpenData/SemanticWebSearchEngines -Common Vocabularies ngOpenData/CommonVocabularies ngOpenData/CommonVocabularies RDF in a nutshell Representing data in RDF Q/A 2/4 36

37 Search Computing What is a value? When shall we URI-fy a value? Literals cannot be used to merge different data set E.g., having chosen to represent postal codes as a string, merging different data sets using postal codes is impossible –20100 may refer to lots of different thing on the Web e.g., try URI-fy any value that can be eventually used to merge different dataset and leave the other values as literals RDF in a nutshell Representing data in RDF Q/A 3/ = ?

38 Search Computing What if I cannot thing about a good URI? When no go URI exists, you can use blank nodes ( ) The following relational data … … can be translated in RDF, in the BIO vocabulary [1], as follows [1] RDF in a nutshell Representing data in RDF Q/A 4/4 38 PersonBio EventDate SofiaBirth SofiaMarriage

39 Search Computing RDF in a nutshell Other data structure in RDF Trees can be represented in RDF Anything can be represented in RDF 39

40 Search Computing RDF in a nutshell Serializing RDF Three alternatives to write triples, in 1.RDF/XML: Standard serialization in XML value – e.g., –Check-out the triples using 2.NTriples: Simple (verbose) reference serialization (for specifications only) value. 3.N3 and Turtle: Developer-friendly serializations :subject :property value. 40

41 Search Computing RDF in a nutshell Serializing RDF in Turtle - namespaces URI terms can be abbreviated using rdf:. rdf:type geo:P. = 'a' a geo:P. 41

42 Search Computing RDF in a nutshell Serializing RDF in Turtle - Literals Literals: "Milano" Typed literals: "3.14"^^xsd:float Literals with language tags: geo:. geo:population " "^^xsd:integer. geo:name "Milan". geo:alternateName geo:alternateName geo:alternateName 42

43 Search Computing RDF in a nutshell Serializing RDF in Turtle - Convience Syntax Abbreviating repeated subjects: geo:population " "^^xsd:integer. geo:name "Milan".... is the same as... geo:population " "^^xsd:integer ; geo:name "Milan". Abbreviating repeated subject/predicate pairs: geo:alternateName geo:alternateName geo:alternateName is the same as... geo:alternateName 43

44 Search Computing RDF in a nutshell Serializing RDF in Turtle – black nodes The following RDF model (see slide 25), including two blank nodes, can be serialized as shown bio:. bio:event [ a bio:Birth; bio:date " "^^xsd:date ], [ a bio:Mariage; bio:date " "^^xsd:date ]

45 Search Computing RDF in a nutshell XML vs. RDF w.r.t. Evolving Data 1/2 V1 has several features V1.1 adds two features V2 changes the root tag 45 XMLRDF XMLRDF XMLRDF What effect on existing client software? –Regenerate stubs? –Recompile? What effect on existing client software? :# Just few more triples :-) Just few more triples :-)

46 Search Computing RDF in a nutshell XML vs. RDF w.r.t. Evolving Data 2/2 Flexibility is important Products are always changing (competitive environment) People are always adding more features Graceful evolution is important Relational data is remarkably flexible XML syntax is important Lots of application, which use XML, are already available Lots of tools for XML are already available Trees alows for simple parsing without loading the entire model (i.e., XML parsing using SAX) 46

47 Search Computing RDF in a nutshell RDF Resources RDF at the W3C - primer and specifications Semantic Web tools - community maintained list; includes triple store, programming environments, tool sets, and more 302 Semantic Web Videos and Podcasts - includes a section specifically on RDF videos semantic-web-videos-and-podcasts/http://www.semanticfocus.com/blog/entry/title/302- semantic-web-videos-and-podcasts/ 47

48 Search Computing Query: SPARQL 48

49 Search Computing SPARQL in a nutshell What is SPARQL? SPARQL is the query language of the Semantic Web stays for SPARQL Protocol and RDF Query Language A Query Language...: Find names and websites of contributors to PlanetRDF: PREFIX dbpedia: PREFIX dbp-ont: PREFIX dbp-prop: PREFIX geo-ont: PlanetRDF SELECT * WHERE { ?Company dbp-prop:location ?Place ; dbp-ont:industry dbpedia:Mass_media. ?Place geo-ont:parentFeature dbpedia:Italy. }... and a Protocol. ttp%3A%2F%2Fdbpedia.org%2Fresource%2F%3E%0D%0APREFIX+dbp -ont%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology … ttp%3A%2F%2Fdbpedia.org%2Fresource%2F%3E%0D%0APREFIX+dbp -ont%3A+%3Chttp%3A%2F%2Fdbpedia.org%2Fontology … 49

50 Search Computing SPARQL in a nutshell Why SPARQL? SPARQL let us Pull values from structured and semi-structured data represented in RDF Explore RDF data by querying unknown relationships Perform complex joins of disparate RDF repositories in a single query Transform RDF data from one vocabulary to another Develop higher-level cross-platform application 50

51 Search Computing SPARQL in a nutshell Anatomy of a SPARQL query Source: Pérez, Arenas and Gutierrez, Chapter 1: On the Semantics of SPARQL, Semantic Web Information Management: A Model Based Perspective, Springer 2010

52 Search Computing SPARQL in a nutshell Anatomy of a SPARQL SELECT query 52

53 Search Computing SPARQL in a nutshell The Modigliani Test 53

54 Search Computing SPARQL in a nutshell SPARQL, FactForge and the Modigliani Test 1/2 SELECT DISTINCT ?painting_l ?owner_l ?city_fb_con ?city_db_loc ?city_db_cit WHERE { ?p fb:visual_art.artwork.artist dbpedia:Amedeo_Modigliani ; fb:visual_art.artwork.owners [ fb:visual_art.artwork_owner_relationship.owner ?ow ] ; ff:preferredLabel ?painting_l. ?ow ff:preferredLabel ?owner_l. OPTIONAL { ?ow fb:location.location.containedby [ rdf:type umbel-sc:City ; ff:preferredLabel ?city_fb_con ] }. OPTIONAL { ?ow dbp-prop:location ?loc. ?loc rdf:type umbel-sc:City ; ff:preferredLabel ?city_db_loc } OPTIONAL { ?ow dbp-ont:city [ ff:preferredLabel ?city_db_cit ] } FILTER ( bound(?city_fb_con) || bound(?city_db_loc) || bound(?city_db_cit) ) } 54

55 Search Computing SPARQL in a nutshell SPARQL, FactForge and the Modigliani Test 2/2 55

56 Search Computing SPARQL in a nutshell SPARQL Resources SPARQL Frequently Asked Questions SPARQL implementations - community maintained list of open-source and commercial SPARQL engines Public SPARQL endpoints - community maintained list SPARQL extensions - collection of SPARQL extensions implemented in various SPARQL engines

57 Search Computing Ontology: RDF-S and OWL 57

58 Search Computing RDF-S/OWL in a nutshell Ontology definition Philosophy (400BC): Systematic explanation of Existence Neches (91): Ontology defines basic terms and relations comprising the vocabulary of a topic area as well as the rules for combining terms and relations to define extensions to the vocabulary Gruber (93): Explicit specification of a conceptualization Borst (97): Formal specification of a shared conceptualization Studer(98) Formal, explicit specification of a shared conceptualization 58

59 Search Computing RDF-S/OWL in a nutshell What does it mean? 59 Formal, explicit specification of a shared conceptualization Machine readable Several people agrees that such conceptual model is adequate to describe such aspects of the reality A conceptual model of some aspects of the reality It makes domain assumption explicit

