Interactive Feedback Elements for Semantic Search Data Correctness
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Solution Overview
Problem
Existing search technologies face challenges in obtaining effective user feedback for semantic web and structured data searches, particularly in ensuring data correctness, freshness, and user relevance, due to difficulties in obtaining positive/negative feedback and suggested changes for entity attributes and lists.
Innovation Solution
The implementation of interactive feedback elements in a user interface that allows users to interact with entity attributes using standard inputs and natural user interface gestures, combined with crowd-sourcing and suggested information to derive and present top popular or suggested attribute values, ensuring data correctness and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search interfaces are used for semantic web and structured data searches, then basic search functionality is provided, but user feedback on entity attributes and lists cannot be effectively obtained
Solution Approach 1:
The patent implements interactive feedback elements (thumbs up/thumbs down buttons, edit suggestions) that enable users to provide direct feedback on entity attributes and search results. This feedback mechanism allows the system to continuously improve data correctness by incorporating user corrections and validations, resolving the contradiction between maintaining reliable data and enabling easy feedback collection.
Solution Approach 2:
The system allows users to directly edit and suggest corrections to entity attributes through the interface. Users can modify attribute values, add new information, and validate data accuracy themselves, eliminating the need for complex administrative processes while improving data correctness through crowd-sourced validation.
2Reliability
If interactive feedback elements are added to obtain user feedback on entity attributes, then data correctness and freshness are improved, but device complexity increases
Solution Approach 1:
The feedback functionality is segmented into discrete, independent interactive elements (individual thumbs up/thumbs down buttons for each attribute, separate edit suggestion fields) that can be selectively applied to specific entity attributes. This modular approach allows the system to gather feedback on a per-attribute basis without requiring a complete redesign of the entire interface, thus improving data correctness while managing complexity through incremental implementation.
3Reliability
If crowd-sourcing is used to derive suggested attribute values, then data freshness and user relevance are improved, but processing time and system complexity increase
Solution Approach 1:
The system pre-processes and stores crowd-sourced feedback and suggestions in structured formats during off-peak times. When generating search results, the system retrieves pre-processed suggested attribute values and crowd-sourced data, avoiding real-time computation delays. This approach maintains data freshness by continuously incorporating new feedback while minimizing processing time during user interactions.
Data Source
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AI summary
Architecture that implements fact interactive elements and list interactive elements in a user interface (UI) to assist in obtaining user feedback on entity attributes for semantic web and structured data searches to ensure data correctness, freshness, and user relevance. The fact interactive element enables user interaction with the attribute data of the corresponding attribute. The user interaction then provides feedback as to correctness of the attribute data for the given attribute. Each state has a corresponding visual state which has a clear visual distinction from other states. The interactive elements enable the use of standard user inputs such as with input devices, as well as interaction using gestures such as associated with natural user interface (NUI) gestures.