Entity Importance Engine for Book Text Analysis
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Solution Overview
Problem
Existing information systems face challenges in determining the relative importance of entities mentioned in documents, such as books, without explicit guidance, leading to difficulties in presenting information effectively and comprehensibly.
Innovation Solution
The implementation of an entity importance engine that assigns query-independent importance scores to entities mentioned in books, using various modules to calculate scores based on context, precision, and user interactions, and a presentation engine that selects and presents entities in a style enhancing comprehension.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all entities mentioned in a book are presented to users, then complete information is provided, but the presentation becomes cluttered and incomprehensible
Solution Approach 1:
The patent extracts only the most important entities from the book text for presentation. The entity importance engine calculates importance scores for each entity, and the presentation engine selects and displays only those entities with scores above a threshold, effectively extracting the essential information while leaving out less important details that would cause clutter.
Solution Approach 2:
The patent applies different presentation qualities to different entities based on their importance scores. High-importance entities are presented with full detail and prominent display, while lower-importance entities are either omitted or presented with reduced detail, creating a localized quality distribution that optimizes overall comprehensibility.
2Productivity
If importance scores are calculated for all entities in a book, then effective ranking is achieved, but system complexity increases
Solution Approach 1:
The patent segments the importance calculation process into distinct modular components: an entity identification module that extracts entities from text, an entity importance engine that calculates scores using multiple independent factors (frequency, section importance, entity type), and a presentation engine that displays results. This segmentation allows each module to be developed and optimized independently, managing system complexity while achieving effective ranking.
Solution Approach 2:
The entity importance engine serves multiple functions simultaneously: it identifies entities, calculates their importance scores based on various factors, ranks them, and provides the data for presentation. This multi-functionality reduces the need for separate systems for each task, thereby managing complexity while maintaining high productivity in information ranking.
3Measurement precision
If multiple factors are considered in importance scoring, then accuracy of entity ranking is improved, but calculation time increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing the importance of different book sections (e.g., table of contents, chapter headings, body text) before entity scoring. When entities are extracted from the text, their importance scores can be quickly calculated by referencing these pre-computed section importances along with simple frequency counts, rather than performing complex analyses at query time. This reduces calculation time while maintaining accuracy.
Data Source
AI summary
A corpus contains information including text from books and metadata about the books. The book texts mention entities of various types, such as location, date, event, and person entities. An entity importance engine analyzes the information in the corpus to identify the entities mentioned therein, and ranks the entities using query-independent importance scores. The importance scores can be based in part on the contexts in which the entities are mentioned by the books. A presentation engine enables searching of the corpus to identify books satisfying a search query. The presentation engine presents information about a designated book. Entities mentioned in the book are presented in a style selected to enhance comprehension and utility. For example, location entities can be presented on a map with markers showing the locations of the entities. The number of entities presented are limited and ordered based on the query-independent importance scores.