6. A computer implemented method for scoring a relative likelihood that a hypothetical event in a scenario is to occur, the method comprising: collecting narrative text communications, each communication including a group of words, regarding evidence related to the scenario; organizing the evidence contained in the narrative text into a knowledge base as one or more triples with each triple including a subject, a relation and an object, according to a given ontology; representing one of the one or more triples in the knowledge base in a semantic graph and building a symmetric weighted adjacency matrix representing an undirected topology of the semantic graph; building a tensor space to represent events or classes of events of the scenario; building a tensor operator measuring a connectedness or closeness of entities in the scenario into the hypothetical event; associating a vector space to the knowledge base by associating each entity with a basis vector, an entity being either a subject or an object in one of the one or more triples in the knowledge base; and scoring each event or classes of events by using the tensor operator to rank the relative likelihood of each event to occur relative to a class of events and defining a score as a tensor scalar product between two tensors representing events or classes of events.
7. The method according to claim 6 further comprising using the adjacency matrix as a linear operator onto the vector space associated with the knowledge base.