1. Agenda
Semantic Web and
Machine Learning Tutorial • Introduction
• Foundations of the Semantic Web
• Ontology Learning
• Learning Ontology Mapping
• Semantic Annotation
Steffen Staab Andreas Hotho
ISWeb – Information Knowledge and Data Engineering Group • Using Ontologies
Systems and Semantic Web University of Kassel
University of Koblenz Germany • Applications
Germany
2
Syntax is not enough Information Convergence
• Convergence not just in devices, also in “information”
– Your personal information (phone, PDA,…)
Calendar, photo, home page, files…
– Your “professional” life (laptop, desktop, … Grid)
Web site, publications, files, databases, …
– Your “community” contexts (Web)
Hobbies, blogs, fanfic, social networks…
• The Web teaches us that people will work to share
– How do we CREATE, SEARCH, and BROWSE in the non-text
Andreas based parts of our lives?
• Tel
• E-Mail
3 4

2. Meaning of Informationen: XML ≠ Meaning, XML = Structure
(or: what it means to be a computer)
name ναµε
< name >
education < education>
<εδυχατιον>
CV Χς
< CV >
work < work>
<ωορκ>
private < private >
<πριϖατε>
5 6
Source of Problems (One) Layer Model of the Semantic Web
XML is unspecific:
No predetermined vocabulary
No semantics for relationships
& must be specified upfront
Only possible in close cooperations
– Small, reasonably stable group
– Common interests or authorities
Not possible in the Web or on a broad scale in
general !
7 8

3. Some Principal Ideas What is an Ontology?
• URI – uniform resource identifiers Gruber 93:
• XML – common syntax
• Interlinked An Ontology is a
• Layers of semantics – Tim Berners- formal specification ⇒ Executable
from database to Lee, Weaving
of a shared ⇒ Group of persons
the Web
knowledge base to
proofs conceptualization ⇒ About concepts
of a domain of interest ⇒ Between application
and „unique truth“
Design principles of WWW applied to Semantics!!
9 10
Menu Menu
Taxonomy Thesaurus
Object Object
Person Topic Document Person Topic Document
Student Researcher Semantics Student Researcher Semantics
Doctoral Student PhD Student F-Logic Ontology Doktoral Student PhD Student F-Logic Ontology
synonym similar
Taxonomy := Segmentation, classification and ordering of • Terminology for specific domain
elements into a classification system according to their • Graph with primitives, 2 fixed relationships (similar, synonym)
relationships between each other • originate from bibliography
11 12

4. Menu
Topic Map Ontology (in our sense)
Object
is_a
Object
knows described_in
Person Topic Document
knows described_in
Person Topic Document is_a
writes
writes
Student Researcher Semantics F-Logic Ontology
Student Researcher Semantics is_a
subTopicOf similar
Affiliation
Doktoral Student PhD Student
PhD Student
PhD Student F-Logic Ontology Rules
Doktoral Student PhD Student F-Logic Ontology instance_of
T described_inD
similar T is_about D
Tel Affiliation
synonym similar
Tel Affiliation York Sure
P writes D is_about T P knows T
+49 721 608 6592 AIFB
• Topics (nodes), relationships and occurences (to documents)
• ISO-Standard
• Representation Language: Predicate Logic (F-Logic)
• typically for navigation- and visualisation
• Standards: RDF(S); coming up standard: OWL
13 14
The Semantic Web What’s in a link? Formally
cooperatesWith
cooperatesWith
Ontology rdfs:Domain rdfs:Range
Person
Person W3C recommendations
rdfs:subClass
Employee
Employee
• RDF: an edge in a graph
rdfs:subClass
PostDoc
rdfs:subClass
• OWL: consistency (+subsumption+classif. + …)
PostDoc Professor
Professor
rdf:type
rdf:type
<swrc:PostDoc rdf:ID="person_sha">
<swrc:name>Siegfried
<swrc:Professor Currently under discussion
• Rules: a deductive database
Handschuh</swrc:name> rdf:ID="person_sst">
Meta- <swrc:cooperatesWith rdf:resource =
<swrc:name>Steffen Staab
</swrc:name>
data
"http://www.uni-koblenz.de/~staab
#person_sst"/>
...
</swrc:Professor>
swrc:cooperatesWith
...
Currently under intense research
</swrc:PostDoc>
Web
• Proof: worked-out proofs
page • Trust: signature & everything working together
15 16
URL http://www.aifb.uni-karlsruhe.de/WBS/sha http://www.aifb.uni-karlsruhe.de/WBS/sst

