Abstract: We propose an Information Highway (IH) which addresses interoperability in software systems supporting Humanitarian Crises (HC) and consequently enables efficient decision making at any level: operational, organizational and donor levels. We model our IH by (a) manipulating the semantics stored in knowledge of data repositories, which are interwoven in everyday activities of managing responses to HC and (b) understanding the meaning and the purpose of requests for data retrievals issued in such environments.

Abstract: Decision-makers in humanitarian crisis need information to guide them in making critical decisions. Finding information in such environments is a challenging task. Therefore, decision-makers rely on domain experts who possess experience and knowledge from previous humanitarian crises to provide them with the information they need. In this paper, we explore the ability of the existing computing technologies to augment the capabilities of those experts and help decision-makers to make faster and better decisions. Among many computing technologies we have today, word embedding and the semantic web are able to support such augmentation of the domain expert. In this paper, we train a word embedding model using word2vec, transform words and terms from news archive to entities in domain ontology, annotate those entities with their equivalent concepts from upper ontologies, and reason about them using semantic similarity and semantic matching, to represent and retrieve knowledge, and answer questions of interest to decision-makers in humanitarian crises. The approach was evaluated by comparing the use of word embeddings with and without semantic classification for the retrieval of information about the current humanitarian crisis in Syria.