60 Search Computing RDF-S/OWL in a nutshell Did I Get it? 60

61 Search Computing What is an Ontology? A model of (some aspect of) the world Introduces vocabulary relevant to domain –e.g., anatomy Specifies meaning (semantics) of terms –Heart is a muscular organ that is part of the circulatory system Formalised using suitable logic – x.[ Heart(x) MuscolarOrgan(x) y.[isPartOf(x,y ) CirculatorySystem(y)]] Shared among multiple people organizations

62 Search Computing RDF-S/OWL in a nutshell How much explicit shall the specification be? 62 A little semantics, goes a long way [James Hendler, 2001] A little semantics, goes a long way [James Hendler, 2001]

63 Search Computing RDF-S/OWL in a nutshell A simple ontology 63 Artist Piece Painter Paint paints Sculptor Sculpt sculpts creates

64 Search Computing RDF-S/OWL in a nutshell Specifying classes, sub-classes and instances Creating a class RDFS: Artist rdf:type rdfs:Class. FOL: x Artist(x) Creating a subclass RDFS: Painter rdfs:subClassOf Artist. RDFS: Sculptor rdfs:subClassOf Artist. FOL: x [Painter(x) Sculptor(x) Artist(x)] Creating an instance RDFS: Rodin rdf:type Sculptor. FOL: Sculptor(Rodin) 64 Artist Painter Sculptor Rodin

65 Search Computing Creating a property RDFS: creates rdf:type rdf:Property. FOL: x y Creates(x,y) Using a property RDFS: Rodin creates TheKiss. FOL: Creates(Rodin, TheKiss) Creating subproperties RDFS: paints rdfs:subPropertyOf creates. FOL: x y [Paints(x,y) Creates(x,y)] RDFS: sculpts rdfs:subPropertyOf creates. FOL: x y [Sculpts(x,y) Creates(x,y)] RDF-S/OWL in a nutshell Specifying properties and sub-properties creates paints

66 Search Computing RDF-S/OWL in a nutshell Specifying domain/range constrains Checking which classes and properties can be use together RDFS: creates rdfs:domain Artist. creates rdfs:range Piece. paints rdfs:domain Painter. paints rdfs:range Paint. sculpts rdfs:domain Sculptor. sculpts rdfs:range Sculpt. FOL: x y [Creates(x,y) Artist(x) Piece(y)] x y [Paints(x,y) Painter(x) Paint(y)] x y [Sculpts(x,y) Sculptor(x) Sculpt(y)] 66

67 Search Computing RDF-S/OWL in a nutshell The ontology we specified 67 Artist Piece Painter Paint paints Sculptor Sculpt sculpts creates

68 Search Computing RDF-S/OWL in a nutshell RDF semantics (a part of it) if then x rdfs:subClassOf y. a rdf:type y. a rdf:type x. x rdfs:subClassOf y. x rdfs:subClassOf z. y rdfs:subClassOf z. x a y. x b y. a rdfs:subPropertyOf b. a rdfs:subPropertyOf b. a rdfs:subPropertyOf c. b rdfs:subPropertyOf c. x a y. x rdf:type z. a rdfs:domain z. x a u. u rdf:type z. a rdfs:range z. 68 Read out more in RDF Semantics

69 Search Computing RDF-S/OWL in a nutshell RDF semantics at work Shared the ex:. ex:Sculptor rdfs:subClassOf ex:Artist. ex:Painter rdfs:subClassOf ex:Artist. ex:Sculpt rdfs:subClassOf ex:Piece. ex:Painting rdfs:subClassOf ex:Piece. ex:creates rdfs:domain ex:Artist. ex:creates rdfs:range ex:Piece. ex:sculpts rdfs:subPropertyOf ex:creates. ex:sculpts rdfs:domain ex:Sculptor. ex:sculpts rdfs:range ex:Sculpt.... when transmitting the following triple … ex:Rodin ex:sculpts ex:TheKiss. 69

70 Search Computing RDF-S/OWL in a nutshell Without Inference A recipient, that only understands XML syntax receiving can answer the following queries What does Rodin sculpt? Who does sculpt TheKiss? Try out your self at but it cannot answer Who is Rodin? What is TheKiss? Is there any Sculptor/Scupts? Is there any Artist/Piece? 70

