Methods and system for improving the relevance, usefulness, and efficiency of search engine technology

Abstract

The disclosed methods, systems, and apparatus use Natural Language Processing (NLP) in conjunction with a world model and cognitive frames to semantically analyze, understand, rank, store, and retrieve digital text. The goal is to improve the relevance, usefulness and efficiency of information search. The world model represents things existing in the real world whereas cognitive frames specify possible user interaction with such a world. Using NLP in conjunction with a world model and cognitive frames to understand text is an advancement in automated text analysis. It addresses three serious shortcomings of the existing search technology: the inadequate measure of the meaningful content in web pages; a poor understanding of users' goals and tasks in their search and, the irrelevant search results. The disclosed methods have led to the successful implementation of a full-scale semantic search engine in medicine, and they are applicable and adaptable to other disciplines.

Dependencies are inferred from the claim text.