by Serge Linckels University of Luxembourg 2017/2018

1. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 1
Semantic Web
Unit 10: Semantic Search
Faculty of Science, Technology and Communication (FSTC)
Bachelor en informatique (professionnel)

2. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 2
10. Semantic search
Semantic Web Roadmap:
Controlled growth bottom
up according to this
architecture.
Architecture was (slightly)
modified in the last years.

3. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 3
10.1. Google
10.2. Insert semantics into HTML
10.3. Multimedia information retrieval
10.4. Towards a semantic search engine
10.5. Visions and outlook
10. Semantic search
10.6. References

4. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 4

5. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 5
FINISHED FILES ARE THE
RESULT OF YEARS OF SCIENTIFIC
STUDY COMBINED WITH THE
EXPERIENCE OF YEARS

6. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 6
10.1. Google
10. Semantic search
Boiling point of Radium (Ra)

7. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 7
10.1. Google
10. Semantic search
Boiling point of Radium (Ra)
1140 °C
google.com
1737 °C
chemicalelements.com
1500 °C
chemicool.com
1536 °C
environmentalchemistry.com
all sites from first page
of Google results

8. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 8
10. Semantic search
10.1. Google
Google keyword trends
porn
semantic web
http://www.google.com/trends

9. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 9
10. Semantic search
10.1. Google
https://www.youtube.com/watch?v=wSF82AwSDiU

10. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 10
10. Semantic search
10.1. Google
http://www.iflscience.com/health-and-medicine/here-are-pornhub-search-habits-british-public

11. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 11
10. Semantic search
10.1. Google
http://www.siegemedia.com/seo/most-popular-keywords

12. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 12
10.1. Google
10. Semantic search
same result with “google works does how”
source: http://www.google.com/trends

13. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 13
10. Semantic search
Page Rank, by Larry Page (1998)
The “Page Rank” of a web page depends on
the number of incoming links
The PR of each web page is initialized equally,
here: 0,25.
10.1. Google

14. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 14
Information retrieval process
10.2. Insert semantics into HTML
10. Semantic search
knowledge representation

15. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 15
10. Semantic search
The answer is
not part of the
query
Linked data
“Best results”
first  ranking
10.1. Google

16. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 16
10. Semantic search
10.2. Insert semantics into HTML

17. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 17
10. Semantic search
10.2. Insert semantics into HTML

18. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 18
10. Semantic search
Microformats
web based approach to
semantic markup
http://microformats.org/
10.2. Insert semantics into HTML

19. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 19
Microdata
Supporting vocabulary that can be used
by applications, i.e., search engines
10. Semantic search
10.2. Insert semantics into HTML

20. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 20
10. Semantic search
10.2. Insert semantics into HTML

21. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 21
<?xml version="1.0"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:myNS="http://www.linckels.lu/myNS/"
xmlns:foaf="http://xmlns.com/foaf/0.1/">
<rdf:Description rdf:about="http://www.linckels.lu/Demo">
<foaf:name>Neil Armstrong</foaf:name>
<myNS:hasWalkedOn rdf:resource="http://en.wikipedia.org/wiki/Moon" />
</rdf:Description>
</rdf:RDF>
10. Semantic search
10.2. Insert semantics into HTML

22. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 22
10. Semantic search
10.2. Insert semantics into HTML

23. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 23
Information retrieval process
10. Semantic search
knowledge mining
10.3. Multimedia information retrieval

24. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 24
10.3. Multimedia information retrieval
10. Semantic search
ambiguity alert

25. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 25
10. Semantic search
ambiguity alert
Human Thing
Cyclist Astronaut
Place Automobile
TOP
Man
Musician
10.3. Multimedia information retrieval

26. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 26
Multimedia content and metadata
10. Semantic search
10.3. Multimedia information retrieval

27. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 27
10. Semantic search
Document (d1)
Aim of information retrieval
Document (d2)
compute similarities: sim(q,d1) vs. sim(q,d2)
Query (q)
Multimedia content and metadata
10.3. Multimedia information retrieval

28. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 28
Boolean model
10. Semantic search
Document (d1) Document (d2)
Query (q)
Index terms: K = {nude, woman, street}
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
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0
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2d
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0
0
1
q

10.3. Multimedia information retrieval

29. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 29
10. Semantic search
Document (d1)
Document (d2)
Query (q)
Index terms: K = {nude, woman, street}
Nude(y)
Woman (x)
q d2
d1
Vector model
10.3. Multimedia information retrieval
45°
90°

30. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 30
Other models
10. Semantic search
Probabilistic model
Latent semantic indexing model
Fuzzy set model
Neural network model
Baysian network
...
Classical MIR vs. semantic search
Classical approaches fail when it comes to more complex queries
Need of better human-machine interfaces, e.g., natural language input
Semantic search is not based on keyword / index term checking, but on the reasoning over
the sense of the metadata
10.3. Multimedia information retrieval

31. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 31
Semantic interpretation
10.4. Towards a semantic search engine
10. Semantic search
Translation from an informal language into a formal language
D1  Picture hasColor.BW isArtistic
pictureOf.(Woman isNaked)
isLocated.(Street isNarrow)
D2  Frame isNarrow
hasContent.(Woman isColor.Black)
hasContent.(Man isColor.Black)

32. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 32
10. Semantic search
Translation from an informal language into a formal language
D1  Picture hasColor.BW isArtistic
pictureOf.(Woman isNaked)
isLocated.(Street isNarrow)
D2  Frame isNarrow
hasContent.(Woman isColor.Black)
hasContent.(Man isColor.Black)
Q  Photo hasColor.BW
photoOf.(Woman isNude)
isOutdoors.Daylight
Ontology inside
image
picture
photo movie
Photo Picture
pictureOf  photoOf
isNude  isNaked
equivalences
Q  Photo Picture hasColor.BW
pictureOf.(Woman isNaked)
isOutdoors.Daylight
similarity
similarity
Semantic interpretation
10.4. Towards a semantic search engine

33. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 33
Semantic distance
10. Semantic search
D1  Picture hasColor.BW isArtistic
pictureOf.(Woman isNaked)
isLocated.(Street isNarrow)
Q  Photo Picture hasColor.BW
pictureOf.(Woman isNaked)
isOutdoors.Daylight
similarity
Miss(Q,D1) = Q − lcs(Q,D1)
Miss(Q,D1) = Photo
isOutdoors.Daylight
Rest(Q,D1) = C − lcs(Q,D1)
Rest(Q,D1) = isLocated.(Street
isNarrow) isArtistic
cover(Q,D1) = Picture hasColor.BW
pictureOf.(Woman
isNaked)
|Miss(Q,D1)| = 1 + (2 + 1) = 4
|Rest(Q,D1)| = 2 + (1 + 2) + 2 = 7
Miss(Q,D): requested in Q but not
delivered in D
Rest(Q,D): delivered in D but not
requested in Q
cover(Q,D): requested in Q and
delivered in D
10.4. Towards a semantic search engine

34. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 34
Semantic distance
10. Semantic search
Q  Photo Picture hasColor.BW
pictureOf.(Woman isNaked)
isOutdoors.Daylight
Miss(Q,D2) = Q − lcs(Q,D2)
Miss(Q,D2) = Q
Rest(Q,D2) = C − lcs(Q,D2)
Rest(Q,D2) = D2
cover(Q,D2) =Т
|Miss(Q,D2)| = 13
|Rest(Q,D1)| = 13
Miss(Q,D): requested in Q but not
delivered in D
Rest(Q,D): delivered in D but not
requested in Q
cover(Q,D): requested in Q and
delivered in D
D2  Frame isNarrow
hasContent.(Woman isColor.Black)
hasContent.(Man isColor.Black)
similarity
10.4. Towards a semantic search engine

35. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 35
Semantic distance
10. Semantic search
Document (d1) Document (d2)
Query (q)
|Miss(Q,D1)| = 4
|Rest(Q,D1)| = 7
|Miss(Q,D2)| = 13
|Rest(Q,D2)| = 13
Best cover
object with smallest rest and miss
preference is given to smallest miss
10.4. Towards a semantic search engine

36. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 36
Examples of semantic search engines
10. Semantic search
E-Librarian Service – www.linckels.lu/research/elibrarian
Ask.com – www.ask.com
Hakia – www.hakia.com
WolframAlpha – www.wolframalpha.com
10.4. Towards a semantic search engine

37. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 37
A vision on the evolution…
10.5. Visions and outlook
10. Semantic search

38. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 38
Semantic Web tools
Categories:
– Triple Stores
– Inference engines
– Converters
– Search engines
– Middleware
– CMS
– Semantic Web browsers
– Development environments
– Semantic Wikis
– …
Some names:
– Jena, AllegroGraph, Mulgara, Sesame,
flickurl, …
– TopBraid Suite, Virtuoso, Falcon,
Drupal 7, Redland, Pellet, …
– Disco, Oracle 11g, RacerPro, IODT,
Ontobroker, OWLIM, Talis Platform, …
– RDF Gateway, RDFLib, Open Anzo,
Zitgist, Protégé, …
– Thetus publisher, SemanticWorks, SWI-
Prolog, RDFStore…
10.5. Visions and outlook
10. Semantic search
Deployment communities
Major communities pick the technology up: digital libraries, defense, eGovernment, energy
sector, financial services, health care, oil and gas industry, life sciences …
Semantic Web also appear in the “Web 2.0/Web 3.0” applications exchange of social data,
personal “space” applications, dynamic Web site backends, multimedia asset
management, etc.

39. Semantic Web ::: Serge Linckels ::: http://www.linckels.lu/ ::: serge@linckels.lu 39
Creating the Semantic Web with RDF: Professional Developer's Guide
Johan Hjelm
Foundations of Semantic Web Technologies
Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph
E-Librarian Service
User-Friendly Semantic Search in Digital Libraries
Serge Linckels, Christoph Meinel
10.6. References
10. Semantic search