E N C E P A S S I O N T E C H N O L O G Y 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 Technology 2 Gesis – Leibniz Institute for the Social Sciences 3 University of Koblenz
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).
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.
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.
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
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.
& 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
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.
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.
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).
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.