Semantic Region Identification via Density Clustering
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Solution Overview
Problem
Current location-based services and map services are inadequate in providing semantic information, as they rely primarily on longitude/latitude coordinates and official administrative names, failing to effectively handle non-official place names and their varying geographical boundaries.
Innovation Solution
A geographical location rendering system and method that utilizes user-generated content for density clustering and data mining to identify semantic regions by extracting common region names and determining their spatial scope, incorporating modules for density clustering, name extraction, and region scope detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current location systems use longitude/latitude coordinates and official administrative names, then location information can be precisely recorded and stored, but the system cannot effectively handle non-official place names and semantic region identification
Solution Approach 1:
The patent introduces an intermediary layer between official administrative names and user queries. This layer includes semantic region identification modules that map non-official place names (like 'SoHo') to their corresponding official administrative boundaries, enabling the system to understand and render semantic locations without requiring direct support for every unofficial name variant
Solution Approach 2:
The system performs preliminary actions by pre-identifying semantic regions and pre-mapping non-official place names to official administrative boundaries. This preparation allows the location system to handle diverse user queries efficiently without requiring real-time analysis of every new place name, resolving the contradiction between adaptability and information preservation
2Ease of operation
If semantic region boundaries are defined based on cultural and social characteristics, then location information becomes more meaningful and intuitive, but the geographical boundaries become unclear and difficult to define
Solution Approach 1:
The patent applies local quality by allowing different regions to be defined with different boundary characteristics. Semantic regions can have fuzzy boundaries based on cultural characteristics in some areas, while maintaining precise administrative boundaries in others. The system adapts the boundary definition to the local context, making location information more intuitive where needed while maintaining precision where required
Solution Approach 2:
The system dynamically adjusts boundary definitions based on the specific semantic region being rendered. For regions with clear cultural boundaries, the system uses well-defined boundaries. For regions with ambiguous boundaries, the system can dynamically adjust the rendering parameters to provide appropriate level of precision, making the system both intuitive and precise as needed
3Measurement precision
If the system processes a huge amount of user generated contents for semantic region identification, then location rendering becomes more accurate and comprehensive, but the processing time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary processing by pre-identifying semantic regions and pre-extracting key features from user-generated contents. This pre-processing creates a database of semantic region information that can be quickly queried during actual location rendering operations, significantly reducing the processing time required for each individual location analysis while maintaining high accuracy
Solution Approach 2:
The patent extracts only the most relevant information from the huge amount of user-generated contents. Instead of processing every detail, the system extracts key semantic features, common place names, and boundary characteristics to create a condensed representation of semantic regions. This extraction approach maintains identification accuracy while dramatically reducing the computational resources and time required for processing
Data Source
AI summary
A geographical location rendering method executed in a geographical location rendering system for identifying at least one semantic region is provided. A density clustering is performed on a plurality of user generated contents of respective geographical location name information to generate a plurality of region candidates. A name extraction is performed on the region candidates to extract and confirm a common region name of the region candidates as a name of the semantic region. A region scope of the region candidates is detected as a location scope of the semantic region according to a spatial density analysis.