This document describes the BLOOMS+ approach for performing contextual ontology alignment of Linked Open Data datasets with an upper ontology. It presents the challenges of existing ontology matching approaches when applied to large, diverse LOD datasets. BLOOMS+ leverages structured knowledge from Wikipedia categories to generate "BLOOMS trees" representing different senses of concept names, and calculates similarity between trees to identify subclass, equivalence and other relationships between concepts in different datasets. It outperforms existing approaches on aligning three real-world LOD ontologies to the PROTON upper ontology. Future work aims to improve the weighting and identify additional contextual sources to assist with schema alignment across datasets.

1. Contextual Ontology Alignment of LOD with
an Upper Ontology: A Case Study with
Proton
PrateekJain, Peter Z. Yeh, KunalVerma, Reymonrod Vasquez, Mariana
Damova, Pascal Hitzler and Amit P. Sheth
Kno.e.sis, Wright State University, Dayton, OH
Ontotext, Sofia, Bulgaria,
Accenture Technology Labs, San Jose, CA

2. Outline
• Introduction
• Background
• Challenges
• Existing Approaches
• BLOOMS+ Approach
• Conclusion & Future Work
• References
2

3. Outline
• Introduction
• Background
• Challenges
• Existing Approaches
• BLOOMS+ Approach
• Conclusion & Future Work
• References
3

4. Web of Data
4

5. Linked Open Data
• “The term Linked Data is used to describe a method of exposing,
sharing, and connecting data via de-referenceable URIs on the
Web.”- Wikipedia
• Datasets part of Linked Open Data include
– Geographical Datasets
– Movies
– Life Science, Genes, Proteins
– General Information (Wikipedia), Customer Reviews,…
– US Census, Senator Voting Records,….
• Links primarily at instance level to assert equality between
entities
Example: linkedMDB:film/77 owl:sameAsdbpedia:resource/Pulp_Fiction
• By September 2010 LOD is estimated to have 25 billion RDF
triples, interlinked by around 395 million RDF links.
5

6. Outline
• Introduction
• Background
• Challenges
• Existing Approaches
• BLOOMS+ Approach
• Conclusion & Future Work
• References
6

7. If everything is nice, why am I here..
• Lack of Conceptual Description of Datasets
• Absence of Schema Level Links
• Lack of expressivity
• Difficulties with respect to querying using SPARQL
– Schema heterogeneity
– Entity disambiguation
– Ranking of results
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8. What can be done?
• Relationships are at the heart of Semantics.
• LOD captures instance level relationships, but lacks class level
relationships.
– Superclass
– Subclass
– Equivalence
• How to find these relationships?
– Perform a matching of the LOD Ontology’s using state of the art schema
matching tools.
• Desirable
– Considering the size of LOD, at least have results which a human can
curate.
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9. Schema Matching
• Schema matching is the process of identifying that two objects
are semantically related.
• In two schemas DB1.Student (Name, SSN, Level, Major, Marks)
and DB2.Grad-Student (Name, ID, Major, Grades); possible
matches would be: DB1.Student ≈ DB2.Grad-Student; DB1.SSN =
DB2.ID etc. and possible transformations or mappings would be:
DB1.Marks to DB2.Grades (100-90 A; 90-80 B..).
• Need for high quality data for querying and analytics in large
enterprises.
• Schema mapping provides a way of resolving discrepancies in
data.
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10. Why does it matters?
• Massive amount of data available within enterprise which refers
to same entities, terminology is different.
• Enterprise information asset awareness.
• Finding relevant and related schemata,
• Project planning.
– Can project specific requirements be fulfilled with the data at
disposal.
• Generating an exchange schema.
– Collaboration with clients which use different schemas.
Reference: K. Smith, P. Mork, L. Seligman, A. Rosenthal, M. Morse, D. Allen, and M. Li. The Role
of Schema Metching in Large Enterprises. CIDR, 2009. 10

11. Outline
• Introduction
• Background
• Challenges
• Existing Approaches
• BLOOMS+ Approach
• Conclusion & Future Work
• References
11

12. Existing Approaches
A survey of approaches to automatic Ontology matching by Erhard Rahm, Philip A. Bernstein in the VLDB
Journal 10: 334–350 (2001) 12

13. Outline
• Introduction
• Background
• Challenges
• Existing Approaches
• BLOOMS+ Approach
• Conclusion & Future Work
• References
13

