1. A system to recommend content to a user, comprising: a computer; a content of interest to the user stored on the computer; a first semantic abstract representing the content of interest stored on the computer, the first semantic abstract including a first plurality of state vectors; a second semantic abstract representing a second content, the second semantic abstract including a second plurality of state vectors; a semantic abstract comparer to compare the first semantic abstract to the second semantic abstract; and a content recommender to recommend the second content if the first semantic abstract is within a threshold distance of the second semantic abstract, wherein: a dictionary includes a directed set of concepts including a maximal element and directed links between pairs of concepts in the directed set, the directed links defining “is a” relationships between the concepts in the pairs of concepts, so that each concept is either a source or a sink of at least one directed link; for each concept in the directed set other than the maximal element, at least one chain in the directed set includes a set of directed links between pairs of concepts connecting the maximal element and the concept; a basis includes a subset of the chains; and each state vector in the first semantic abstract and the second semantic abstract measures how concretely a concept is represented in each chain in the basis by identifying the smallest predecessor in the chain in relation to the concept.