71 Search Computing RDF-S/OWL in a nutshell Knowing the ontology and RDF semantics … A recipient, that knows the ontology and understands RDF semantics Receiving Rodin sculpts TheKiss. 71 Artist Piece Painter Paint paints Sculptor Sculpt sculpts creates Rodin TheKiss

72 Search Computing RDF-S/OWL in a nutshell … a reasoner can answer 1/2 the previous queries What does Rodin sculpt? PREFIX rdfs: PREFIX ex: SELECT ?x WHERE { ex:Rodin ex:sculpts ?x } ?x = ex:TheKiss Who does sculpt TheKiss? WHERE { ex:Rodin ex:sculpts ?x } ?x = ex:Rodin and it can also answer Who is Rodin? WHERE { ex:Rodin a ?x } ?x = ex:Artist, ex:Sculptor, rdfs:Resource What is TheKiss? WHERE { ex:TheKiss a ?x } ?x = ex:Sclupt, ex:Piece, rdfs:Resource 72

73 Search Computing RDF-S/OWL in a nutshell … a reasoner can answer 2/2 Is there any Sculptor? WHERE { ?x a ex:Sculptor} ?x = ex:Rodin Is the any Artist? WHERE { ?x a ex:Artist } ?x = ex:Rodin Is there any Sculpt? WHERE { ?x a ex:Sculpt } ?x = ex:TheKiss Is there any Piece? WHERE { ?x a ex:Piece } ?x = ex:TheKiss Is there any Paint? WHERE { ?x a ex:Paint } 0 results Is there any Painter? WHERE { ?x a ex:Painter } 0 results 73

74 Search Computing RDF-S/OWL in a nutshell Reasoning and Query Answering SPARQL alone cannot answer queries that require reasoning but a reasoner can be exposed as a SPARQL service. Or a query can be rewritten in order to incorporate the ontology 74 data SPARQL service Reasoner data SPARQL service Inferred data ontology data SPARQL service ontology Rewritten query Reasoner

75 Search Computing RDF-S/OWL in a nutshell More expressive power 1/3 RDFS is a light ontological language that allows for defining simple vocabularies. One may want also express Cardinality constrains (max, min, exactly) for properties usage –Es. a Polygon has 3 or more edges – x [Polygon(x) 3y Edge(y) Forms(y,x) ] Property types –transitive -e.g. hasAncestor is a transitive property: if A hasAncestor B and B hasAncestor C, then A hasAncestor C. - x y z [HasAncestor(x,y) HasAncestor(y,z) HasAncestor(x,z) ] –inverse -e.g. sclupts has isSculptedBy as inverse property: if A sclupts B then B isSculptedBy A - x y [Sculpts(x,y) IsSculptedBy(y,x) ] 75

76 Search Computing RDF-S/OWL in a nutshell More expressive power 2/3 –simmetric -e.g. isCloseTo is a simmetric property: if A isCloseTo B then B isCloseTo A - x y [IsCloseTo(x,y) IsCloseTo(y,x) ] Restrictions of usage for a specific property –All values of property must be of a certain kind -e.g. a D.O.C. Wine can be only produced by a Certified Wienery - x y [DOCWine(x) Produces(x,y) CertifiedWienery(y)] –Some values of property must be of a certain kind -e.g. a Famous Painter must have painted some Famous Painting - x [FamousPainter(x) y FamousPaint(y) IsPaintedBy(y,x)] A class is defined combining other classes (union, intersection, negation,...) –A white wine is a Wine and its color is white – x [Wine(x) White(x)] 76

77 Search Computing RDF-S/OWL in a nutshell More expressive power 3/3 Two instances refers to the same real object –The Boss and Bruce Springsteen are two names for the same person – TheBoss = BruceSpringsteen Two classes refers to the same set –Painters in english and Pittori in italian – x [Painter(x) Pittore(x)] Two properties refers to the same binary relationship –Paints in english and Dipinge in italian – x y [Paints(x,y) Dipinge(x,y)] 77

