Presentation by Jaimie Murdock, Cameron Buckner and Colin Allen at PhiloWeb 2012 (WWW 2012), Lyon, France.

1. Containing the Semantic Explosion
Jaimie Murdock
Cameron Buckner
Colin Allen
Indiana Philosophy Ontology (InPhO) Project
Indiana University – Bloomington, IN, USA
inpho@indiana.edu
17 April 2012
Murdock, Buckner, Allen (IU) Containing the Semantic Explosion 17 April 2012 1 / 33

2. Introduction
Table of Contents
1 Introduction
2 Building Ontologies
3 Dynamic Ontology
4 Future Directions
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3. Introduction
The Information Explosion
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4. Introduction
The Information Explosion
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5. Introduction
Guiding Principles
“Augmented intelligence”
Let humans and computers each do
only what they do best
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6. Introduction
Guiding Principles
“Augmented intelligence”
Let humans and computers each do
only what they do best
“Guided serendipity”
Provide useful tools for scholars:
cross-referencing
semantic search
document classification
visualizations
Murdock, Buckner, Allen (IU) Containing the Semantic Explosion 17 April 2012 5 / 33

7. Introduction
What Philosophers Know
Stanford Encyclopedia of
Philosophy
http://plato.stanford.edu
1522 authors
127 subject editors
1315 articles since 14 Sep 1995
1 million weekly article
downloads
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8. Introduction
What Philosophers DON’T Know
What’s in the SEP!
http://plato.stanford.edu
16.3 million words — equivalent to
27,250 pages at 600 words/page
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9. Introduction
The Indiana Philosophy Ontology (InPhO) Project
https://inpho.cogs.indiana.edu/
Pragmatic attempt to organize the discipline of philosophy through a three
step process of:
data mining
expert verification
machine reasoning
Murdock, Buckner, Allen (IU) Containing the Semantic Explosion 17 April 2012 8 / 33

10. Introduction
The Indiana Philosophy Ontology (InPhO) Project
https://inpho.cogs.indiana.edu/
Murdock, Buckner, Allen (IU) Containing the Semantic Explosion 17 April 2012 9 / 33

11. Introduction
The Indiana Philosophy Ontology (InPhO) Project
https://inpho.cogs.indiana.edu/
Murdock, Buckner, Allen (IU) Containing the Semantic Explosion 17 April 2012 10 / 33

