URL Concept Identification via Structural and Semantic Parsing
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Solution Overview
Problem
Conventional information retrieval systems fail to detect related concepts from URLs and domain names due to simple keyword matching, which limits their ability to provide relevant information and target advertisements effectively.
Innovation Solution
The solution involves structural parsing to extract information from URLs or domain names, followed by semantic parsing to identify concepts, which are then mapped using a concept association map to retrieve and rank related concepts, allowing for better capture of user intent and improved advertisement targeting, even with typographical errors or lacking click-through rate and cost per click data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple keyword matching is used to extract keywords from URLs or domain names, then the process is fast and easy to implement, but the system fails to detect related concepts and provides inaccurate relevant information
Solution Approach 1:
The patent introduces a concept association map as an intermediary component that bridges the gap between simple keyword extraction and accurate concept detection. The system extracts keywords using simple methods, then uses the concept association map to retrieve related concepts, thereby maintaining speed while improving accuracy through the intermediary mapping layer.
Solution Approach 2:
The patent segments the concept retrieval process into distinct stages: keyword extraction, concept identification through semantic parsing, and related concept retrieval through the concept association map. This segmentation allows each component to optimize for its specific function, with keyword extraction remaining simple and fast while concept relationships are handled through the structured map.
2Device complexity
If conventional search engines use simple keyword matching, then the system complexity is low, but the ability to provide relevant information and target advertisements is limited
Solution Approach 1:
The concept association map serves multiple functions: it retrieves related concepts for better search results, enables more accurate advertisement targeting, and handles various URL structures. This multi-functionality allows the system to maintain relatively low complexity while significantly improving adaptability and versatility in information retrieval and advertising.
Solution Approach 2:
The concept association map acts as an intermediary that enables sophisticated advertisement targeting without requiring complex real-time analysis. The pre-built map allows the system to quickly retrieve relevant concepts and match them with appropriate advertisements, maintaining system simplicity while improving targeting capability.
3Productivity
If the system only uses exact keyword matching, then the processing is simple and quick, but it cannot handle typographical errors or variations in user intent
Solution Approach 1:
The concept association map is pre-built and stored in advance, containing relationships between concepts and their variations. When a query arrives, the system can quickly retrieve related concepts without performing complex real-time analysis, thereby maintaining high processing speed while improving reliability in handling typographical errors and intent variations.
Solution Approach 2:
The concept association map serves as an intermediary that bridges exact keyword matching with flexible error handling. It allows the system to quickly retrieve concepts related to the query terms, providing reliable results even when typographical errors are present, while maintaining fast processing through the pre-organized map structure.
Data Source
AI summary
A solution for identifying related concepts of URLs and domain names includes using structural parsing to extract information from user input comprising a URL or domain name. The information includes one or more of a protocol, a location, and a subdirectory. Semantic parsing of the information is used to identify a first one or more concepts represented by one or more tokens within the extracted information. A content association map is queried to retrieve a second one or more concepts related to the first one or more concepts. Each of the concepts represents a unit of thought, expressed by a term, letter, or symbol. The concept association map includes a representation of concepts, concept metadata, and relationships between the concepts. The first one or more concepts and the second one or more concepts are ranked, and the ranked concepts are stored for displaying to one or more users of the computer platform.