5. What’s in a link? Informally Ontologies and their Relatives (I)
• There are many relatives around:
• RDF: pointing to shared data
• OWL: shared terminology – Controlled vocabularies, thesauri and classification systems available
in the WWW, see http://www.lub.lu.se/metadata/subject-help.html
• Classification Systems (e.g. UNSPSC, Library Science, etc.)
• Thesauri (e.g. Art & Architecture, Agrovoc, etc.)
• Rules: if-then-else conditions • DMOZ Open Directory http://www.dmoz.org
– Lexical Semantic Nets
• WordNet, see http://www.cogsci.princeton.edu/~wn/
• Proof: proof already shown • EuroWordNet, see http://www.hum.uva.nl/~ewn/
– Topic Maps, http://www.topicmaps.org (e.g. used within knowledge
• Trust: reliability management applications)
• In general it is difficult to find the border line!
17 18
Ontologies and their Relatives (II) Ontologies - Some Examples
• General purpose ontologies:
– WordNet / EuroWordNet, http://www.cogsci.princeton.edu/~wn
– The Upper Cyc Ontology, http://www.cyc.com/cyc-2-1/index.html
General – IEEE Standard Upper Ontology, http://suo.ieee.org/
Formal logical • Domain and application-specific ontologies:
Thesauri Is-a Frames constraints – RDF Site Summary RSS, http://groups.yahoo.com/group/rss-dev/files/schema.rdf
Catalog / ID – UMLS, http://www.nlm.nih.gov/research/umls/
– GALEN
– SWRC – Semantic Web Research Community: http://ontoware.org/projects/swrc/
– RETSINA Calendering Agent, http://ilrt.org/discovery/2001/06/schemas/ical-full/hybrid.rdf
– Dublin Core, http://dublincore.org/
Terms/ Informal Formal Value • Web Services Ontologies
Is-a Axioms – Core ontology of services http://cos.ontoware.org
Glossary Instance Restric- Disjoint – Web Service Modeling ontology http://www.wsmo.org
tions Inverse
– DAML-S
• Meta-Ontologies
Relations, – Semantic Translation, http://www.ecimf.org/contrib/onto/ST/index.html
... – RDFT, http://www.cs.vu.nl/~borys/RDFT/0.27/RDFT.rdfs
– Evolution Ontology, http://kaon.semanticweb.org/examples/Evolution.rdfs
• Ontologies in a wider sense
– Agrovoc, http://www.fao.org/agrovoc/
– Art and Architecture, http://www.getty.edu/research/tools/vocabulary/aat/
– UNSPSC, http://eccma.org/unspsc/
– DTD standardizations, e.g. HR-XML, http://www.hr-xml.org/
19 20

6. Tools for markup... Not tied to specific domains
PhotoStuff Demo
21 22
Not tied to specific domains Shared Workspace (Xarop + Screenshot)
Shape Visual
VDE plug-in Shape
erasure Descriptor
launch selection
selection
Save Shape Color
selection Descriptor
Prototype
extraction
Instances
Domain
Ontology
Browser Selected
region
Draw panel
M-OntoMat is publicly available
http://acemedia.org/aceMedia/results/software/m-ontomat-annotizer.html
23 24

7. Social networks:
Coming sooner than you may think… e.g. Friend of a Friend (FOAF)
• Say stuff about yourself (or others) in OWL files,
link to who you “know”
25 Estimates of the number of Foaf users range from 2M-5M 26
Using FOAF in other contexts Get a B&N price (In Euros)
Jennifer Golbeck
27 28
http://trust.mindswap.org

8. Of a particular book In its German edition?
29 30
The Semantic Wave
YOU
ARE
HERE
2005
YOU
ARE
HERE
2003
(Berners-Lee, 03)
31 32

9. Now. The semantic web and machine learning
• RDF, RDFS and OWL are ready for prime time What can machine learning do for What can the Semantic Web do
the Semantic Web? for Machine Learning?
– Designs are stable, implementations maturing
• Major Research investment translating into application 1. Learning Ontologies 1. Lots and lots of tools to
development and commercial spinoffs (even if not fully automatic) describe and exchange data
2. Learning to map between for later use by machine
– Adobe 6.0 embraces RDF learning methods in a
ontologies
– IBM releases tools, data and partnering 3. Deep Annotation: Reconciling canonical way!
– HP extending Jena to OWL databases and ontologies 2. Using ontological structures
– OWL Engines by Ontoprise GmbH, Network Inference, Racer GmbH 4. Annotation by Information to improve the machine
Extraction learning task
– Proprietary OWL ontologies for vertical markets
5. Duplicate recognition 3. Provide background
• c.f. pharmacology, HMO/health care, ... Soft drinks
knowledge to guide machine
– Several new starts in SW space learning
33 34
Foundations of the Semantic Web: References Agenda
• Semantic Web Activity at W3C http://www.w3.org/2001/sw/
• www.semanticweb.org (currently relaunched) • Introduction
• Journal of Web Semantics
• D. Fensel et al.: Spinning the Semantic Web: Bringing the World Wide Web to Its Full • Foundations of the Semantic Web
Potential, MIT Press 2003
• G. Antoniou, F. van Harmelen. A Semantic Web Primer, MIT Press 2004. • Ontology Learning
• S. Staab, R. Studer (eds.). Handbook on Ontologies. Springer Verlag, 2004.
•
•
S. Handschuh, S. Staab (eds.). Annotation for the Semantic Web. IOS Press, 2003.
International Semantic Web Conference series, yearly since 2002, LNCS
• Learning Ontology Mapping
• World Wide Web Conference series, ACM Press, first Semantic Web papers since
1999
• Semantic Annotation
• York Sure, Pascal Hitzler, Andreas Eberhart, Rudi Studer, The Semantic Web in One
Day, IEEE Intelligent Systems, • Using Ontologies
http://www.aifb.uni-karlsruhe.de/WBS/phi/pub/sw_inoneday.pdf
• Applications
• Some slides have been stolen from various places, from Jim Hendler and Frank van
Harmelen, in particular.
35 36

