Abstract
This chapter presents background on SRL models on which our work is based on. We start with a brief technical background on first-order logic and graphical models. In Sect. 2.2, we present an overview of SRL models followed by details on two popular SRL models. We then present the learning challenges in these models and the approaches taken to solve them in literature. In Sect. 2.3.3, we present functional-gradient boosting, an ensemble approach, which forms the basis of our learning approaches. Finally, We present details about the evaluation metrics and datasets we used.
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Notes
- 1.
Ensemble methods learn multiple models instead of one Bishop (2006).
- 2.
We assume a finite set of constants throughout this document.
- 3.
The Markov blanket of a node x i is all the direct neighbors of x i in the ground Markov network.
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Natarajan, S., Kersting, K., Khot, T., Shavlik, J. (2014). Statistical Relational Learning. In: Boosted Statistical Relational Learners. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-13644-8_2
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DOI: https://doi.org/10.1007/978-3-319-13644-8_2
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