1. A method for automatic semantic rating and abstraction of literature comprising: extracting by inferring by a computer system a plurality of plot details and character relationships using deep semantic analysis of a first electronic text, wherein the extracting comprises using deep semantic analysis of a first electronic text comprises program instructions for performing at least one semantic parsing process selected from the group consisting of: parsing the electronic text to extract relationships between information entities in their associated contexts and how they relate to each other thereby producing triplets including triple store information items, pairs of information items and an ontological relationship between each pair, or both; and parsing the electronic text to extract specific relationships between two information entities, thereby producing triplets of pairs of entities and their relationship; introducing by a computer a first level in a first electronic hierarchical model representing the electronic text, wherein the first level comprises semantic relationships including the extracted plot details and character relationships from the first electronic text; abstracting by a computer using deep semantic analysis a plurality of plot details and character relationships from the first level of the first electronic hierarchical model; introducing by a computer a second level of semantic relationships to the first electronic hierarchical model, wherein the second level comprises the plot details and character relationships abstracted from the first level such that the second level in the hierarchical model is more abstract and less detailed than the first level; comparing by a computer system two or more levels in the first electronic model to two or more levels of a second hierarchical electronic model representing a second electronic text, wherein the second electronic text has an associated user preference indicator; responsive to the comparing finding a match above a pre-determined degree of similarity, declaring by a computer system to a user via a user interface device the first electronic text to have an expected user preference indicator as the user preference indicator associated with the second electronic text; and responsive to the comparing finding a match above pre-determined degree of dissimilarity, declaring by a computer system to a user via a user interface device the first electronic text to have an expected user preference indicator complementary with the user preference indicator associated with the second electronic text; wherein the declaration via a user interface device comprises at least a tangible recommendation output from the computer system.
1. A method for automatic semantic rating and abstraction of literature comprising: extracting by inferring by a computer system a plurality of plot details and character relationships using deep semantic analysis of a first electronic text, wherein the extracting comprises using deep semantic analysis of a first electronic text comprises program instructions for performing at least one semantic parsing process selected from the group consisting of: parsing the electronic text to extract relationships between information entities in their associated contexts and how they relate to each other thereby producing triplets including triple store information items, pairs of information items and an ontological relationship between each pair, or both; and parsing the electronic text to extract specific relationships between two information entities, thereby producing triplets of pairs of entities and their relationship; introducing by a computer a first level in a first electronic hierarchical model representing the electronic text, wherein the first level comprises semantic relationships including the extracted plot details and character relationships from the first electronic text; abstracting by a computer using deep semantic analysis a plurality of plot details and character relationships from the first level of the first electronic hierarchical model; introducing by a computer a second level of semantic relationships to the first electronic hierarchical model, wherein the second level comprises the plot details and character relationships abstracted from the first level such that the second level in the hierarchical model is more abstract and less detailed than the first level; comparing by a computer system two or more levels in the first electronic model to two or more levels of a second hierarchical electronic model representing a second electronic text, wherein the second electronic text has an associated user preference indicator; responsive to the comparing finding a match above a pre-determined degree of similarity, declaring by a computer system to a user via a user interface device the first electronic text to have an expected user preference indicator as the user preference indicator associated with the second electronic text; and responsive to the comparing finding a match above pre-determined degree of dissimilarity, declaring by a computer system to a user via a user interface device the first electronic text to have an expected user preference indicator complementary with the user preference indicator associated with the second electronic text; wherein the declaration via a user interface device comprises at least a tangible recommendation output from the computer system. 5. The method as set forth in claim 1 wherein the extracting, identifying and abstracting are repeated for a plurality of different electronic texts, and wherein the method further comprises: aggregating by a computer user ratings from the same user across three or more different electronic texts; cross-referencing by a computer one or more elements across the three or more different electronic texts to infer commonalities and distinctions; and inferring by a computer one or more literary elements the user is inclined to enjoy according to the aggregated user ratings according to the commonalities, or according to the distinctions, or according to both the commonalities and the distinctions; and designating one or more of the three or more different electronic texts with a likely user rating.