10. The OL Layer Cake How do people acquire taxonomic knowledge?
• I have no idea!
∀x, y (married ( x, y ) → love( x, y )) Rules
• But people apply taxonomic reasoning!
Relations – „Never do harm to any animal!“
cure(dom:DOCTOR,range:DISEASE)
=> „Don‘t do harm to the cat!“
is_a(DOCTOR,PERSON) Concept Hierarchies
• More difficult questions:
DISEASE:=<I,E,L> Concepts – representation
– reasoning patterns
{disease,illness} Synonyms
• But let‘s speculate a bit! ;-)
disease, illness, hospital Terms
37 38
How do people acquire taxonomic knowledge? How do people acquire taxonomic knowledge?
What is liver cirrhosis? What is liver cirrhosis?
Diseases such as liver cirrhosis are
Mr. Smith died from liver cirrhosis. difficult to cure. (New York Times)
Mr. Jagger suffers from liver cirrhosis.
Alcohol abuse can lead to liver cirrhosis.
=>prob(isa(liver cirrhosis,disease))
39 40

11. How do people acquire taxonomic knowledge? Evaluation of Ontology Learning
The apriori approach is based on a gold standard ontology:
– Given an ontology modeled by an expert
-> The so called gold standard
What is liver cirrhosis? – Compare the learned ontology with the gold standard
Cirrhosis: noun[uncountable] • Which methods exists:
serious disease of the liver, – learning accuracy/precision/recall/f-measure
often caused by drinking too – Count edges in the “ontology graph”
• Counting of direct relation only (Reinberger et.al. 2005)
much alcohol • Least common superconcept
• Semantic cotopy
• …
liver cirrhosis ≈ cirrhosis ∧ isa(cirrhosis, disease) – Evaluation via application (cf. section using ontologies)
→ prob(isa(liver cirrhosis, disease)) 41 42
The Semantic Cotopy Example for SC
bookable root
SC (c, O) = {c' | c' ≤ O c ∨ c ≤ O c'} rentable joinable thing activity
driveable appartment excursion trip vehicle appartment excursion trip
rideable car TWV car
bike bike
[Maedche & Staab 02] SC(bike)={bike,rideable,driveable.rentable,bookable} SC(bike)={bike,TWV,vehicle,thing,root}
43 => TO(bike,O1,O2)=1/9!!! 44

12. Common Semantic Cotopy Example for SC‘
bookable root
SC ' (c, O1 , O2 ) = {c' | c'∈ C1 ∩ C2 ∧ (c' ≤ O1 c ∨ c ≤ O1 c' )} rentable joinable thing activity
driveable appartment excursion trip vehicle appartment excursion trip
rideable car TWV car
bike bike
SC‘(driveable)={bike,car} SC‘(vehicle)={bike,car}
45
=> TO(driveable,O1,O2)=1 46
One more Example Semantic Cotopy Revisited (Once More)
root
SC ' ' (c, O1 , O2 ) = {c' | c'∈ C1 ∩ C2 ∧ (c' > O1 c ∨ c < O1 c' )}
thing activity
car bike apartment excursion trip
vehicle appartment excursion trip
1
TWV car TO (O1 , O2 ) = ∑ TO(c, O1 , O2 )
| C1 | c∈C1 ,∉C2
bike
SC‘(car)={car} SC‘(vehicle)={bike,car}
=> TO(driveable,O1,O2)=1/2
47 48

13. Example for Precision/Recall Example for Precision/Recall
P=100% P=100%
bookable root
bookable root
rentable joinable thing activity
rentable joinable thing activity
driveable appartment excursion trip vehicle appartment excursion trip
driveable appartment excursion trip vehicle appartment excursion trip
rideable car TWV car
bike car TWV car
bike bike
F=100% R=87,5% bike
F=93.33%
R=100%
49 50
Example for Precision/Recall Another Example
P=90% P=100%
bookable root
root
rentable joinable thing activity
thing activity
car bike apartment excursion trip
driveable appartment planable trip vehicle appartment excursion trip
vehicle appartment excursion trip
rideable car excursion TWV car
TWV car
bike bike
F=94.74% bike F=57.14%
R=40%
R=100% 51 52

