Semantic Location Parsing with Pre-calculated Spatial Verification
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Solution Overview
Problem
Existing location description parsing systems face inefficiencies due to high false result generation and responsiveness issues, particularly in languages like Chinese, where lack of standardized abbreviations and multiple aliases complicate parsing, leading to the need for additional semantic analysis that slows down online services.
Innovation Solution
Integration of semantic verification within the parsing process to validate syntactical components by checking spatial relationships such as 'part-of', 'near-by', and 'intersect' between keywords, with pre-calculated relationships to enhance parsing efficiency and reduce false results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional semantic analysis steps are applied after parsing to improve accuracy, then false results are reduced, but service responsiveness slows down
Solution Approach 1:
The patent pre-calculates and stores spatial relationships (part-of, near-by, intersect) between location keywords in a knowledge base before parsing operations. During parsing, these pre-computed relationships are directly queried rather than calculated in real-time, enabling semantic verification to occur early in the parsing process without slowing down service responsiveness
Solution Approach 2:
The patent replaces complex post-parsing semantic analysis with simpler spatial relationship verification using pre-computed data. Instead of applying general semantic analysis techniques after parsing, the system substitutes this with targeted queries to a pre-built knowledge base of spatial relationships, significantly reducing processing time while maintaining accuracy
2Productivity
If semantic verification is performed early in the parsing process, then parsing efficiency increases, but system complexity increases due to pre-calculation requirements
Solution Approach 1:
The patent segments the location parsing system into distinct modules: a pre-processing module that builds the spatial relationship knowledge base, and a parsing module that queries this knowledge base. This segmentation allows the complex task of semantic verification to be handled by a dedicated component with pre-computed data, reducing overall system complexity while improving parsing efficiency
Solution Approach 2:
The spatial relationships between location keywords are pre-calculated and stored in a knowledge base before the parsing process begins. This preliminary action eliminates the need for complex real-time spatial calculations during parsing, thereby improving efficiency without requiring the entire system to be fundamentally more complex
Data Source
AI summary
An automated arrangement for parsing location descriptions is provided in which semantic verification is integrated into a parsing process to reduce the generation of false results. The semantic verification involves checking up to three semantic relationships between keywords (i.e., syntactical components) parsed from the location description in a tokenization process to determine if a tokenization result is valid. The semantic relationships include: a) a spatial “part-of” relationship between location keywords; b) a spatial “near-by” relationship; and, c) a spatial “intersect” relationship. The semantic relationships between particular locations may be pre-calculated and stored as extended vocabulary to enable the semantic verification to occur early in the parsing process to thus increase overall parsing efficiency. The results of the parsing are sorted based on a rank score that is derived using the semantic relationships between the locations.