Intelligent Text Annotation Using Topic Maps and Nested Data Structures
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Solution Overview
Problem
Current word processing technologies lack effective methods for intelligent annotation of text documents, which limits the accuracy of text-related applications such as text categorization, topic detection, and document search, and does not provide users with actionable insights from annotated data types like names or dates.
Innovation Solution
A method and system that create a topic map based on user interests, using a data structure to store topic information and annotations, allowing for the retrieval and attachment of relevant knowledge structures to corresponding topics within text documents, enabling users to take quick actions based on identified data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional word processing annotation methods are used, then text can be annotated with basic markings, but the accuracy of text-related applications remains limited and actionable insights cannot be provided
Solution Approach 1:
The patent implements nested data structures where TopicAnnotation objects contain TopicDataStructure objects, which in turn contain TopicMap objects. This hierarchical nesting allows complex annotation information to be organized in manageable layers, improving accuracy without overwhelming system complexity
Solution Approach 2:
The patent introduces TopicMap and TopicDataStructure as intermediary layers between the text and the annotation system. These intermediaries structure and organize topic information, enabling accurate text-related applications while managing complexity through standardized data interfaces
2Loss of information
If comprehensive topic information and knowledge structures are stored for each topic, then actionable insights can be provided, but data storage and processing complexity increases
Solution Approach 1:
The patent segments annotation data into distinct components: TopicAnnotation (container), TopicDataStructure (data organization), and TopicMap (topic relationships). This segmentation allows comprehensive information storage while managing complexity through modular, independently manageable data structures
Solution Approach 2:
The patent adds a dimensional layer to text annotation by introducing hierarchical topic structures. Instead of flat annotations, the system creates multi-dimensional topic relationships with parent-child connections and associated data, enabling comprehensive information retrieval without linear complexity increase
3Measurement precision
If topic maps and data structures are created and processed for each text, then intelligent annotation accuracy improves, but processing time increases
Solution Approach 1:
The patent creates TopicMaps and TopicDataStructures that can be prepared in advance for known topics. This preliminary structuring allows rapid retrieval and attachment during text annotation, improving accuracy while reducing real-time processing time through pre-organized data
Data Source
AI summary
Text is intelligently annotated by first creating a topic map summarizing topics of interest of the user. A data structure is created. The topic map is used to create two linked user dictionaries, a topic dictionary reflecting topic names and a traversal dictionary reflecting the knowledge structure of a topic. Actions may be linked with topic types. When the text to be annotated is being read, the topic data structure of the topics found in the text are automatically instantiated using the dictionaries and any actions previously linked to topic types. Instantiated topic data structures are automatically attached to the text being annotated. A user GUI may be created to allow the user to access and interact with the text annotations.