Feasting on Brains!

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Feasting on Brains! From Web Services to Web 2.0
to the Semantic Web and back again A
personal journey through the Semantic Web and Web
Services for Health Care and Life Sciences Mark
Wilkinson (markw_at_illuminae.com) Assistant
Professor, Medical Genetics University of British
Columbia Heart and Lung Research Institute at St.
Pauls Hospital
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Benjamin Good(Hes a Creep!)
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approach
Bioinformatics is a broad fieldand suffers
SEVERE interoperability problems
Bioinformaticians tend to be specialists in a
particular domain of computational analysis
As a group, the brains of all bioinformaticians
Contain all (known) bioinformatics
Is it possible to extract the knowledge Required
for interoperability from the brains of
bioinformaticians en masse?
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Human Computation (luis von Ahn)
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Ontology Spectrum
Thesauri narrower term relation
Selected Logical Constraints (disjointness,
inverse, )
Frames (properties)
Formal is-a
Catalog/ ID
Informal is-a
Formal instance
General Logical constraints
Terms/ glossary
Value Restrs.
Originally from AAAI 1999- Ontologies Panel by
Gruninger, Lehmann, McGuinness, Uschold, Welty
updated by McGuinness. Description in
www.ksl.stanford.edu/people/dlm/papers/ontologies-
come-of-age-abstract.html
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An ontology is a representation of knowledge
Animal
has
Mammal
Hair
Primate
is_a
Lemur
Human
Zombie
eats
Brains
Chips
Shoots
Classes, instances properties, relationships
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Classes
Animal
Mammal
Hair
Primate
Lemur
Human
Zombie
Brains
Chips
Shoots
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instances
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Properties
has
is_a
eats
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relations
has
is_a
eats
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An ontology is a representation of knowledge
Animal
has
Mammal
Hair
Primate
is_a
Lemur
Human
Zombie
eats
Brains
Chips
Shoots
Classes, instances properties, relationships
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Web Service?

• A software tool that is accessible over the Web
• Web Services are intended to be accessed by
  machines, not people.

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Interoperability?

• The ability of two Web Services to exchange
  information, and use that information correctly
• This generally requires Semantics in the form of
  Ontologies

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Mmmm Brains!!

• BioMoby
• Eating brains to enable Web Service
  Interoperability

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What does BioMoby do?
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• Create an ontology of bioinformatics data-types
• Define an ontology of bioinformatics operations
• Open these ontologies for community input
• Define Web Services v.v. these two ontologies
• A Machine can find an appropriate service
• A Machine can execute that service unattended
• Ontology is community-extensible

The BioMoby Plan
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Overview of BioMoby Semantic Interoperability
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Why couldnt we do this before?
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Interoperability is HARD!
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Interoperability throughHuman Computation

• BioMoby Data Type Ontology An explicit list of
  all biological data-types, and the relationships
  between them.
• Ontology built, brain by brain, by
  informaticians!
• We achieve interoperability simply because
  informaticians donate their brain-power
• HUMAN COMPUTATION

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A portion of the BioMoby Ontology built from
the brains of the community!
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so what can I do with it?
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Analytical workflow Discovery

• No explicit coordination between providers
• Run-time discovery of appropriate tools
• Automated execution of those tools
• The machine understands the data you have
  in-hand, and assists you in choosing the next
  step in your analysis.

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Interoperability throughHuman Computation

• Individuals contributed their knowledge about
  bioinformatics data-types to a central ontology
• Their combined knowledge enabled the construction
  of an interoperable framework

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• Who uses BioMoby?

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Usage Statistics

• 15 Nations
• gt 60 independent institutions
• gt1600 interoperable Bioinformatics Resources
• 500,000 requests for brokering each month

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What have we learned?

• We can consume
• the brains of a large community
• to generate something complex, yet organized

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Open Kimono

• The BioMoby ontology is actually quite messy
• communal brains can build useful ontologies, but
  the problem is

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Ontologies are HARD!
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How are ontologies usually constructed?
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By small, hard-working, dedicated groups with
lots of money!

• Gene Ontology code
• Curated 5 full-time staff
• 25 Million (Lewis,S personal communication)
• NCI Metathesaurus code
• Curated 12 full-time staff
• 15 Million (Peter K. , estimate)
• Health Level 7 (HL7)
• Curated
• Lots Some claim as much as 15 Billion
  (Smith, Barry, KBB Workshop, Montreal, 2005)

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• To build the global Semantic Web for Systems
  Biology we need to encode knowledge from EVERY
  domain of biology from barley root apex
  structure and function, to HIV clinical-trials
  outcomes and this knowledge is constantly
  changing!
• At gt15M each, can we afford the Semantic Web???

