The document proposes an approach called BLOOMS to identify schema level relationships between ontologies in the Linked Open Data cloud by leveraging the structured knowledge in Wikipedia categories to build context trees for ontology classes and identify alignments based on the overlap between the trees. BLOOMS pre-processes ontology classes, maps them to Wikipedia articles, constructs category trees up to 4 levels, prunes irrelevant categories, and determines subclass relationships between ontologies based on the overlap between their respective trees. The approach aims to enhance existing ontology matching techniques for the noisy and loosely structured ontologies commonly found in the LOD cloud.

1. About 22 years ago..
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2. 11 years later…
Image from Scientific American Website

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6. Tim Berners-Lee 2006
1. Use URIs as names for things
2. Use HTTP URIs so that people can look up those names.
3. When someone looks up a URI, provide useful
information, using the standards (RDF*, SPARQL)
4. Include links to other URIs. so that they can discover more
things.
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7. In 2006 Web of Data
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8. Linked Open Data
• Massive collection of instance data
• Primarily connected via owl:sameAs relationship
• Excellent source of information for background
knowledge
• Labeled as mainstream Semantic Web
7/2/2012 8
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9. Is it really mainstream Semantic Web?
• What is the relationship between the models
whose instances are being linked?
• How to do querying on LOD without knowing
individual datasets?
• How to perform schema level reasoning over
LOD cloud?
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10. What can be done?
• Relationships are at the heart of Semantics
• LOD primarily consists of owl:sameAs links
• LOD captures instance level relationships, but lacks
class level relationships.
o Superclass
o Subclass
o Equivalence
• How to find these relationships?
o Perform a matching of the LOD Ontology’s using state of the art ontology matching tools.
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11. Linked Data Alignment
and Enrichment
Proposal Defense June 11th, 2012
Prateek Jain
Kno.e.sis Center
Wright State University, Dayton, OH

12. Agenda
• Motivation and Significance of this research
• Research questions and proposed solutions
• State of the current research and planned
work
• Questions and comments
14th February 2012 12

13. Linked Open Data
• A set of best practices for publishing and
connecting structured data on the Web
• Practices have been adopted by an increasing
number of data providers in the past 5 years
• Latest count is at 295 datasets with over 50
Billion triples (approx)
14th February 2012 13

14. Linked Open Data 2007 (May)
Linking Open Data cloud diagram, this and subsequent pages, by Richard Cyganiak and AnjaJentzsch. http://lod-cloud.net/
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15. Linked Open Data 2007 (Oct)
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16. Linked Open Data 2009
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17. Linked Open Data 2011
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18. Linked Open Data
Number of Datasets Number of triples (Sept 2011)
31,634,213,770
2011-09-19 295
with 503,998,829 out-links
2010-09-22 203
2009-07-14 95
2008-09-18 45
2007-10-08 25
2007-05-01 12
From http://www4.wiwiss.fu-berlin.de/lodcloud/state/
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19. 6 years of existence how
many applications come to
your mind?
7/2/2012 19

20. 20

21. Reality…
• “We DID NOT use the entire Dbpedia or LOD.
The only component of LOD which helped us
with Watson was YAGO class hierarchy present
in DBpedia. We had strict information gain
requirements and other components honestly
did not help much“
– Researcher with the Watson Team
7/2/2012 21
21

22. Why?

23. A simple query..
“Identify congress members, who have voted “No”
on pro environmental legislation in the past four
years, with
high-pollution industry in their congressional
districts.”
But even with LOD we cannot answer this query.
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24. Example: GovTrack
Vote: 2009- vote:hasOption
vote:vote 887 Votes:2009-887/+
vote:votedBy
Aye rdfs:label
vote:hasAction
people/P000197
H.R. 3962: Affordable
Health Care for America
dc:title
Act name
On Passage: H R
dc:title 3962 Affordable Nancy Pelosi
Health Care for
Bills:h3962 America Act
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25. Example: GeoNames
rdfs:subClassOf?
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26. Our Approach
Use knowledge contributed by users
To enhance existing approaches
to solve these issues:
• Ontology integration
• Detection relationships within
LOD
and across datasets
Cloud
• Querying multiple datasets
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27. Circling Back
• LOD captures instance level relationships, but
lacks class level relationships.
o Superclass
o Subclass
o Equivalence
7/2/2012 28
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28. BLOOMS – Bootstrapping …

