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Fig. 16.9 Merging ingredient subgraphs to create a work plan
ingredient-specific, two ingredients of the same type will not necessarily be assigned
the same subgraph.
In the second step, represented in Fig. 16.9 , we proceed with merging all the
selected ingredient subgraph clusters together. Our approach is a variation on themin-
imum common supergraph computation [ 30 ], with differences in where the merging
occurs, what is being merged, and in what order.
Our task ontology contains a limited number of verbs that can accept more than
one input—mainly “add” and “mix”. Therefore, these are the only vertices where
the merging can occur. Another constraint inherent to recipes is that the graph must
end with one single output (even if the last instruction indicates serving several dish
elements together).
As we are dealing with clusters of subgraphs, a representative subgraph must be
picked within each cluster. The selection can take into account the weight of each
subgraph and its distance to the other members of the clusters.
The recipe graph is built by merging the representative ingredient subgraphs one
at a time, with the minimum common supergraph being obtained by choosing the
highest possible merge point in the action sequence. In the example in Fig. 16.9 ,the
subgraphs for ingredients 1 and 2 are merged on action 1 rather than action 4. There
may be cases when a subgraph cannot be merged, because of a lack of a suitable
merge point. One could conceivably insert an “add” action anywhere in a subgraph,
since one can always combine two ingredient mixtures together, even though the
result is not guaranteed. Another, more preferable option, is to reconsider the choice
of the representative ingredient subgraph, or of the ingredient subgraph itself.
As representative ingredient subgraphs are reconsidered, the order of the ingre-
dients being merged becomes important. Indeed, not all ingredient types are deter-
minant to shape the recipe outline. Herbs and spices can be added to a dish at many
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