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Talk at the Nancy Unviersity to students of the Master SCA. (Unlike the title, most of the slides are in english).
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Exploiter le Web Sémantique, le comprendre et y contribuer(dans cet ordre)
Mathieu d’Aquin KMi, The Open University – [email protected]
Le reste des diapos sont principalement en anglais…
The other slides are mostly in English
Outline of the talk
Exploiter le Web Sémantique, le comprendre et y contribuer
1.2.
3. 4.
Outline of the talk
Exploiter le Web Sémantique, le comprendre et y contribuer
?
The Semantic Web (in theory)
A large scale, heterogenous collection of formal, machine processable, ontology-based statements (semantic metadata) about web resources and other entities in the world, expressed in a standard syntax
<rdf:RDF> <owl:Ontology rdf:about=""> <owl:imports rdf:resource="http://usefulinc.com/ns/doap#"/> </owl:Ontology> <j.1:Organization rdf:ID="KMi"> <rdfs:comment rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >The Knoledge Media Institute of the Open University, Milton Keynes UK</rdfs:comment> </j.1:Organization> <j.1:Document rdf:ID="KMiWebSite"> … <rdf:RDF>
<channel rdf:about=“http://watson.kmi.open.ac.uk/blog”><title>Elementaries - The Watson Blog</title><link>http://watson.kmi.open.ac.uk:8080/blog/</link><description>"Oh dear! Where the Semantic Web is going to go now?" -- imaginary user 23</description><language>en</language><copyright>Watson team</copyright><lastBuildDate>Thu, 01 Mar 2007 13:49:52 GMT</lastBuildDate><generator>Pebble (http://pebble.sourceforge.net)</generator><docs>http://backend.userland.com/rss</docs>…
Metadata
UoD
<rdf:RDF><channel rdf:about=“http://watson.kmi.open.ac.uk/blog”><title>Elementaries - The Watson Blog</title><link>http://watson.kmi.open.ac.uk:8080/blog/</link><description>"Oh dear! Where the Semantic Web is going to go now?" -- imaginary user 23</description><language>en</language><copyright>Watson team</copyright><lastBuildDate>Thu, 01 Mar 2007 13:49:52 GMT</lastBuildDate><generator>Pebble (http://pebble.sourceforge.net)</generator><docs>http://backend.userland.com/rss</docs>…
<rdf:RDF> <foaf:Image rdf:about='http://static.flickr.com/132/400582453_e1e1f8602c.jpg'> <dc:title>Zen wisteria</dc:title> <dc:description></dc:description> <foaf:page rdf:resource='http://www.flickr.com/photos/xcv/400582453/'/> <foaf:topic rdf:resource='http://www.flickr.com/photos/tags/vittelgarden/'/> <foaf:topic rdf:resource='http://www.flickr.com/photos/tags/wisteria/'/> <dc:creator> <foaf:Person><foaf:name>Mathieu d'Aquin</foaf:name> …
<rdf:RDF> <owl:Ontology rdf:about=""> <owl:imports rdf:resource="http://usefulinc.com/ns/doap#"/> </owl:Ontology> <j.1:Organization rdf:ID="KMi"> <rdfs:comment rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >The Knoledge Media Institute of the Open University, Milton Keynes UK</rdfs:comment> </j.1:Organization> <j.1:Document rdf:ID="KMiWebSite"> …
FOAF
DCRSS TAP
WORDNET
NCI Galen
Music
…… …
…
…
<rdf:RDF> <foaf:Image rdf:about='http://static.flickr.com/132/400582453_e1e1f8602c.jpg'> <dc:title>Zen wisteria</dc:title> <dc:description></dc:description> <foaf:page rdf:resource='http://www.flickr.com/photos/xcv/400582453/'/> <foaf:topic rdf:resource='http://www.flickr.com/photos/tags/vittelgarden/'/> <foaf:topic rdf:resource='http://www.flickr.com/photos/tags/wisteria/'/> <dc:creator> <foaf:Person><foaf:name>Mathieu d'Aquin</foaf:name> …
<rdf:RDF> <owl:Ontology rdf:about=""> <owl:imports rdf:resource="http://usefulinc.com/ns/doap#"/> </owl:Ontology> <j.1:Organization rdf:ID="KMi"> <rdfs:comment rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >The Knoledge Media Institute of the Open University, Milton Keynes UK</rdfs:comment> </j.1:Organization> <j.1:Document rdf:ID="KMiWebSite"> …
Ontology alignmentData integration
Data analysisReasoning
Etc.
