Interactive Semantic Concept Annotation for Documents
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Solution Overview
Problem
Authors of web-published documents lack an easy method to create annotations associating semantic concepts with unique terms, leading to inaccurate or irrelevant additional content being automatically inserted by webhosts, which does not reflect the author's intent.
Innovation Solution
A system and technique that identifies unique terms in a document and suggests semantic concepts to the author for selection and insertion as annotations, allowing authors to control the addition of relevant content by parsing the document and providing real-time or post-creation suggestions for semantic concepts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If webhosts automatically insert annotations with semantic concepts, then productivity is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system performs preliminary identification of unique terms in the document before annotation insertion. By pre-processing the document to identify candidate terms and their potential semantic concepts, the system prepares the groundwork for accurate annotation while maintaining efficiency through automated preliminary analysis.
Solution Approach 2:
The system provides feedback to authors by suggesting semantic concepts and allowing them to review, accept, or reject annotations. This feedback loop ensures that annotations align with author intent while maintaining the benefits of automated suggestion, resolving the contradiction between automation speed and precision.
2Ease of operation
If automated algorithms select semantic concepts, then ease of operation is improved, but reliability deteriorates
Solution Approach 1:
The system acts as an intermediary between automated algorithms and final annotation selection. It provides suggested semantic concepts to authors, who then make the final decision on which annotations to apply. This intermediary role combines the efficiency of automated suggestions with the reliability of human judgment.
Solution Approach 2:
Authors have the ability to manually review and adjust annotation suggestions according to their own judgment and intent. This self-service capability ensures that annotations reliably reflect author intent while maintaining ease of operation through the automated suggestion system.
3Device complexity
If no semantic concepts are available for a unique term, then adaptability deteriorates, but device complexity is improved
Solution Approach 1:
The system performs preliminary searches across multiple data sources to identify available semantic concepts before presenting options to the author. By proactively gathering potential matches from various sources, the system expands coverage without requiring complex real-time generation of new concepts.
Solution Approach 2:
The system uses a universal semantic concept database that can serve multiple purposes and cover diverse domains. This multi-functional database structure allows the same system to handle various types of terms and concepts across different subjects, improving adaptability without proportionally increasing complexity.
Data Source
AI summary
One or more techniques and/or systems are provided for interactively associating a semantic concept with a unique term that is input by a user. As the user is creating a document and/or once the user has completed a draft of the document, the document is parsed to identify unique terms (e.g., persons, places, things, services, etc.) in the document. When a unique term is identified, a query is generated to locate one or more semantic concepts (e.g., URLs, URNs, or other identifiers, for example) that are associated with the identified unique term and a notification indicative of the results is generated. From this notification, the user can select whether to associate the unique term with any and/or all of the located semantic concepts. In this way, supplemental content may be added to a document that the user is creating, for example.