14. Evaluation Methodology Lexical Recall and F‘
1
TO (O1 , O2 ) = ∑ TO(c, O1, O2 )
| C1 | c∈C1
| CO1 ∩ CO2 |
⎧ TO ' (c, O1 , O2 ) if c ∈ C2 LR (O1 , O2 ) =
TO (c, O1 , O2 ) = ⎨ | CO2 |
⎩TO ' ' (c, O1 , O2 ) if c ∉ C2
| SC (c, O1 , O2 ) ∩ SC (c, O2 , O1 ) | 2 * F (O1 , O2 ) * LR (O1 , O2 )
TO ' (c, O1 , O2 ) := F ' (O1 , O2 ) =
| SC (c, O1 , O2 ) ∪ SC (c, O2 , O1 ) | ( F (O1 , O2 ) + LR (O1 , O2 ))
| SC (c, O1 , O2 ) ∩ SC (c' , O2 , O1 ) |
TO ' ' (c, O1 , O2 ) := max c '∉C2
| SC (c, O1 , O2 ) ∪ SC (c' , O2 , O1 ) |
P (O1 , O 2 ) = TO (O1 , O2 )
R (O1 , O 2 ) = TO (O2 , O1 )
2 ⋅ P(O1 , O2 ) ⋅ R (O1 , O2 )
F (O1 , O2 ) =
P (O1 , O2 ) + R (O1 , O2 )
53 54
Evaluation of Ontology Learning Starting Point in OL from text
• The aposteriori Approach: • Context-based approaches:
– ask domain expert for a per concept evaluation of the learned – Distributional Hypothesis [Harris 85]:
ontology „Words are (semantically) similar to the
– Count three categories of concepts: extent to which they appear in similar (syntactic) contexts“
• Correct : both in learned and the gold ontology – leads to creation of groups
• New : only in learned ontology, but relevant and should be in gold
standard as well
• Spurious: useless • Looking for explicit information:
– Compute precision = (correct + new) / (correct + new + – Texts
spurious) – WWW
• As the result: – Thesauri
The a priori evaluations are aweful – BUT
A posteriori evaluations by domain experts still show
very good results, very helpful for domain expert!
Sabou M., Wroe C., Goble C. and Mishne G.,Learning Domain Ontologies for Web Service Descriptions: an
Experiment in Bioinformatics, In Proceeedings of the 14th International World Wide Web Conference (WWW2005), 55 56
Chiba, Japan, 10-14 May, 2005.

15. Looking for explicit information Pattern based approaches (Hearst Patterns)
There are two sources: • Match patterns in corpus:
• NP0 such as NP1 ... NPn-1 (and|or) NPn
• such NP0 as NP1 ... NPn-1 (and|or) NPn
• Looking for patterns in texts: • NP1 ... NPn (and|or) other NP0
– ‚is-a‘ patterns [Hearst 92,98],[Poesio et al. 02], [Ahmid et al. 03] • NP0, (including,especially) NP1 ... NPn-1 (and|or) NPn
– ‚part-of‘ patterns [Charniak et al. 99]
– ‚causation‘ patterns [Girju 02/03]
for all NPi 1 ≤ i ≤ n isa Hearst (head(NPi ), head(NP0 ))
# HearstPatterns(t1 , t 2 )
isa Hearst (t1 , t 2 ) =
# HearstPatterns(t1 ,*)
• Using the Web:
– [Etzioni et al. 04] • isaHearst(conference,event)=0.44
• isaHearst(conference,body)=0.22
– [Cimiano et al. 04]
• isaHearst(conference,meeting)=0.11
• isaHearst(conference,course)=0.11
• isaHearst(conference,activity)=0.11
57 58
WWW Patterns The Vector-Space Model
Generate patterns: • Idea: collect context information based on the
• <t1>s such as <t2> distributional hypothesis and represent it as a
• such <t1>s as <t2> vector:
• <t1>s, especially <t2>
• <t1>s, including <t2>
• <t2> and other <t2>s die_from suffer_from enjoy eat
• <t2> or other <t2>s
disease X X
and Query the Web using the GoogleAPI: cirrhosis X X
# Patterns(t1 , t 2 ) • compute similarity among vectors
isa WWW (t1 , t 2 ) =
# Patterns(t1 ,*) wrt. to some measure
59 60