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Mmmm Need MORE Brains!!

• iCAPTURer
• experiment

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Dr. Bruce McManus with a human heart in his
hands He knows his hearts but he doesntknow
how to buildan ontology
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What we need
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The Problem
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The Solution?
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The Solution?
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So how do we do it?
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Remember what we learnedfrom Moby communities
CAN build ontologies!
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Building Systems BiologyOntologies through
Human Computation
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iCAPTURer

• Benjamin Good
• Ph.D. Student, UBC Bioinformatics
• Genome BC Better Biomarkers in Transplantation
  project, St. Pauls Hospital iCAPTURE Centre

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Old Way

• KE drills the brain of one or a very few
  experts.
• Painful, expensive, and time-consuming

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New Way? the iCAPTURer

• KE creates a clever interface
• No direct interaction with expert
• Thousands of experts
• Cheap Cheap Cheap!

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iCAPTURer 1.0

• Go to a scientific conference
• Text-mine conference abstracts
• Auto-Extract concepts
• Put concepts into a series ofquestion
  templates
• a web interface presents questions about these
  concepts to conference attendees
• Give points for every question they answer
• Give a prize to the highest point winner

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Results

• Is _____ a meaningful term?
• Yes, No, I dont know buttons
• What is a synonym for ______
• Text entry box
• Where does _____ fit in the following tree of
  related terms?
• Clickable tree

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Observations

• Yes/No questions work well
• Text entry is less effective
• Adding to a tree is a disaster!
• Competition is a great motivatorfor human
  computation!

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COST?
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COST?
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COST?
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COST?
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COST?
lt 15,000,000
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iCAPTURer 1.5
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Start with hypothetical concept tree Put
concepts-concept relations into a series of
true/false questions Make a web interface to
ask questions If a relationship is false, then
re-start at the root of the concept tree Give
points for every question they answer Give a
prize to the highest point winner
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Chatterbot

• Ive heard that a cardiac myocyte is a type of
  cardiac cell. Is this true?
• Ive heard that STEMI means the same thing as ST
  Elevated Myocardial Infarction. Is that
  nonsense, or is it correct?
• How do you feel about your mother?

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Results

• Knowledge capture in 3 days
• gt11,000 Concepts

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COST
0
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Full details of this experiment are available
in Proceedings of the Pacific Symposium on
Biocomputing, 2006
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Ontology Quality?
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Potential Ontology Evaluation Metrics

• Manual, subjective
• Auto, questionable value
• Auto, useful, not enough
• Auto, dependent on NLP
• Auto/Manual gold standard must exist!
• Optimal! Auto/Manual, but not generalizable

• Domain independent
• philosophical desiderata
• graphical structure
• satisfiability
• Domain specific
• Fit to text
• Similarity to a gold standard
• Task-based

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Good???
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What do we mean by Good?
Ontology construction is motivated by the goal
of alignment not on concepts but on the
universals in reality and thereby also on the
corresponding instances - Barry
Smith Reality should be the benchmark for the
goodness of an ontology
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ontology evaluation based on referents in
reality
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Chosen Philosophical PrincipleEpistemology
Precedes Ontology

• A Class should refer to an invariant pattern of
  properties common among all its instances
• Mammals have mammary glands and hair
• Humans are an instance of the class Mammal
• Therefore
• If class-instances are mapped into an ontology
• Each instance has properties or qualities
• These properties or qualities SHOULD segregate
  into different classes if the ontology is any good

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Philosophical Desiderata

• Non-vagueness
• at least one instance can exist with the Class
  pattern
• Vague class mammalian cell wall
• Non-ambiguity
• no more than one common pattern per Class
• Ambiguous class cell (e.g. cell phone, jail
  cell)
• Non-redundancy
• within the same level of granularity, no other
  class refers to same common properties
• Redundant classes human, homo sapiens

Cimino, J, 1998
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Realist Evaluation Step 1Table of
Instance-Properties
Instance Char1 Char2 Class B?
I.1 Y N Y
I.2 Y Y Y
I.3 N N N
I.4 N Y N
... ... ... ...
A
C
B
I.1
I.3
I.2
I.4
(Test one class at a time)
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Realist Evaluation Step 2Machine Learning
Instance Char1 Char2 Class B?
I.1 Y N Y
I.2 Y Y Y
I.3 N N N
I.4 N Y N
... ... ... ...
If char1 Y Then Class X 100
Pattern
Class B score for this pattern
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WEKA

• Produced by Waikato University in New Zealand
• An open source library containing implementations
  of hundreds of machine learning algorithms
• (rule learners, LDA, SVM, neural networks... )

