**Abstract**

Disaster response presents the current situation, creates a summary of available information on the disaster, and sets the path for recovery and reconstruction. During the last 10 years, various disciplines have investigated disaster response twofold. First, researchers published several studies using state-of-the-art technologies for disaster response. Second, humanitarian organizations have produced numerous mission statements on how to respond to natural disasters. The former suggests questioning: If we have developed a considerable number of studies to respond to a natural disaster, how can we cross-validate its results with humanitarian organizations' mission statements to bring the knowledge of specific disciplines into disaster response? To address the above question, the research proposes an experiment that considers both: knowledge produced in the form of 8364 abstracts of academic writing on Disaster Response and 1930 humanitarian organizations' mission statements indexed online. The experiment uses an Artificial Intelligence algorithm, Neural Network, to perform the task of word embedding––Word2Vec––and an unsupervised machine learning algorithm for clustering––Self-Organizing Maps. Finally, it employs Human Intelligence for the selection of information and decision-making. The result is a digital infrastructure that will suggest actions and tools relevant to a specific scenario, providing valuable information loaded with architectural knowledge to guide the decision-makers at the operational level in tasks dealing with spatial and constructive constraints.

**Keywords:** artificial intelligence, disaster response, big data, Word2Vec, self-organizing maps, architecture
