1. The Semantic Web “Turns Ten”
, Milano
Emanuele Della Valle

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3. Agenda Introduction and Motivation Data Interchange on the Web: RDF3
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. Introduction The Web Today
Large number of integrations
- ad hoc - pair-wise
Millions of Applications
Too much information to browse, need for searching and mashing up automatically
Search & Mash-up Engine
010 1
1101
10100
1 101 ?
Each site is “understandable” for us
Computers don’t “understand” much

5. Introduction The Problem: “Semantic Gap”
No existing streets
Symbolic Description
Missing building
Semantic Gap
Sensor Data

6. Introduction “Understanding” Means Bridging the Gap
Symbolic Description
understanding
Sensor Data

7. Introduction Do We Really Know What “Understanding” Means?
[ source ]

8. Introduction Two ways for computer to “understand”
Smart Machine
Smart Data

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

10. Introduction Smart Machines alone cannot bridge the gap …
Symbolic Description
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?
Semantic Gap
sea
“c”
Natural Language
Processing
Image Processing
Sensor Data
[Source NLP Related Entertainment

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

12. Introduction What a machine “understands” of the Web
What we say to Web agents
" For more information visit <a href=“ my company </a> Web site. . .”
What they “hear”
" blah blah blah blah blah <a href=“ blah blah blah </a> blah blah. . .”
Jet this is enought to train them to achive tasks for us
[ source ]

13. 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.”

14. 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. Introduction The Semantic Web 2/4
“The Semantic Web is not a separate Web, but an extension of the current one […] ”
Web 1.0
The Web Today

16. Introduction The Semantic Web 3/4
“The Semantic Web […] , in which information is given well-defined meaning […]”
Web 1.0
Semantic Web ?
Human understandable but “only” machine-readable
Human and machine “understandable”

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

18. Introduction Linked Data Standards
View the full talk at !
WebMGS 2010,

19. Introduction Linking Open Data Project
Goal: extend the Web with data commons by publishing open data sets using Semantic Web techs
Visit !

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

21. Introduction data.gov and data.gov.uk
[source: J. Hendler WIMS’11 Key Note – ]

22. Introduction http://www.data.gov/opendatasites/
And a growing number of apps is appearing :-)

23. Introduction Industrial Uptake: BBC’s Artist as Linked Data
<?xml version="1.0" encoding="utf-8"?> <rdf:RDF xmlns:rdf = " xmlns:rdfs = " xmlns:owl = " xmlns:dc = " xmlns:foaf = " xmlns:rel = " xmlns:mo = " xmlns:rev = " > <rdf:Description rdf:about="/music/artists/a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432.rdf"> <rdfs:label>Description of the artist U2</rdfs:label> <foaf:primaryTopic rdf:resource="/music/artists/a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432#artist"/> </rdf:Description> <mo:MusicGroup rdf:about="/music/artists/a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432#artist"> <foaf:name>U2</foaf:name> <owl:sameAs rdf:resource=" /> <foaf:page rdf:resource="/music/artists/a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432.html" /> <mo:musicbrainz rdf:resource=" /> <mo:homepage rdf:resource=" /> <mo:fanpage rdf:resource=" /> <mo:wikipedia rdf:resource=" /> <mo:imdb rdf:resource=" /> <mo:myspace rdf:resource=" /> <mo:member rdf:resource="/music/artists/7f eb14-40c3-98e2-17b6e1bfe56c#artist" /> <mo:member rdf:resource="/music/artists/1f52af ac-9a15-e5052bb670c2#artist" />
HTML:
RDF :

24. Introduction Industrial Uptake: Best Buy
Since Fall 2009
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

25. Introduction Industrial Uptake: Best Buy - example
<div rel="v:hasReview">
<span property="v:rating" datatype="xsd:string"> 4.8</span> of <span property="v:best">5</span>
RDFa

26. Introduction Industrial Uptake: Best Buy – SEO on Google
Sponsored Links

27. 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".

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

29. Introduction Semantic Web “layer cake”
Already Possible
Under Investigation
Standardized
[ source ]

30. Data Interchange: RDF

31. RDF in a nutshell Looking for a flexible data model
3/22/2017
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
© Della Valle - CEFRIEL

32. 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

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

34. 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
Legend
resource
literal
Milano
Milan
Mailand

35. RDF in a nutshell Representing data in RDF Q/A 1/4
Which URI should we use?
Popular ones! Data merge will take place automatically! +
20100 =
20100

36. RDF in a nutshell Representing data in RDF Q/A 2/4
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
and in particular the following pages of the Wiki
Data Sets
Semantic Web Search Engines
Common Vocabularies

37. RDF in a nutshell Representing data in RDF Q/A 3/4
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 +
= ?
20100
20100

38. RDF in a nutshell Representing data in RDF Q/A 4/4
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]
Person
Bio Event
Date
Sofia
Birth
Marriage

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

40. RDF in a nutshell Serializing RDF
Three alternatives to write triples, in
RDF/XML: Standard serialization in XML
<Description about=”subject”>
<property>value</property>
</Description>
e.g.,
Check-out the triples using
NTriples: Simple (verbose) reference serialization (for specifications only)‏
< < “value” .
N3 and Turtle: Developer-friendly serializations
:subject :property “value” .

