Fig. 2. Flow diagram detailing the procedure followed to provide insights on what concepts were considered to create the prediction and enrich it with relevant context (media
events and suggested external datasets).
context regarding feature and media events related to them. Another
return abstraction
target audience could be experts who develop demand forecasting
models. They would benefit from the data mentioned above and the
def include_concepts_until_desired_limit(abstractions_set,
sorted_candidate_concepts, n_concepts):
possibility to drill down from described concepts to specific model
idx = 0
features. Such an explanation could be enriched with insights on fre-
while abstractions_set.length() < n_concepts and idx <
quentkeywordsfoundinrelatedeventsreportedinthemediaandopen
sorted_candidate_concepts.length():
datasetsthatmaybeusefultothemodel.
abstractions_set.add(sorted_candidate_concepts[idx])
idx++
4. Ontology
Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio