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u Graz University of Technology CIKM2014

S C I E N C E P A S S I O N T E C H N O L O G Y

u Graz University of Technology CIKM2014

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 Technology2 Gesis – Leibniz Institute for the Social Sciences3 University of Koblenz

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2 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).

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3 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.

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4 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!

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5 Types of Action Paths

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6 Types of Action Paths

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7 Types of Action Paths

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8 Types of Action Paths

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9 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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10

Exploring Regularities and

Sequential Patterns

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11 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

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12 Results for the Sequential Pattern Analysis

Users for Classes Paths

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13 Results for the Sequential Pattern Analysis

Users for Classes Paths

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14

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.

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16 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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17 Results Bayesian Model Selection

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18

Predicting User Actions

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19 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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20 K-Fold Cross-Fold Prediction Results

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21 Results for the Prediction Task

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22 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.

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23 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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24

Questions?

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Thank you for your attention!

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26 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.