Automatic Semantic Rating and Abstraction of Literature

摘要

Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

主权项

1. A method for automatic semantic rating and abstraction of literature comprising:
introducing by a computer a first level in a first electronic hierarchical model representing a first electronic text, wherein the first level comprises semantic relationships including a plurality of plot details and character relationships from within 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 at least the second level in the first electronic model at least one level 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.