78 Search Computing RDF-S/OWL in a nutshell Expressivity vs. Tractability The more an ontological language is expressive the less is tractable the Web Ontology Language (OWL) comes with several profiles that offers different trade-offs between expressivity and tractability. 78

79 Search Computing RDF-S/OWL in a nutshell OWL profiles OWL 1 defines only one fragment (OWL Lite) And it isnt very tractable! OWL 2 defines several different fragments with Useful computational properties –E.g., reasoning complexity in range LOGSPACE to PTIME Useful implementation possibilities –E.g., Smaller fragments implementable using RDBs OWL 2 profiles OWL 2 EL, OWL 2 QL, OWL 2 RL 79

80 Search Computing RDF-S/OWL in a nutshell OWL 2 EL Useful for applications employing ontologies that contain very large number of properties and/or classes Captures expressive power used by many large- scaleontologies E.g.; SNOMED CT, NCI thesaurus Features Included: existential restrictions, intersection, subClass,equivalentClass, disjointness, range and domain, object property inclusion possibly involving property chains, and data property inclusion, transitive properties, keys … Missing: include value restrictions, Cardinality restrictions (min, max and exact), disjunction and negation Maximal language for which reasoning (including query answering) known to be worst-case polynomial 80

81 Search Computing RDF-S/OWL in a nutshell OWL 2 QL Useful for applications that use very large volumes of data, and where query answering is the most important task Captures expressive power of simple ontologies like thesauri, classifications, and (most of) expressive power of ER/UML schemas E.g., CIM10, Thesaurus of Nephrology,... Features Included: limited form of existential restrictions, subClass, equivalentClass, disjointness, range & domain, symmetric properties, … Missing: existential quantification to a class, self restriction, nominals, universal quantification to a class, disjunction etc. Can be implemented on top of standard relational DBMS Maximal language for which reasoning (including query answering) is known to be worst case logspace (same as DB) 81

82 Search Computing RDF-S/OWL in a nutshell OWL 2 RL Useful for applications that require scalable reasoning without sacrifying too much expressive power, and where query answering is the most important task Support most OWL features but with restrictions placed on the syntax of OWL 2 standard semantics only apply when they are used in a restricted way Can be implemented on top of rule extended DBMS E.g., Oracles OWL Prime implemented using forward chaining rules in Oracle 11g Related to DLP and pD* Allows for scalable (polynomial) reasoning using rule-based technologies 82

83 Search Computing RDF-S/OWL in a nutshell RDF-S/OWL Resources OWL Frequently Asked Questions RDF-S/OWL implementations - community maintained list of open-source and commercial SPARQL engines d07454b4f0d51f5e9d878822d911d0bfea9dcdfdhttp://esw.w3.org/topic/SemanticWebTools#head- d07454b4f0d51f5e9d878822d911d0bfea9dcdfd RDF-S Specification OWL Working Group Wiki

84 Search Computing Conclusions 84

85 Search Computing Credits Introduction and RDF slides are partially based onFundamentals of the Semantic Web by David Booth OWL 2 slides are partially based on OWL 2 Update by Christine Golbreich 10_F2F?action=AttachFile&do=get&target=HCLSF2F200 8-OWL2-CG.pdf 10_F2F?action=AttachFile&do=get&target=HCLSF2F200 8-OWL2-CG.pdf Scalable Ontology-Based Information Systems by Ian Horrocks presented at EDBT/ICDT 2010 Joint Conference, Lausanne, Switzerland, March 26th, 2010.http://www.comlab.ox.ac.uk/people/ian.horrocks/ Seminars/download/EDBT-2010.pdfhttp://www.comlab.ox.ac.uk/people/ian.horrocks/ Seminars/download/EDBT-2010.pdf 85

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