Smart Application
Therefore…
• Many research and development efforts in – Supporting the design of ontologies (methodologies,
toolkits, editors, etc.)
– Supporting the annotation Web resources (natural language processing, information extraction, etc.)
– Supporting the publication of semantic data and information online (linking open data, `semantification’ of legacy information systems)
– …
• Resulting in an explosion of the amount of machine processable knowledge online.
OK, nice… but what’s the reality?
2007 2008 2009
Slide 9
And for ontologies?
Slide 10
And for ontologies?
So, the Semantic Web in reality?
• Millions of Semantic Web documents (data), containing billions of RDF triples
• Thousands of ontologies online in OWL and RDFs, covering many different domains (will talk about that later)
• But, distributed and heterogeneous in representation, meaning, quality…
• So, what do we really do with it?
Outline of the talk
Exploiter le Web Sémantique, le comprendre et y contribuer
?
Next Generation Semantic Web Applications
NG SW Application Semantic WebSmart Features
Able to exploit the Semantic Web at large – Dynamically retrieving the relevant semantic resources – Combining at run-time heterogeneous and distributed
Ontologies
Next Generation Semantic Web Applications
Dynamically retrieving, exploiting and combining relevant semantic resources from the SW, at large
Need for a Gateway to the Semantic Web
Watson: a Gateway to the Semantic Web
Architecture
Interface
http://watson.kmi.open.ac.uk
But the important part is: the APIs
• Provide Semantic Web application developers with the ability to efficiently:
– Locate (find) Semantic Web documents online using advanced search functions
– Explore the documents, automatically extracted metadata and content
– Query the documents, to exploit online knowledge in an homogeneous way
• In a set of lightweight APIs, and without having to download the data or use any other dedicated infrastructure.
Some Applications We Developed
Ontology Reuse:The Watson Plugin
Question Answering:PowerAqua
Semantic Browsing:PowerMagpie
Semantic Relation Discovery:Scarlet
Folksonomy Enrichment
And also:Word sense disambiguationQuery ExpansionSynonym DiscoveryWeb Service Annotation…
Example: The Watson Plugin
Chose an entity to search
Get entities from online ontologies
Integrate statements Into the edited ontology
Example: Scarlet
ka2.rdf
Researcher AcademicStaff
Sem
anti
c W
eb
Researcher
AcademicStaff
⊆
⊆
ISWC SWRCHam SeaFood
Sem
anti
c W
eb
HamSeaFood
Meat
Meat
SeaFood
Agrovoc NALT
⊆
€
⊥
€
⊥
€
⊥
pizza-to-go
wine.owl
NALT
Example: Scarlethttp://scarlet.open.ac.uk/
Example: Wahoo http://watson.kmi.open.ac.uk/wahoo
Example: PowerAqua
Natural language question
Answers from online semantic data
Example: FLOR
Can the Semantic Web provide the structure needed to improve search and navigation of tagged spaces?
Search in Tag Spaces
5/24 ≈ 21% relevant
Dog Dog
DogDog
Bird
Bird
Bird
Bird
Bird
Bird
Bird
Tiger
Tiger
Tiger
Tiger
CatLandscape
Landscape
Landscape
Let’s find photos of “animals which live in the water”
Query: Animal Water
Bring in the SW…
Dolphin
Seal
Marine Mammal
Mammal
Sea
livesIn
Whale
Body of Water
Ocean
Sea Elephant
FishlivesIn
Animal
FreshwaterFish SaltwaterFish
livesIn
Animal Water
<Animal livesIn Water>
<Dolphin>or<Seal>or<“Sea Elephant”>or<Whale>
Results
dolphin
seal
whale
sea elephant
18/24 ≈ 75% relevant
SWEET: Semantic Annotation of REST services
And so?
• These are only a few of the applications developed in KMi (i.e., us, the people who are doing Watson)
• Many other people are developing such applications (and we don’t necessarily know all of them)
• Many other tools exists that help building applications (triple stores, query engines, other Semantic Web search engines)…
• But what does that tell us about the Semantic Web?
