Generating a Graph Data Structure that Identifies Relationships among Topics Expressed in Web Documents

摘要

A technique produces a graph data structure based on at least partially unstructured information dispersed over web documents. The technique involves applying a machine-trained model to a set of documents (or, more generally “document units”) to identify topics in the documents. The technique then generates count information by counting the occurrences of the single topics and co-occurrences of parings of topics in the documents. The technique generates conditional probability information based on the count information. An instance of conditional probability information describes a probability that a first topic will occur, given an appearance of a second topic, and a probability that the second topic will occur, given an appearance of the first topic. The technique then formulates the conditional probability information in a graph data structure. The technique also provides an application system that utilizes the graph data structure to provide any kind of computer-implemented service to a user.