16. Clustering Concept Hierarchies from Text Context Extraction
• Observation: ontology engineers need information about • extract syntactic dependencies from text
the effectiveness, efficiency and trade-offs of different ⇒ verb/object, verb/subject, verb/PP relations
approaches ⇒ car: drive_obj, crash_subj, sit_in, …
• LoPar, a trainable statistical left-corner parser:
• Similarity-based
– agglomerative/bottom-up
– divisive/top-down: Bi-Section-KMeans
Parser tgrep Lemmatizer Smoothing
• Set-theoretical
– set operations (inclusion)
– FCA, based on Galois lattices
Lattice
FCA Pruning Weighting
Compaction
[Cimiano et al. 03-04]
61 62
Example Weighting (threshold t)
• People book hotels. The man drove the bike • Conditional: P(n | varg )
along the beach.
⎛ P(n | varg ) ⎞
• Hindle: P (n | varg ) ⋅ log⎜
⎜ P ( n) ⎟ ⎟
book_subj(people)
⎝ ⎠
book_subj(people)
book_obj(hotels) book_obj(hotel)
drive_subj(man) ⎛ P(n | varg ) ⎞
drove_subj(man) • Resnik: S R (varg ) ⋅ P(n | varg ) ⋅ log⎜
⎜ P ( n) ⎟ ⎟
drove_obj(bike) Lemmatization drive_obj(bike) ⎝ ⎠
drove_along(beach) drive_along(beach) ⎛ P(n' | varg ) ⎞
S R (varg ) = ∑ P(n' | varg ) ⋅ log⎜⎜ P ( n' ) ⎟ ⎟
n' ⎝ ⎠
63 64

17. Tourism Formal Context Tourism Lattice
bookable rentable driveable rideable joinable
appartment X X
car X X X
motor-bike X X X X
excursion X X
trip X X
65 66
Concept Hierarchy Agglomerative/Bottom-Up Clustering
bookable
rentable joinable
driveable appartment excursion trip
rideable car
car bus appartment excursion trip
bike
67 68

18. Linkage Strategies Bi-Section-KMeans
• Complete-Linkage:
– consider the two most dissimilar elements of each of the clusters car appartment bus
=> O(n2 log(n)) trip excursion
• Average-Linkage:
– consider the average similarity of the elements in the clusters appartment excursion
=> O(n2 log(n)) car trip
bus
• Single-Linkage:
– consider the two most similar elements of each of the clusters
=> O(n2)
bus car appartment excursion trip
bus car
69 70
Data Sets Results Tourism Domain
• Tourism (118 Mio. tokens):
– http://www.all-in-all.de/english
– http://www.lonelyplanet.com
– British National Corpus (BNC)
– handcrafted tourism ontology (289 concepts)
• Finance (185 Mio. tokens):
– Reuters news from 1987
– GETESS finance ontology (1178 concepts)
71 72

19. Results in Finance Domain Results Tourism Domain
73 74
Results in Finance Domain Summary
Effectiveness Efficiency Traceability
FCA 43.81/41.02% O(2n) Good
Agglomerative 36.78/33.35% O(n2 log(n)) Fair
Clustering 36.55/32.92% O(n2 log(n))
38.57/32.15% O(n2)
Divisive 36.42/32.77% O(n2) Weak-Fair
Clustering
75 76

20. Other Clustering Approaches Ontology Learning References
• Bottom-Up/Agglomerative
• Reinberger, M.-L., & Spyns, P. (2005). Unsupervised text mining for the learning of dogma-inspired ontologies. In Buitelaar, P., Cimiano, P., & Magnini,
B. (Eds.), Ontology Learning from Text: Methods, Evaluation and Applications.
• Philipp Cimiano, Andreas Hotho, Steffen Staab: Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text. ECAI
– (ASIUM System) Faure and Nedellec 1998 •
2004: 435-439
P. Cimiano, A. Pivk, L. Schmidt-Thieme and S. Staab, Learning Taxonomic Relations from Heterogenous Evidence. In Buitelaar, P., Cimiano, P., &
– Caraballo 1999
Magnini, B. (Eds.), Ontology Learning from Text: Methods, Evaluation and Applications.
• Sabou M., Wroe C., Goble C. and Mishne G.,Learning Domain Ontologies for Web Service Descriptions: an Experiment in Bioinformatics, In Proceeedings
of the 14th International World Wide Web Conference (WWW2005), Chiba, Japan, 10-14 May, 2005.
– (Mo‘K Workbench) Bisson et al. 2000 • Alexander Maedche, Ontology Learning for the Semantic Web, PhD Thesis, Kluwer, 2001.
• Alexander Maedche, Steffen Staab: Ontology Learning for the Semantic Web. IEEE Intelligent Systems 16(2): 72-79 (2001)
• Alexander Maedche, Steffen Staab: Ontology Learning. Handbook on Ontologies 2004: 173-190
• Other: • M. Ciaramita, A. Gangemi, E. Ratsch, J. Saric, I. Rojas. Unsupervised Learning of semantic relations between concepts of a molecular biology ontology.
IJCAI, 659ff.
– Hindle 1990 • A. Schutz, P. Buitelaar. RelExt: A Tool for Relation Extraction from Text in Ontology Extension. ISWC 2005.
– Pereira et al. 1993 • Faure, D., & N´edellec, C. (1998). A corpus-based conceptual clustering method for verb frames and ontology. In Velardi, P. (Ed.), Proceedings of the
LREC Workshop on Adapting lexical and corpus resources to sublanguages and applications, pp. 5–12.
– Hovy et al. 2000
• Michele Missikoff, Paola Velardi, Paolo Fabriani: Text Mining Techniques to Automatically Enrich a Domain Ontology. Applied Intelligence 18(3): 323-340
(2003).
• Gilles Bisson, Claire Nedellec, Dolores Cañamero: Designing Clustering Methods for Ontology Building - The Mo'K Workbench. ECAI Workshop on
Ontology Learning 2000
77 78
Lots of Overlapping Ontologies
Agenda on the Semantic Web
• Introduction
• Foundations of the Semantic Web Search Swoogle
• Ontology Learning for “publication”
• Learning Ontology Mapping 185 matches in
• Semantic Annotation the repository
• Using Ontologies Different
• Applications definitions,
viewpoints,
notions
79 80
© Noy