29. • BLOOMS - Bootstrapping-based Linked Open
Data Ontology Matching System
• Developed specifically for LOD Ontologies
• Identifies schema level links between different
LOD datasets
• Aligns ontologies belonging to diverse domains
using diverse data sources
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30. Existing Approaches
A survey of approaches to automatic Ontology matching by Erhard Rahm, Philip A. Bernstein in the VLDB Journal 10:
334–350 (2001)
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31. LOD Ontology Alignment
• Actual Results from these techniques
 Nation = Menstruation, Confidence=0.9 
• They perform extremely well on established benchmarks, but
typically not in the wilds.
• LOD Ontology’s are of very different nature
• Created by community for community.
• Emphasis on number of instances, not number of meaningful relationships.
• Require solutions beyond syntactic and structural matching.
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32. Rabbit out of a hat?
• Traditional auxiliary data sources
(WordNet, Upper Level Ontologies) have limited
coverage.
• Community generated is noisy, but is rich in
• Content
• Structure
• Has a “self healing property”
• Problems like Ontology Matching have a
dimension of context associated with them.
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33. Wikipedia
• The English version alone has more than 2.9
million articles
• Continually expanded by approx. 100,000 active
volunteer editors
• Multiple points of view are mentioned with
proper contexts
• Article creation/correction is an ongoing activity 34

34. Ontology Matching using Wikipedia
• On Wikipedia, categories are used to organize
the entire project.
• Wikipedia's category system consists of
overlapping trees.
• Simple rules for categorization
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35. BLOOMS Approach – Step 1
• Pre-process the input ontology
 Remove property restrictions
 Remove individuals, properties
• Tokenize the class names
 Remove underscores, hyphens and other delimiters
 Breakdown complex class names
• example: SemanticWeb => Semantic Web
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36. BLOOMS Approach – Step 2
• Identify article in Wikipedia corresponding to the concept.
o Each article related to the concept indicates a sense of the usage of the
word.
• For each article found in the previous step
o Identify the Wikipedia category to which it belongs.
o For each category found, find its parent categories till level 4.
• Once the “BLOOMS tree” for each of the sense of the source
concept is created (Ts), utilize it for comparison with the
“BLOOMS tree” of the target concepts (Tt).
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37. BLOOMS Approach – Step 3
• In the tree Ts, remove all nodes for which the parent node
which occurs in Tt to create Ts’.
o All leaves of Ts are of level 4 or occur in Tt.
o The pruned nodes do not contribute any additional new knowledge.
• Compute overlap Os between the source and target tree.
o Os= n/(k-1), n = |z|, zε Ts’ ΠTt, k= |s|, sε Ts’
• The decision of alignment is made as follows.
o For Ts εTc and Ttε Td, we have Ts=Tt, then C=D.
o If min{o(Ts,Tt),o(Tt,Ts)} ≥ x, then set C rdfs:subClassOf D if o(Ts,Tt) ≤ o(Tt,
Ts), and set D rdfs:subClassOf C if o(Ts, Tt) ≥ o(Tt, Ts).
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38. Example
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39. Evaluation Objectives
• To examine BLOOMS as a tool for the purpose of LOD
ontology matching.
• To examine the ability of BLOOMS to serve as a general
purpose ontology matching system.
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40. Circling Back
• LOD primarily consists of owl:sameAs links
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41

41. Part of Relationship
Identification

42. Partonomy Identification
• Currently entities across datasets are linked using primarily the
owl:sameAs relationship
• Relationships such as partonomy (part-of), and causality can
allow creating even more intelligent applications such as Watson
• Approach PLATO (Part-Of relation finder on Linked Open DAta
Tool)
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43. PLATO Approach
• PLATO generates all possible partonomically
linked pairs between the entities in the dataset.
o Utilize “strongly” associated entities
• Identify the type of each entity in the pair using
WordNet.
o Use Class Names
o Gives the lexicographer files for the synsets
corresponding to these entities
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44. Winston’s Taxonomy
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45. PLATO Approach – Step 2
• PLATO generates linguistic patterns for each applicable
property based on linguistic cues suggested by Winston.
o Cell Wall is made of Cellulose
• Tests the lexical patterns for each entity pair in a corpus-
driven manner.
o Using Web as a corpus
• PLATO counts the total number of web pages that contain
the pattern
o Parse the page and identify the occurance of pattern.
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46. PLATO Approach – Step 3
• Asserts the partonomy property with strongest supporting
evidence
o Cell Wall is made of Cellulose, 48
o Cellulose is made of Cell Wall, 10
• PLATO also enriches the schema by generalizing from the
instance level assertions.
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47. Evaluation Objectives
• To examine PLATO as a tool for finding different kinds of
part-of relation.
• To examine PLATO as a tool for finding part-of relation
within a dataset
• To examine PLATO as a tool for finding part-of relation
across dataset
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48. 49

49. BLOOMS BLOOMS+ PLATO Others
2010 1. 1 paper at ISWC 1. Paper at AAAI SS
2. 1 paper at OM 2. Paper at GEOS
workshop
2011 1. 1 paper at ESWC
2. Workshop at ICBO
2012 1. 1 paper at ACM
Hypertext
Total of 7 publications covering this research
14th February 2012 50

50. Research Plan
• Evaluation of BLOOMS on LOD ontologies
• Evaluation of PLATO
• Automatic classification of datasets
• Property alignment on LOD
14th February 2012 51

51. Questions?