Simon Walk's talk at CIKM '14 about our paper titled "Sequential Action Patterns in Collaborative Ontology Engineering Projects: A Case-study in the Biomedical Domain"

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S C I E N C E  P A S S I O N  T E C H N O L O G Y
Sequential Action Patterns in
Collaborative Ontology-Engineering Projects:
A Case-Study in the Biomedical Domain
Simon Walk1, Philipp Singer2 and Markus Strohmaier2,3
1 Graz University of Technology
2 Gesis – Leibniz Institute for the Social Sciences
3 University of Koblenz
u Graz University of Technology CIKM2014

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Introduction & Motivation
The importance of collaborative ontology-engineering
projects increased over recent years due to an
increase in
• complexity of the modeled domains
• requirements for the resulting ontology
No individual is able to single-handedly cover the increased
complexity and requirements.
Hence, it is crucial to better understand and steer the
underlying processes of how users collaboratively
work on an ontology (i.e., via predictive models).
u Graz University of Technology CIKM2014

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Approach & Objective
To that extend we analyzed five collaborative ontology-engineering
projects from the biomedical domain to:
1. explore regularities and common patterns in user
action sequences
2. fit and select models using Markov chains of
varying order
3. predict user actions via the fitted Markov chains
Our main objective is to predict future user actions
in collaborative ontology-engineering projects.
u Graz University of Technology CIKM2014

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Datasets
Five collaborative ontology-engineering projects from
the biomedical domain with varying sizes of features.
Note that all ontologies were created with WebProtégé
or derivatives of WebProtégé!
u Graz University of Technology CIKM2014

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Types of Action Paths
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Types of Action Paths
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Types of Action Paths
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Types of Action Paths
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Extracted Action Paths
1. Users for Classes
 Sequences of users that changed a class.
2. Change Types for Users & Classes
 Sequences of change types performed by a user / on
a class.
3. Properties for Users & Classes
 Sequences of properties changed by a user / for a
class.
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Exploring Regularities and
Sequential Patterns
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Exploring Regularities
Randomness & Regularities
Wald-Wolfowitz runs test
 Adapted by O’Brien and Dyck (1985)
 For ~60% of our paths, regularities could be detected.1
Sequential Pattern Mining
PrefixSpan to investigate commonly used sequential
patterns.
Only immediately succeeding states build patterns.
 E.g., “A B C” contains “A B” and “B C” but not “A C”
1https://github.com/psinger/RunsTest
u Graz University of Technology CIKM2014

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Results for the Sequential Pattern Analysis
Users for Classes Paths
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Results for the Sequential Pattern Analysis
Users for Classes Paths
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Model Fitting & Selection
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Modeling Fitting
 Markov chains are stochastic processes
representing transition probabilities between
a countable number of known states.
 A state space: listing all possible states
 A transition matrix: listing all transition-probabilities
between states
 A Markov chain of n-th order means that n previous
states contain predictive information about the next
state.
u Graz University of Technology CIKM2014

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Modeling Fitting & Selection
We fitted Models from orders of zero to five.2
 Lower order models are nested within higher order
models.
 Higher orders need exponentially more parameters
and may result in overfitting.
Bayesian model selection (Singer et al. 2014)2
 Higher order models receive a penalty due to higher
complexity.
2 https://github.com/psinger/PathTools
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Results Bayesian Model Selection
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Predicting User Actions
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K-Fold Cross-Fold Prediction Experiment
1. Fit Markov chain model.
 Split Paths into training and test set (stratified).
 Rank transitions for each row in the transition matrix.
1. Determine position of test set transition in the fitted
Markov chain model.
1. Calculate average over all positions.
 Average Position of 1 equals best prediction
accuracy.
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K-Fold Cross-Fold Prediction Results
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Results for the Prediction Task
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Conclusions
 A number of sequences were produced in a non-random
way and frequent patterns can be extracted.
 Memory effects (serial dependence) can increase
prediction accuracy.
 The resulting prediction models can (potentially) be
used for
 the creation of various recommendations as well as
 to assess the impact of potential changes on the
ontology and the community.
u Graz University of Technology CIKM2014

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Future Work
 Include additional data sources (e.g., Semantic
MediaWikis).
 Analyze higher order patterns and compare patterns
of different data sources
 Conduct live-lab experiments with generated
prediction-models (recommendations).
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Questions?
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Thank you for your attention!
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References
Wald and J. Wolfowitz. On a test whether two samples are from
the same population. The Annals of Mathematical Statistics,
11(2):147–162, 1940.
P. C. O’Brien and P. J. Dyck. A runs test based on run lengths.
Biometrics, pages 237–244, 1985.
P. Singer, D. Helic, B. Taraghi, and M. Strohmaier. Detecting
memory and structure in human navigation patterns using
markov chain models of varying order. PloS one,
9(7):e102070, 2014.
u Graz University of Technology CIKM2014