21. Creating Correspondences Between Ontologies
81 82
© Noy
Ontology-to-Ontology Mappings:
Ontology-level Mismatches Sources of information
• The same terms describing different concepts
• Different terms describing the same concept • Lexical information: edit distance, …
• Different modeling paradigms • Ontology structure: subclassOf, instanceOf,…
– e.g., intervals or points to describe temporal aspects
• User input: “anchor points”
• Different modeling conventions
• External resources: WordNet,…
• Different levels of granularity
• Prior matches
• Different coverage
• Different points of view
• ...
83 84
© Noy © Noy

22. Mapping Methods Example Thing
simLabel = 0.0 Vehicle
simSuper = 1.0
• Heuristic and Rule-based methods simInstance = 0.9 1.0
Automobile hasSpecification
simRelation = 0.9
• Graph analysis simAggregation = 0.7 Speed
Object
Marc’s Porsche fast
• Probabilistic approaches 0.7
Vehicle
hasOwner 0.9
• Reasoning, theorem proving
Boat
Owner Car 0.9
• Machine-learning hasSpeed Speed
Marc
Porsche KA-123 250 km/h
85 86
Mapping Methods GLUE: Defining Similarity
A,S
Assoc. Prof Snr. Lecturer ¬A, S
• Heuristic and Rule-based methods
A,¬S
Hypothetical
• Graph analysis Common
Marked up
domain
• Probabilistic approaches
¬A,¬S
• Reasoning, theorem proving P(A ∩ S) P(A,S)
Sim(Assoc. Prof., Snr. Lect.) = =
[Jaccard, 1908] P(A ∪ S) P(A,¬S) + P(A,S) + P(¬A,S)
• Machine-learning Joint Probability Distribution: P(A,S),P(¬A,S),P(A,¬S),P(¬A,¬S)
Multiple Similarity measures in terms of the
87 JPD 88

23. GLUE: No common data instances Machine Learning for computing similarities
¬A,¬S United States ¬A,S A,¬S Australia ¬A,¬S
In practice, not easy to find data tagged with both A S
ontologies !
A S
¬S
¬A
¬S
¬A A,¬S A,S A,S ¬A,S
United States Australia A S
CLA CLS
Solution: Use Machine Learning ¬A ¬S
JPD estimated by counting the sizes of the partitions
89 90
GLUE: Improve Predictive Accuracy – Use Multi-
Strategy Learning GLUE Next Step: Exploit Constraints
Single Classifier cannot exploit all available information • Constraints due to the taxonomy structure
Combine the prediction of multiple classifiers Parents
People Staff
Staff
A
Meta-Learner Staff Fac Acad Tech
CLA1 A Children
¬A Prof Assoc. Prof Asst. Prof Prof Snr. Lect. Lect.
…
A
¬A
CLAN
¬A
• Domain specific constraints
– Department-Chair can only map to a unique concept
Content Learner
Frequencies on different words in the text in the data instances
Name Learner
• Numerous constraints of different types
Words used in the names of concepts in the taxonomy
Extended Relaxation Labeling to ontology matching
Others …
91 92

