Semantic modelling of animal welfare explained – Part 2: The basis of welfare weighting and usage of scientific information
Abstract
Abstract A methodology called semantic modelling can be used to synthesise scientific information to assess farm animal welfare. Different housing systems consist of attributes that can each have an effect on the welfare of the animal. Examples include type of floor, water provision system, and lighting system. Each of those attributes has so-called attribute levels. So, for instance, a water provision system can be drinking nipples, bowls, or troughs. Statements from the scientific literature that report the welfare effects of certain attribute levels serve as the basis for the welfare assessment. A semantic model takes attribute levels describing a housing system as input and generates a weighted overall welfare score as output. Although the procedure used in semantic modelling to decompose scientific statements into welfare-relevant attributes has been applied to multiple contexts, the procedure of information extraction has not been described in sufficient detail to allow new modellers to easily understand the procedure. Hence, the reproducibility of semantic modelling may be at stake despite its potential for integrated animal welfare research. Therefore, the objective of this paper was to provide a set of formalised rules showing how scientific information can be decomposed and weighted using semantic-modelling principles. We describe how to read the scientific literature to select scientific statements and how to decompose them to extract the relevant information for…
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