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A Logic Framework for Incremental Learning of Process Models

Ferilli S., Esposito F.

Fundamenta Informaticae

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2013

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Vol. 128, nr 4

413--443

EN

Standardized processes are important for correctly carrying out activities in an organization. Often the procedures they describe are already in operation, and the need is to understand and formalize them in a model that can support their analysis, replication and enforcement. Manually building these models is complex, costly and error-prone. Hence, the interest in automatically learning them from examples of actual procedures. Desirable options are incrementality in learning and adapting the models, and the ability to express triggers and conditions on the tasks that make up the workflow. This paper proposes a framework based on First-Order Logic that solves many shortcomings of previous approaches to this problem in the literature, allowing to deal with complex domains in a powerful and flexible way. Indeed, First-Order Logic provides a single, comprehensive and expressive representation and manipulation environment for supporting all of the above requirements. A purposely devised experimental evaluation confirms the effectiveness and efficiency of the proposed solution.

2

Relational Feature Mining with Hierarchical Multitask kFOIL

Cilia E., Landwehr N., Passerini A.

Fundamenta Informaticae

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2011

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Vol. 113, nr 2

151-177

EN

We introduce hierarchical kFOIL as a simple extension of the multitask kFOIL learning algorithm. The algorithm first learns a core logic representation common to all tasks, and then refines it by specialization on a per-task basis. The approach can be easily generalized to a deeper hierarchy of tasks. A task clustering algorithm is also proposed in order to automatically generate the task hierarchy. The approach is validated on problems of drug-resistance mutation prediction and protein structural classification. Experimental results show the advantage of the hierarchical version over both single and multi task alternatives and its potential usefulness in providing explanatory features for the domain. Task clustering allows to further improve performance when a deeper hierarchy is considered.

3

Relational Transformation-based Tagging for Activity Recognition

Landwehr N., Gutmann B., Thon I., Raedt L. de, Philipose M.

Fundamenta Informaticae

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2008

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Vol. 89, nr 1

111-129

EN

The ability to recognize human activities from sensory information is essential for developing the next generation of smart devices. Many human activity recognition tasks are - from a machine learning perspective-quite similar to tagging tasks in natural language processing. Motivated by this similarity, we develop a relational transformation-based tagging system based on inductive logic programming principles, which is able to cope with expressive relational representations as well as a background theory. The approach is experimentally evaluated on two activity recognition tasks and an information extraction task, and compared to Hidden Markov Models, one of the most popular and successful approaches for tagging.

4

A Restarted Strategy for Efficient Subsumption Testing

Kuželka O., Železný F.

Fundamenta Informaticae

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2008

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Vol. 89, nr 1

95-109

EN

We study runtime distributions of subsumption testing. On graph data randomly sampled from two different generative models we observe a gradual growth of the tails of the distributions as a function of the problem instance location in the phase transition space. To avoid the heavy tails, we design a randomized restarted subsumption testing algorithm RESUMER2. The algorithm is complete in that it correctly decides both subsumption and non-subsumption in finite time. A basic restarted strategy is augmented by allowing certain communication between odd and even restarts without losing the exponential runtime distribution decay guarantee resulting from mutual independence of restart pairs. We empirically test RESUMER2 against the state-of-the-art subsumption algorithm Django on generated graph data as well as on the predictive toxicology challenge (PTC) data set. RESUMER2 performs comparably with Django for relatively small examples (tens to hundreds of literals), while for further growing example sizes, RESUMER2 becomes vastly superior.

5

Graph-based Relational Learning with Application to Security

Holder L., Cook D., Coble J., Mukherjee M.

Fundamenta Informaticae

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2005

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Vol. 66, nr 1,2

83--101

EN

We describe an approach to learning patterns in relational data represented as a graph. The approach, implemented in the Subdue system, searches for patterns that maximally compress the input graph. Subdue can be used for supervised learning, as well as unsupervised pattern discovery and clustering. We apply Subdue in domains related to homeland security and social network analysis.