24. Putting it all together GLUE System APFEL: Similarity Features
Mappings for O1 , Mappings for O2
Feature Similarity Measure
Concepts label String Similarity
Relaxation Labeler
subclassOf Set Similarity
Generic & Domain Similarity Matrix
constraints instances Set Similarity
Similarity Estimator …
Similarity function Relations
Joint Distributions:P(A,B),P(A,¬B),…
Instances
Meta Learner
Distribution Estimator Distribution Aggregation - Example: sim(e, f ) = ∑ wk simk (e, f )
Learner CL1 Learner CLN Estimator
k
Taxonomy O1 Taxonomy O2 Interpretation: map(e1j) = e2j ← sim(e1j ,e2j)>t
93 94
(structure + data instances) (structure + data instances)
APFEL: Optimize Integration Duplicate Recognition
Iterations
Generation
Of Initial Pair
Features
Ontologies
Entity
Alignments User x
Similarity Training:
Aggregation Optimized
Interpretation
Selection Validation Feature/Similarity Alignment
Weighting Scheme Method
and Threshold
Simple Generation of
Alignment Feature/Similarity
Fixing • Do two objects refer to the same entity?
Input Output
Method Hypotheses – We know objects have the same type (their types are
mapped/merged)
• Examples
– Duplicate removal after merging knowledge bases
– Citation matching
95 96
© Noy

25. Using External Sources for Duplicate Recognition Duplicate Recognition: Citation Matching
Appolo (USC/ISI) Pasula, Marthi, et.al. (UC Berkeley)
– Combines information- – Performs citation matching based on probability
integration mediator models for
(Prometheus) with a record-
• author names
linkage system (Active Atlas)
• titles
– Uses a domain model of
sources and information that • title corruption, etc.
they provide – Extends standard domain model to
incorporate probabilities
– Learns probability models from large
data sets
97 98
© Noy © Noy
References Agenda
User Input driven - Prompt, Chimaera, ONION
Chimaera (Stanford KSL; D. McGuinness et al) • Introduction
AnchorPrompt (Stanford SMI; Noy, Musen et al) • Foundations of the Semantic Web
Similarity Flooding (Melnik, Garcia-Molina, Rahm)
• Ontology Learning
IF-Map (Kalfoglou, Schorlemmer)
• Learning Ontology Mapping
Using metrics to compare OWL concepts (Euzenat and Volchev)
QOM (Ehrig and Staab)
• Semantic Annotation
• Using Ontologies
Corpus of Matches (O.Etzioni, A. Halevy, et.al.)
APFEL (Ehrig, Staab, Sure) • Applications
SAT Reasoning - S-Match (U. Trento; Serafini et al)
Mapping Composition: Semantic gossiping (Aberer et al),
Piazza (Halevy et al), Prasenjit Mitra
99 100

26. CREAM – Creating Metadata Annotation by Markup
[K-CAP 2001; [K-CAP 2001]
WWW 2002]
Generate
Generate
Class
Class
Instance Download of
Instance
markup-only
version of
Attribute
Attribute OntoMat from
Instance
Instance
DAML
Onto- http://annotation.
Agents semanticweb.org
Relationship Relationship
Instance Instance
101 102
Annotation by Authoring [WWW 2003] Annotation vs. Deep Annotation
Input Annotation Output Ontology
[WWW 2002]
Create Text and Ontology
based-
if possible Links Metadata
out of a Class
Instance
Input Deep Annotation Output
Attribute Instance
Relationship
Ontology
Instance
generates
simple text
Mapping
Rules
103 DB Database 104
DB

27. The annotation problem from a scientific point
The annotation problem in 4 cartoons of view
105 106
© Cimiano
The annotation problem in practice The vicious cycle
107 108

28. Current State-of-the-art Semi-automatic Annotation
• ML-based IE (e.g.Amilcare@{OntoMat,MnM}) [EKAW 2002]
– start with hand-annotated training corpus
– rule induction
• Standard IE (MUC)
– handcrafted rules
– Wrappers
• Large-scale IE [SemTag&Seeker@WWW‘03]
EU IST
– Large scale system
Dot-Kom
– disambiguation with TAP
• (C-)Pankow (Cimiano et.al. WWW’04, WWW’05)
• KnowItAll (Etzioni et al. WWW‘04)
109 110
Comparison of CREAM and S-CREAM Different Results
Core processes: Input, Output <hotel> Zwei Linden </hotel> Zwei Linden InstOf Hotel
– (M) Manual Annotation (OntoMat) Relational Metadata Zwei Linden Locatet_At Dobbertin
– (A1) Information Extraction (Amilcare) XML annotated Dokument <city>Dobbertin</city> Dobbertin InstOf City
Zwei Linden Has_Room single_room_1
M <singleroom>Single room</singleroom> single_room1 InstOf Single_Room
single_room1 Has_Rate rate1
Thing rate1 InstOf Rate
<price>25,66</price> rate1 Price 25,66
<hotel> region accommodation <currency>EUR</currency> rate1 Currency EUR
A1 Zwei Linden Located_at Zwei Linden Has_Room double_room1
</hotel>
Document IE
<city>
Dobbertin
? City
Located_at
Hotel <doubleroom>Double room</doubleroom> double_room1 InstOf Double_Room
double_room1 Has_Rate rate2
rate2 InstOf Rate
</city>
Dobbertin Zwei Linden <price>43,66</price> rate2 Price 43,46
<currency>EUR</currency> rate2 Currency EUR
Amilcare (IE-Tool) OntoMat-Annotizer
111 112