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Realist Evaluation
0.35
0.1
0.92
Instance Char1 Char2 Class 1?
I.1 Y N Y
I.2 Y Y Y
I.3 N N N
I.4 N Y N
... ... ... ...
Class Score for Each Class
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Realist Evaluation - positive control

1. Identify an ontology that already has logical
   constraints on properties of a classes.
2. Assemble instances that have those properties
3. Classify the instances with a reasoner
4. Remove class restrictions from the ontology, but
   keep instances assigned to their classes
5. Look for patterns of instance properties
6. If successful, patterns should be detected
7. The higher the pattern score, the gooder the
   ontology is

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Positive Control Phosphabase

• An ontology describing different classes of
  phosphatase enzymes.
• Given the domain composition of a protein,
  phosphatase class can be inferred automatically.

Wolstencraft et al (2006) Protein classification
using ontology classification Bioinformatics.
Vol. 22 no. 14, pages 530538
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Remove the Logical Rules

• Remove the defining rules for each class
• Maintain the classified instances
• Execute the realist evaluation
• Can we re-discover the patterns that the logical
  class-rules used to dictate?

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Realist Evaluation Positive Control

• 25 classes from phosphabase tested on 700
  simulated protein instances
• 21 - pattern correctly identified for 100 of
  instances
• For 4 others, patterns identified covering 99,
  92, 82, 82 of instances respectively.

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Realist Evaluation Positive Control

• So the Phosphabase ontology is good
• We can detect strong patterns of properties in
  its instances that follow the philosophical
  desiderata
• This is unsurprising, since we knew that it was
  good in the first place

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Evaluation of Gene Ontologyis ongoing
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Interesting side effect

• Class-defining rules are generated by the realist
  evaluation
• Most existing bio-ontologies lack formal
  class-definitions
• This evaluation could be used to create such
  rules ? automatic classifiers
• Can also detect what TYPE of property is best
  classified by current bio-ontologies

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Is Realist Evaluation a Valid metric?

• the realist evaluation measures the success of an
  ontology in classifying a specific set of
  properties
• We claim that this is a metric relating to the
  quality of that ontology
• Is this metric any better than other metric like
  graph complexity, or fit-to-text?

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Evaluatingmetrics
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OntoLoki Making mischief with Ontologies

1. Take an ontology that we claim is good
2. Make it worse by mischievously adding changes
3. Measure the degree of mischief
4. Run the evaluation metric of interest
5. ? Metric score should correlate with the amount
   of mischief added

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Comparison of ontology quality metrics
Measured Ontology Quality
Amount of noise added (ontology quality
decreasing)
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Is Reality Evaluation a good metric?
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Lets OntoLoki it to find out!
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OntoLoki test of Realist Evaluation Metric
Average Class Score
Noise Added (a measure of nodes affected)
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Conclusion

• Human computation can collect significant amounts
  of knowledge in an organized way

OntoLoki seems to be effective atevaluating
the evaluation metrics
Reality evaluation is an interesting new
metric for testing ontologies
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Subjective iCAPTURer Observations

• Humans had an EXTREMELY difficult time
  classifying concepts into pre-existing categories
• Humans had an EXTREMELY difficult time defining
  new categories and placing them into the existing
  classification system

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Classification is HARD!
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Abandoning Classification
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(briefly)
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An ontology is a representation of knowledge
Animal
has
Mammal
Hair
Primate
is_a
Lemur
Human
Gorilla
Classes, instances properties, relationships
has_size
Big
Medium
Small
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AN ontology is ONE representation of knowledge
Animal
has
Mammal
Hair
Primate
is_a
Lemur
Human
Gorilla
Ontology of Anatomy
has_size
Big
Medium
Small
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AN ontology is ONE representation of knowledge
Animal
lives
African_animal
Africa
Southern_African_animal
is_a
Ontology of Habitat Also might want Odour,
digits, bone density, friendliness, cuteness..
Aquatic
plains
mountain
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Clay Shirky Ontology is Overrated

• Attempts to predict the future
• Soviet Union used to be a category in the
  Library of Congress
• Attempts mind-reading
• Size, location, odour.. Authors must predict what
  users are interested in
• Great minds dont think alike..
• No two people are likely to create the same
  ontology

http//www.shirky.com/writings/ontology_overrated.
html
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Categories
Properties
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BRAINS!! MORE BRAINS!!

