1. Piotr Kaminski University of Victoria September 24th, 2002
Integrating Information on the Semantic Web Using Partially Ordered Multi Hypersets
Piotr Kaminski
University of Victoria
September 24th, 2002

2. This is not an Outline Outline Motivation Approach Solution
Evaluation and Contributions
Future work
The Semantic Web as Hyper-pomsets

3. The Semantic Web
Getting machines to understand the contents of the web
I'd better talk to the bank before I spend it, because of what has happened to other people
Nobody owes me that much money
wisdom
actions
Agents
Your bank balance has jumped 8087% to $
knowledge
reasoning
Semantic Web
World Wide Web is only understandable by humans; for computers, it's just data
shades of AI, but emphasis is on getting useful results
The Semantic Web:
data is structured, making it into information
information can be reasoned upon  knowledge
agents can act on knowledge  wisdom
Why?
can make database-like queries, instead of natural language parsing/grepping
e.g. "Am I allowed to take SEng 330 next term?", "How many train lines in Japan?", "Cheapest price for this list of books, including shipping?", "Doctors in my area specializing in sports injuries with whom I could make a mutually convenient appointment tomorrow?"
information
structure
World Wide Web
data
The Semantic Web as Hyper-pomsets
Information Interchange on the Semantic Web

4. Existing Solutions Document analysis Yahoo! Google Data models
Relational Databases
Extensible Markup Language (XML)
Information models
Resource Description Framework (RDF)
Topic Maps
Syntax
Object
Semantic
Source Level
Target Level ?
The Semantic Web as Hyper-pomsets

5. My Approach Integrate other metamodels object-level mappings
no changes to source metamodels
no loss of information
identify provenance
Flexible and elegant
simple
closed (self-describing)
practical
Not goals: serialization, code efficiency & complexity
The Semantic Web as Hyper-pomsets

6. The Braque Metamodel Primitives: things relationships order
Presents
Piotr
Motivation
Approach
Solution
Evaluation
Contributions
Future Work
Presents
Piotr
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Approach A1 A2 B1 C1 C2
Presents
Piotr
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Approach A1 A2 B1 C1 C2
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Partial Sets
Queries …
Presents
Piotr
Motivation
Solution
Evaluation
Contributions
Approach A1 A2 B1 C1 C2
Future Work
Partial Sets
Queries …
Piotr
Motivation
Approach
Solution
Evaluation
Contributions
Future Work
Motivation
Approach
Solution
Evaluation
Contributions
Future Work
Motivation
Approach
Solution
Evaluation
Contributions
Future Work
Motivation
Approach
Solution
Evaluation
Contributions
Future Work
Partially Ordered
Multi Hypersets
things
sets
total order
links – relationships as things
partial order
infinite
hyper
The Semantic Web as Hyper-pomsets
Information Interchange on the Semantic Web

7. Braque in Action Naïve Upper Ontology type hierarchies, relations
membership reification, roles
names, identifiers
metatype constraint
Integration mappings
XML: ordered elements, qualified names
RDF: types, properties, containers, reification
Topic Maps: identification, classes, scopes, names
The Semantic Web as Hyper-pomsets

8. Evaluation and Contributions
Braque metamodel
collections
explicit identification
infinite reflection depth
no formal interpretation, open vs. closed worlds
Integration
XML, RDF and Topic Maps
complete, automatic, semantically aligned
no schemas, ugly class punning workaround
Proof of concept implementation
The Semantic Web as Hyper-pomsets

9. Future Work Theory mereology, mereotopology, partial sets
formal theory, algebra, and interpretation
query & inference language, schema ontology
Implementation
developer evaluation studies
semantic integration with host language
visualization, distributed computing
Applications
integration: maintain current maps; UML, UDDI
modelling multi-dimensional software
adoption-centric information management
The Semantic Web as Hyper-pomsets

10. Thank You
The Semantic Web as Hyper-pomsets