41. RDF in a nutshell Serializing RDF in Turtle - namespaces
URI terms can be abbreviated using namespaces
@prefix geo: < .
@prefix rdf: < 02/22-rdf-syntax-ns#> .
rdf:type geo:P .
< 02/22-rdf-syntax-ns#type> = 'a'
a geo:P .

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

43. 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 .
geo:alternateName .

44. 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 hereafter
@prefix bio: < .
bio:event
[ a bio:Birth; bio:date " "^^xsd:date ] ,
[ a bio:Mariage; bio:date " "^^xsd:date ].

45. 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
XML
RDF
What effect on existing client software?
Regenerate stubs?
Recompile?
XML
RDF
Just few more triples
:-)
Just few more triples
:-)
What effect on existing client software? :#
XML
RDF

46. 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)

47. 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

48. Query: SPARQL

49. SPARQL in a nutshell What is 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: <
SELECT * WHERE { ?Company dbp-prop:location ?Place ; dbp-ont:industry dbpedia:Mass_media ?Place geo-ont:parentFeature dbpedia:Italy . }
... and a Protocol. …

50. 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. 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. SPARQL in a nutshell Anatomy of a SPARQL SELECT query52

53. SPARQL in a nutshell The Modigliani Test

54. 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) ) }

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

56. 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. Ontology: RDF-S and OWL

58. 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

59. RDF-S/OWL in a nutshell What does it mean?
Formal, explicit specification of a shared conceptualization
Machine readable
It makes domain assumption explicit
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

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

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

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

64. 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)
Artist
Painter
Sculptor
Rodin

65. RDF-S/OWL in a nutshell Specifying properties and sub-properties
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)]
creates
paints
- 65 -

66. 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)]

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

68. 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 .
Read out more in RDF Semantics

69. RDF-S/OWL in a nutshell RDF semantics at work
Shared the ontology ...
@prefix rdfs: < .
@prefix 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 .

70. RDF-S/OWL in a nutshell Without Inference
A recipient, that only understands XML syntax
receiving
<RDF>
<Description about="Rodin">
<sculpts resource="TheKiss"/>
</Description>
</RDF>
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?
<RDF>
<Description about="Rodin">
<sculpts resource="TheKiss"/>
</Description>
<Description about="Painting">
<subClassOf resource="Piece"/>
<Description about="sculpts">
<range resource="Sculpt"/>
<domain resource="Sculptor"/>
<subPropertyOf resource="creates"/>
<Description about="Sculpt">
<Description about="creates">
<range resource="Piece"/>
<domain resource="Artist"/>
<Description about="Sculptor">
<subClassOf resource="Artist"/>
<Description about="Painter">
</RDF>

71. 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 .
creates
Artist
Piece
Painter
Paint
paints
Sculptor
Sculpt
sculpts
Rodin
TheKiss

72. 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?
?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

73. 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 }
Is there any Sculpt?
WHERE { ?x a ex:Sculpt }
?x = ex:TheKiss
Is there any Piece?
WHERE { ?x a ex:Piece }
Is there any Paint?
WHERE { ?x a ex:Paint }
0 results
Is there any Painter?
WHERE { ?x a ex:Painter }

74. 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
data
SPARQL service
Reasoner
data
Inferred data
SPARQL service
ontology
Reasoner
data
Rewritten query
SPARQL service
ontology

75. 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) ]

76. 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)]

77. 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)]

78. 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.

79. RDF-S/OWL in a nutshell OWL profiles
OWL 1 defines only one fragment (OWL Lite)
And it isn’t 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

80. 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

81. 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)

82. 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., Oracle’s OWL Prime implemented using forward chaining rules in Oracle 11g
Related to DLP and pD*
Allows for scalable (polynomial) reasoning using rule-based technologies

83. 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
RDF-S Specification
OWL Working Group Wiki

84. Conclusions

85. Credits
Introduction and RDF slides are partially based on “Fundamentals of the Semantic Web” by David Booth
OWL 2 slides are partially based on
OWL 2 Update by Christine Golbreich
“Scalable Ontology-Based Information Systems” by Ian Horrocks presented at EDBT/ICDT 2010 Joint Conference, Lausanne, Switzerland, March 26th,

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