• Mass Collaborative Tagging

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Mass Open Social Tagging

• A rapidly growing trend on the Web
• Unstructured
• Mass-collaboration
• Anyone can say anything about anything using any
  words they wish

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Connotea Scientific Tagging(Connotea is a
product of Nature Publishing Group)
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Connotea Growth
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Tagging is EASY!
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The Tagged World

• Tagging is easy!
• Tagging costs nothing
• Tagging empowers all viewpoints
• Tagging is happening!!!!!!

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Lexical Comparison of Tagging with Formal
Indexing Systemsand Ontologies
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Ontology (FMA)
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Ontology (GO Molecular Function)
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Ontology (GO Biological Process)
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Tagging (Bibsonomy)
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Tagging (CiteULike)
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Tagging (Connotea)
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Ontologies and Folksonomies are fundamentally
different!
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Problem??

• Folksonomies and ontologies are fundamentally
  different!
• It may not be possible to derive one from the
  other accurately
• Nevertheless, we would like to take advantage of
  tagging behaviour while gaining the power of
  controlled vocabularies/Ontologies

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E.D.The Entity Desciber
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Connotea tagging
User types in all tags
Type-ahead displays previously used tags
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Connotea E.D. Tagging
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Leveraging Tagging?

• Tagging effectively assigns properties to
  entities
• ED Tagging constrains those properties to a
  controlled vocabulary or ontology
• Can we discover patterns in those properties that
  indicate a natural classification system?
• Can a realist-evaluation generate logical rules
  that define classes based on patterns of tags?

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Final Thoughts

• Ontologies are important, but hard to build
• iCAPTURer formal, template-based, cost-free
  consumption of biologists brains seems to work!
• Informal annotation (tagging) is cheap, easy,
  and scalable,
• and is HAPPENING
• Can we leverage tagging to create ontology-like
  structures? Maybe Maybe not!

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My journey back to Web Services
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Why do I care about WS so passionately?
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The Deep Web

• All the data and knowledge only accessible
  through Web Forms
• Estimated to be orders of magnitude greater than
  the surface Web- 91,000 Terabytes in the deep
  Web- 167 Terabytes in the Surface Web
• Much of the Deep Web CANNOT be represented on the
  Semantic Web since it DOES NOT EXIST until the
  Web Form is accessed

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Moby 2.0 and CardioSHARE Merging the Deep
Weband the Semantic Web
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What Web Services do
BLAST SERVICE
Sequence Data
Blast Hit
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What BioMoby does
??
Sequence Data
Want Blast
MOBY BLAST SERVICE
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The implied relationship between input and output
Sequence Data
Blast Hit
givesBlastResult
Not Bologically Meaningful
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The implied biological relationship between input
and output
hasHomologyTo
Sequence Data
Blast Hit
looks a lot like the RDF statement
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To merge Web Servicesand the Semantic
WebSimply assertthe relationshipand let
Moby do the rest!
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Start with a partial Triple
URI rdftype Sequence
hasHomologyTo
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What Moby 2.0 Does
??
URI rdftypeSequence
hasHomologyTo
MOBY BLAST SERVICE
Moby 2.0 hasHomologyTo property provided
byBLAST services
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Moby 2.0 Query

• FIND SERVICES THAT

Consume Sequence Data Provide hasHomologyTo
Property Attached to other Sequence Data
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Moby 2.0 extends SPARQL

• SPARQL queries contain concepts and relationships
  of interest
• Map RDF predicates onto Moby services capable of
  generating them
• Registry query What Moby service consumes
  subject and generates the predicate
  relationship type?

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But wait, theres more!
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CardioSHARE Exploit knowledge in OWL-DL
ontologies to enhance query
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CardioSHARE Exploit knowledge in OWL-DL
ontologies to enhance query
This SPARQL query could be posed on a database
of RAW, UNANNOTATED Protein sequences, and be
answered by Moby 2.0
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What do Moby 2.0 and CardioSHARE achieve?

• Makes the Deep Web transparently accessible as if
  it were a Semantic Web Resource
• Allows SPARQL to do truly semantic queries!
• Reduces the requirement of Biologists to know
  how/where to get their data of interest
• Simplifies construction of complex analytical
  pipelines by automating much of the
  discovery/execution tasking

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Ontology Spectrum
Thesauri narrower term relation
Selected Logical Constraints (disjointness,
inverse, )
Frames (properties)
Formal is-a
Catalog/ ID
Informal is-a
Formal instance
General Logical constraints
Terms/ glossary
Value Restrs.
Originally from AAAI 1999- Ontologies Panel by
Gruninger, Lehmann, McGuinness, Uschold, Welty
updated by McGuinness. Description in
www.ksl.stanford.edu/people/dlm/papers/ontologies-
come-of-age-abstract.html
149
Fin