14. Our Approach
Use knowledge contributed by users Structured knowledge contributed by
users
To improve
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15. Rabbit out of a hat?
• Traditional auxiliary data sources like (WordNet, Upper Level
Ontologies) have limited coverage and are insufficient for LOD
datasets.
• LOD datasets have diverse domains
• Community generated data although noisy but is rich in
• Content
• Structure
• Has a “self healing property”
• Problems like Schema Matching have a dimension of context
associated with them. Since community generated data is
created by diverse set of people, hence captures diverse
context.
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16. Wikipedia
• The English version alone contains more than 2.9 million
articles.
• It is continually expanded by approximately 100,000 active
volunteer editors world-wide.
• Allows multiple points of view to be mentioned with their proper
contexts.
• Article creation/correction is an ongoing activity with no down
time.
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17. Schema Matching on LOD using Wikipedia
Categorization
• On Wikipedia, categories are used to organize the entire project.
• Wikipedia's category system consists of overlapping trees.
• Simple rules for categorization
– “If logical membership of one category implies logical
membership of a second, then the first category should be
made a subcategory”
– “Pages are not placed directly into every possible category,
only into the most specific one in any branch”
– “Every Wikipedia article should belong to at least one
category.”
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18. BLOOMS+ Approach – Step 1
• Pre-process the input schema
• 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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19. BLOOMS+ Approach – Step 2
• For each concept name processed in the previous step
– Identify article in Wikipedia corresponding to the concept.
– Each article related to the concept indicates a sense of the usage of the
word.
• For each article found in the previous step
– Identify the Wikipedia category to which it belongs.
– 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 (Ti), utilize it for comparison with the
“BLOOMS tree” of the target concepts (Tj).
– BLOOMS trees are created for individual senses of the concepts.
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20. BLOOMS+ Approach – Step 3
• In the tree Ti, find n (the number of common nodes which occurs
in Tj).
• Compute overlap Os between the source and target tree.
• Exponentiation of the inverse depth of common node gives less
node to nodes which appear lower in the hierarchy (generic
nodes)
• Log of tree avoids bias against large trees.
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21. Contextual Similarity
• BLOOMS+ computes contextual similarity between a source
class C and target D to further determine if they should be
aligned.
• Information about super classes of C and D is a good source of
contextual information.
• If the super classes agree, it is a good alignment otherwise it
should be penalized.
• For example, Jaguar has super classes such as Car and Vehicle,
and Cat has super classes such as Feline and Mammal, then the
alignment should be penalized because its contextual similarity
is low.
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22. BLOOMS+ Approach – Step 4
• BLOOMS+ retrieves all super classes of C and D up to level 2
(can be changed). The set of super classes is N( C ) and N (D).
• For each BLOOMS+ tree pair ( Ti, Tj) between C and D, BLOOMS+
determines the number of super classes in N(C) and N(D) in
following way.
• A super class c ∈ N(C) is supported by Tiif either of the following
conditions are satisfied:–
– The name of c matches a node inTj
– The Wikipedia article (or article category) corresponding to c
based on a Wikipedia search web service call using the name
of c – matches a node in Ti.
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23. BLOOMS Approach – Step 5
• BLOOMS+ computes the overall contextual similarity between C
and D with respect to Ti and Tj using the harmonic mean, which
is instantiated as:
• We chose the harmonic mean to emphasize super class
neighborhoods that are not well supported (and hence should
significantly lower the overall contextual similarity).
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24. BLOOMS Approach – Step 6
• BLOOMS+ computes the overall similarity between classes C
and D w.r.t. BLOOMS+ trees Ti and Tj by taking the weighted
average of the class and contextual similarity.
• BLOOMS+ defaults alpha and beta to 1 to give equal importance.
• BLOOMS+ then selects the tree pair (Ti,Tj) ∈ FC × FD with the
highest overall similarity score and if this score is greater than
the alignment threshold HA.
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25. Alignment decision
• If O(Ti,Tj) = O(Ti,Tj), then BLOOMS+ sets
– C owl:equivalentClass D.
• If O(Ti,Tj) <O(Tj,,Ti), then BLOOMS+ sets
– C rdfs:subClassOf D. –
• Otherwise, BLOOMS+ sets D rdfs:subClassOf C.
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26. Results BLOOMS+
26

27. Outline
• Introduction
• Background
• Challenges
• Existing Approaches
• BLOOMS+ Approach
• Conclusion & Future Work
• References
27

28. Conclusion
• We have presented a system called BLOOMS+ for performing
ontology alignment using contextual information.
• BLOOMS+ has been evaluated on alignment of three different
LOD ontologies to PROTON, created manually by human experts
for real world application called FactForge.
• To the best of our knowledge, BLOOMS+ is the only system
which utilizes contextual information present in ontology and
Wikipedia category hierarchy for ontology matching.
• BLOOMS+ significantly outperforms state of the art solutions for
the task of ontology alignment.
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29. Future Work
• Extended BLOOMS to utilize contextual information available on
community generated data.
• New weighting mechanism for identifying matches between the
concepts in the dataset.
• Develop a polling mechanism for identifying the best source to
assist in the process of schema alignment.
• Allow seamless querying across datasets by utilizing the
generated alignments (preliminary work LOQUS).
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30. References
• PrateekJain,Peter Z. Yeh, KunalVerma, Reymonrod Vasquez, Mariana
Damova, Pascal Hitzler and Amit P. Sheth, “Contextual Ontology Alignment
of LOD with an Upper Ontology: A Case Study with Proton”. Proceedings of
the 8th Extended Semantic Web Conference 2011, volume 6643 of Lecture
Notes in Computer Science, Heidelberg, 2011. Springer Berlin
• Prateek Jain, Pascal Hitzler, Amit P. Sheth, KunalVerma, Peter Z. Yeh:
Ontology Alignment for Linked Open Data. Proceedings of the 9th
International Semantic Web Conference 2010, Shanghai, China, November
7th-11th, 2010. Pages 402-417.
• Prateek Jain, Pascal Hitzler, Peter Z. Yeh, KunalVerma, and AmitP.Sheth,
Linked Data Is Merely More Data. In: Dan Brickley, Vinay K. Chaudhri, Harry
Halpin, and Deborah McGuinness: Linked Data Meets Artificial Intelligence.
Technical Report SS-10-07, AAAI Press, Menlo Park, California, 2010, pp.
82-86. ISBN 978-1-57735-461-1.
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31. Thank You!
Questions?