29. Comparison of CREAM and S-CREAM IE and Wrapper Learning
Core processes: Input, Output
– (M) Manual Annotation (OntoMat) Relational Metadata • Boosted wrapper induction
– (A1) Information extraction (Amilcare) XML annotated Document
• Exploiting linguistic constraints
M • Hidden Markov models
Thing
• Data mining and IE
<hotel> DR region accommodation • Bootstrapping
Zwei Linden A2 A3 Located_at
• First-order learning
A1 Hotel
</hotel>
Document IE City Hotel
City
<city>
Dobbertin Hotel Located_at
</city> City
Dobbertin Zwei Linden
Currently: Simple Centering-Modell
Future: Learn Coherency Rules 113 114
Wrapper SemTag
No tutorial about IE and Wrapper learning but… • The goal is to add semantic tags to the existing HTML
body of the web.
• IE often focuses on small number of classes • SemTag uses TAP, where TAP is a public broad,
shallow knowledgebase.
• Is not easily adaptable to new domains
• TAP Contains lexical and taxonomical information
• Needs a lot of trainings examples
about popular objects like music, movies, sports, etc.
Needed Example:
“The Chicago Bulls announced that Michael Jordan will…”
Will be:
• It would be great if IE would scale to a large number The <resource ref = http://tap.stanford.edu/Basketball
of classes (concepts) on a large amount of unlabeled Team_Bulls>Chicago Bulls</resource> announced yesterday
data that <resource ref = “http://tap.stanford.edu/
AthleteJordan_Michael”> Michael Jordan</resource> will...’’
115 116
Dill et al, SemTag and Seeker. WWW’03

30. SemTag The Self-Annotating Web
• Lookup of all instances from the ontology (TAP) – 65K
instances
• There is a huge amount of non-formalized
• Disambiguate the occurrences as: knowledge in the Web
– One of those in the taxonomy
– Not present in the taxonomy
• Placing labels in the taxonomy is hard • Use statistics to interpret this non-formalized
• Use bag-of-words approach for disambiguation knowledge and propose formal annotations:
• 3 people evaluated 200 labels in context – agreed on only
68.5% - metonymy
semantics ≈ syntax + statistics?
• Applied on 264 million pages
• Produced 550 million labels and 434 spots
• Accuracy 82% • Annotation by maximal statistical evidence
117 118
Dill et al, SemTag and Seeker. WWW’03
PANKOW: Pattern-based ANnotation through
Knowledge On the Web Patterns (Cont‘d)
• HEARST1: <CONCEPT>s such as <INSTANCE> • DEFINITE1: the <INSTANCE> <CONCEPT>
• HEARST2: such <CONCEPT>s as <INSTANCE> • DEFINITE2: the <CONCEPT> <INSTANCE>
• HEARST3: <CONCEPT>s, (especially/including) <INSTANCE>
• HEARST4: <INSTANCE> (and/or) other <CONCEPT>s • APPOSITION:<INSTANCE>, a <CONCEPT>
• COPULA: <INSTANCE> is a <CONCEPT>
• Examples:
– countries such as Niger • Examples:
– such countries as Niger • the Niger country
– countries, especially Niger
• the country Niger
– countries, including Niger
• Niger, a country in Africa
– Niger and other countries instanceOf(Niger,country) instanceOf(Niger,country)
• Niger is a country in Africa
– Niger or other countries
119 120

31. PANKOW Process Gimme‘ The Context: C-PANKOW
• Contextualize the pattern-matching by taking into
account the similarity of the Google-abstract in which the
pattern was matched and the one to be annotated
• Download a fixed number n of Google-abstracts
matching so-called clues and analyze them linguistically,
matching the patterns offline:
– match more complex structures
– more efficient as the number of Google-queries only depends on n
– more offline processing, reducing network traffic
121 122
Comparison Web-scale information extraction
System # Recall/ Learning Accuracy KnowItAll Idea:
Accuracy
– Web is the largest knowledge base
[MUC-7] 3 >> 90% n.a.
– The goal is to find all instances corresponding to a given concept in the
[Fleischman02] 8 70.4% n.a. web and extract them
PANKOW 59 24.9% 58.91% The System is:
[Hahn98] –TH 325 21% 67%
– Domain-Independent
[Hahn98]-CB 325 26% 73% – Use Bootstrap technique
– Based on Linguistic Patterns
[Hahn98]-CB 325 31% 76%
KnowItAll vs (C-)Pankow
C-PANKOW 682 29.35% 74.37%
- Pankow starts from a Web page and annotates a given term on the
page using the Web
[Alfonseca02] 1200 17.39% 44%
(strict)
- KnowItAll starts from a concept and aims at finding all instances on the
Web
LA based on least common superconcept123 124
O. Etzioni, 2004.
Etzioni,
lcs of two concepts (Hahn et.al. 98)