12. Building Ontologies
Table of Contents
1 Introduction
2 Building Ontologies
3 Dynamic Ontology
4 Future Directions
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13. Building Ontologies
Computational Ontology
“A body of formally represented knowledge is based on a
conceptualization: the objects, concepts, and other entities that are
presumed to exist in some area of interest and the relationships that hold
among them . . . A conceptualization is an abstract, simplified view of the
world that we wish to represent for some purpose . . . An ontology is an
explicit specification of a conceptualization.” — Gruber (1993)
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14. Building Ontologies
Domain Ontologies
GOLD: General Ontology for Linguistic Description
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15. Building Ontologies
Domain Ontologies
Gene Ontology
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16. Building Ontologies
Formal Ontology
Goals: interoperability, permanence, precision
ULOs represent the most basic, enduring features of reality — not
just a “conceptualization”
Upper-level ontologies (ULOs) unite domain-level ontologies
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17. Building Ontologies
Formal Ontology
Concerns
“Double experts” required
Manual design and change
Bias, controversy
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18. Building Ontologies
Formal Ontology
Concerns
“Double experts” required
Manual design and change
Bias, controversy
Battlefield cluttered with ULOs . . .
BFO, Dolce, DnS, SUMO, Cyc, GFO, IDEAS, GOL, BWW, COSMO,
OCHRE, Gist, OBO, PROTON, IFF, MSO, Sowas, . . .
In civil war, population suffers most . . .
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19. Building Ontologies
Folksonomy
Can we induce an ontology from tagging behavior?
“Wisdom of the crowds”
Del.ico.us, Flickr, Wikipedia
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20. Building Ontologies
Folksonomy Concerns
Uncontrolled vocabulary
Ambiguity, idiosyncrasy, no definitions
Tagging behavior changes over time
Unstructured or training required
The world is not flat — expertise matters!
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21. Building Ontologies
Formal vs. Dynamic Ontology
Formal Ontology Dynamic Ontology
Large projects in business, Smaller, open-access projects in
medicine, and natural sciences fluid domains
Stable consensus Emergent consensus
Manual construction Semi-automated construction
Monolithic perspective Multiple perspectives
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22. Building Ontologies
Formal vs. Dynamic Ontology
Formal Ontology Dynamic Ontology
Large projects in business, Smaller, open-access projects in
medicine, and natural sciences fluid domains
Stable consensus Emergent consensus
Manual construction Semi-automated construction
Monolithic perspective Multiple perspectives
Wish: Top-down quality for a bottom-up price!
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23. Dynamic Ontology
Table of Contents
1 Introduction
2 Building Ontologies
3 Dynamic Ontology
4 Future Directions
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24. Dynamic Ontology
The Indiana Philosophy Ontology (InPhO) Project
https://inpho.cogs.indiana.edu/
Pragmatic attempt to organize the discipline of philosophy through a three
step process of:
data mining
expert verification
machine reasoning
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25. Dynamic Ontology
1. Data Mining
Use statistical techniques to make predictions about content
J-measure (n-grams)
Measures similarity using
co-occurrences
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26. Dynamic Ontology
1. Data Mining
Use statistical techniques to make predictions about content
J-measure (n-grams) Entropy (TF-IDF)
Measures similarity using Measures generality based on
co-occurrences prevalence
Further details: Niepert et al. 2007, Murdock et al. 2012
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27. Dynamic Ontology
2. Expert Verification
Present hypothetical relations to users.
Allows for untrained ontologists.
Amazon Mechanical Turk
Further details: Allen et al. 2008, Niepert et al. 2009, Buckner et al. 2010, Eckart et al. 2010
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28. Dynamic Ontology
3. Machine Reasoning
Input – Expert feedback combined with statistical data
Output – Populated ontology with taxonomic projection
Further details: Niepert et al. 2008
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29. Dynamic Ontology
Applications
Automated cross-referencing
Context-aware search
Citation management
Document classification
Visualizations
Change and controversy
discovery
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30. Dynamic Ontology
Applications
Automated cross-referencing
Context-aware search
Citation management
Document classification
Visualizations
Change and controversy
discovery
“Macroscopes” (B¨rner)
o
Metaphilosophy
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31. Future Directions
Table of Contents
1 Introduction
2 Building Ontologies
3 Dynamic Ontology
4 Future Directions
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32. Future Directions
CodEx Workbench
Dynamic ontology toolkit for arbitrary corpora
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33. Future Directions
CodEx Workbench
Murdock, Buckner, Allen (IU) Containing the Semantic Explosion 17 April 2012 28 / 33

34. Future Directions
CodEx Workbench
Murdock, Buckner, Allen (IU) Containing the Semantic Explosion 17 April 2012 29 / 33

35. Future Directions
CodEx Workbench
Murdock, Buckner, Allen (IU) Containing the Semantic Explosion 17 April 2012 30 / 33

36. Future Directions
Guiding Principles
“Augmented intelligence”
Let humans and computers each do
only what they do best.
Murdock, Buckner, Allen (IU) Containing the Semantic Explosion 17 April 2012 31 / 33

37. Future Directions
Guiding Principles
“Augmented intelligence”
Let humans and computers each do
only what they do best.
“Guided serendipity”
Provide useful tools for scholars:
cross-referencing
semantic search
document classification
visualizations
Murdock, Buckner, Allen (IU) Containing the Semantic Explosion 17 April 2012 31 / 33

38. Future Directions
Acknowledgements
IUB New Frontiers in the Humanities
NEH Digital Humanities Initiative
NEH-DFG Bilateral Digital Humanities Program
Digging into Data Challenge
Alexander von Humboldt Foundation
Views expressed are the authors, and not necessarily the views of any funding angencies
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39. Future Directions
Acknowledgements
Project Director Project Staff
Colin Allen Alex Frost, Wesley Pettyjohn, Sam
Waggoner
Project Founders
Cameron Buckner, Mathias Niepert CogSci Support
Ruth Eberle, Nubli Kasa
Graduate Students
Jaimie Murdock, Robert Rose Application Consultants
Ed Zalta & Uri Nodelman (SEP),
Former Staff Tony Beavers (Noesis)
Evan Boggs, Tarun Gangwani, Scott
Weingart
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