Outline of the talk
Exploiter le Web Sémantique, le comprendre et y contribuer
?
Watson as a Research Platform
• Watson collects a lot of ontologies and Semantic Web documents that are created by different people for different purposes
• In addition to being a gateway for the development of applications exploiting this knowledge, it can be used to better understand how knowledge is published online, how the Semantic Web looks like, and how it evolves
Characterizing and subset of the Watson Collection (2007)
Number of entities
Domain covered
Underlying description logic
Understanding relations between ontologies online
• Ontologies are naturally related with each other, some are equivalent to others, some are versions, some are similar, some are incompatible to each other
• These relations generally stay implicit
Better understanding these relations is useful to support the use of the Semantic Web
DOOR: An Ontology of Ontology Relations
Example Relation: Different Versions
• Ontologies evolve on the Web, there are many different versions of the same ontology are available
• This is rarely made explicit through the appropriate metadata for ontologies (e.g., owl:preVersion)
• But version info is often encoded in the URIs of ontologies, e.g., http://loki.cae.drexel.edu/wbs/ontology/2003/10/iso-metadata http://loki.cae.drexel.edu/wbs/ontology/2004/01/iso-metadata
• Extracting this information can help in studying the evolution of ontologies on the Semantic Web, i.e., the Semantic Web dynamics
Example Relation: Different Versions – Initial Experiment
• We developed simple method based on a few rules recognizing specific patterns in the differences between URIs of ontologies (dates, timestamps, etc.) and ran it on a set of 6,898 ontologies from Watson.
• We found 155,501 (directed) versioning relations between these ontologies, which represent 1,365 evolving ontologies
• A manual evaluation indicates that more than 50% of these are correct
• Next step: improve the method and study evolution patterns on the Semantic Web
Example Relations: Agreement and Disagreement
• Ontologies are knowledge artifacts, they express opinions and beliefs and contradict each others
• Assessing (dis)agreement in ontologies is very useful to understand how to combine knowledge from different sources
• A possible approach would be to check whether inconsistencies and incoherencies appear while combining the ontologies. However we believe that:– There are di erent levels of agreement/disagreement ff
– Covering di erent domains is not agreeing ff
– It is possible to agree and disagree at the same time
• Based on these requirements we define a set of measures for assessing (dis)agreement between statements and ontologies.
Example Relations: Agreement and Disagreement - Measures
• Agreement(st, O) [0..1] and Disagreement(st, O) [0..1] where st is a statement <subject, predicate, object> and O is an ontology
• Based on extracting the part of the ontology that express a relation between subject and object
• (Dis)agreement between ontologies:
• Global (dis)agreement in a repository
• Consensus:
• Controversy:
Example Relations: Agreement and Disagreement – Application?
• Experiment: assessing statements related to the class Seafood in Watson:
a: global agreement, d: global disagreement, cs: consensus, ct: controversy
• More experiments on the Way!
Using 21 ontologies containing a concept SeaFood
Agreement
Disagreement
Camp 1: seaFood disjointWith MeatCamp 2: SeaFood subClassOf Meat
Outline of the talk
Exploiter le Web Sémantique, le comprendre et y contribuer
?
Slide 43
From a Semantic Web search engine…
Slide 44
… to Ontology Repositories?
Ontologies
Ontology Metadata
Versions of
Alignments
Comments and
Reviews
Cu
pb
oard
.op
en.a
c.u
k
Sum
mary
Meta
data
Revie
ws
Final message
• I hope I convinced you that
Using the Semantic Web
Understanding the Semantic Web
Contributing to the Semantic Web
• Through Watson, Cupboard and our applications, our aim is to build an open and efficient platform making the Semantic Web a `playground for research and development’
• There is still a lot to do, and everybody is welcome to comment, help, contribute…
Thank You!
Mathieu d’Aquin ([email protected], http://people.kmi.open.ac.uk/mathieu) With contributions from many people in KMi (http://kmi.open.ac.uk) and the NeOn project (http://neon-project.org)
/* I would normally include a bibliography slide at the end, but all the relevant papers can be found on these 3 websites */