containing the semantic explosion

Containing the Semantic Explosion

Jaimie MurdockCameron Buckner

Colin Allen

Indiana Philosophy Ontology (InPhO) ProjectIndiana University – Bloomington, IN, USA

[email protected]

17 April 2012

Murdock, Buckner, Allen (IU) Containing the Semantic Explosion 17 April 2012 1 / 33

Introduction

Table of Contents

1 Introduction

2 Building Ontologies

3 Dynamic Ontology

4 Future Directions


Introduction

The Information Explosion


Introduction

Guiding Principles

“Augmented intelligence”Let humans and computers each doonly what they do best

“Guided serendipity”Provide useful tools for scholars:

cross-referencing

semantic search

document classification

visualizations


Introduction

What Philosophers Know

Stanford Encyclopedia ofPhilosophyhttp://plato.stanford.edu

1522 authors

127 subject editors

1315 articles since 14 Sep 1995

1 million weekly articledownloads


Introduction

What Philosophers DON’T Know

What’s in the SEP!http://plato.stanford.edu

16.3 million words — equivalent to27,250 pages at 600 words/page


Introduction

The Indiana Philosophy Ontology (InPhO) Project

https://inpho.cogs.indiana.edu/

Pragmatic attempt to organize the discipline of philosophy through a threestep process of:

data mining

expert verification

machine reasoning


Introduction




Building Ontologies

Table of Contents

1 Introduction


3 Dynamic Ontology

4 Future Directions


Building Ontologies

Computational Ontology

“A body of formally represented knowledge is based on aconceptualization: the objects, concepts, and other entities that arepresumed to exist in some area of interest and the relationships that holdamong them . . . A conceptualization is an abstract, simplified view of theworld that we wish to represent for some purpose . . . An ontology is anexplicit specification of a conceptualization.” — Gruber (1993)


Building Ontologies

Domain Ontologies

GOLD: General Ontology for Linguistic Description


Building Ontologies

Domain Ontologies

Gene Ontology


Building Ontologies

Formal Ontology

Goals: interoperability, permanence, precision

ULOs represent the most basic, enduring features of reality — notjust a “conceptualization”

Upper-level ontologies (ULOs) unite domain-level ontologies


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 . . .


Building Ontologies

Folksonomy

Can we induce an ontology from tagging behavior?

“Wisdom of the crowds”

Del.ico.us, Flickr, Wikipedia


Building Ontologies

Folksonomy Concerns

Uncontrolled vocabularyAmbiguity, idiosyncrasy, no definitions

Tagging behavior changes over time

Unstructured or training required

The world is not flat — expertise matters!


Building Ontologies

Formal vs. Dynamic Ontology

Formal Ontology

Large projects in business,medicine, and natural sciences

Stable consensus

Manual construction

Monolithic perspective

Dynamic Ontology

Smaller, open-access projects influid domains

Emergent consensus

Semi-automated construction

Multiple perspectives

Wish: Top-down quality for a bottom-up price!


Dynamic Ontology

Table of Contents

1 Introduction


3 Dynamic Ontology

4 Future Directions


Dynamic Ontology



Pragmatic attempt to organize the discipline of philosophy through a threestep process of:

data mining

expert verification

machine reasoning


Dynamic Ontology

1. Data Mining

Use statistical techniques to make predictions about content

J-measure (n-grams)

Measures similarity usingco-occurrences

Entropy (TF-IDF)

Measures generality based onprevalence

Further details: Niepert et al. 2007, Murdock et al. 2012


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


Dynamic Ontology

3. Machine Reasoning

Input – Expert feedback combined with statistical dataOutput – Populated ontology with taxonomic projection

Further details: Niepert et al. 2008


Dynamic Ontology

Applications

Automated cross-referencing

Context-aware search

Citation management

Document classification

Visualizations

Change and controversydiscovery

“Macroscopes” (Borner)

Metaphilosophy


Future Directions

Table of Contents

1 Introduction


3 Dynamic Ontology

4 Future Directions


Future Directions

CodEx Workbench

Dynamic ontology toolkit for arbitrary corpora


Future Directions

CodEx Workbench


Future Directions

Guiding Principles

“Augmented intelligence”Let humans and computers each doonly what they do best.

“Guided serendipity”Provide useful tools for scholars:

cross-referencing

semantic search

document classification

visualizations


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


Future Directions

Acknowledgements

Project Director

Colin Allen

Project Founders

Cameron Buckner, Mathias Niepert

Graduate Students

Jaimie Murdock, Robert Rose

Former Staff

Evan Boggs, Tarun Gangwani, ScottWeingart

Project Staff

Alex Frost, Wesley Pettyjohn, SamWaggoner

CogSci Support

Ruth Eberle, Nubli Kasa

Application Consultants

Ed Zalta & Uri Nodelman (SEP),Tony Beavers (Noesis)


containing the semantic explosion

Education

pragmatic

change bias

make predictions

building ontologiesformal

threestep

access projects

referencing

dynamic ontology1