Hybrid Keyword and Category Search for Web Services
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Solution Overview
Problem
Conventional web service registry technologies rely on keyword search and category selection as separate queries, which can be inefficient and inaccurate in finding relevant web services, especially when dealing with large datasets and complex hierarchies.
Innovation Solution
A hybrid and iterative query enhancement system that combines keyword search and category selection using a matching engine, keyword preprocessor, and thesaurus to refine queries by calculating relevance indicators and providing feedback for improved search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional keyword search and category selection are used as separate queries, then the search process is simple to implement, but the search accuracy and efficiency deteriorate when dealing with large datasets and complex hierarchies
Solution Approach 1:
The patent combines keyword search and category selection into a unified hybrid search system that processes both query types simultaneously through a single query processor, rather than as separate independent queries. This integration allows the system to leverage both keyword matching and category hierarchy navigation to improve search accuracy while managing complexity through a coordinated processing framework.
Solution Approach 2:
The system dynamically adapts its search strategy by iteratively refining queries based on initial results and relevance feedback. The query processor can adjust between keyword-based and category-based approaches, and between different levels of category hierarchy, based on what proves most effective for finding relevant web services. This dynamic adjustment improves accuracy without requiring a statically complex system design.
2Measurement precision
If users manually specify keywords and categories for web service search, then the query can be precise, but the user input requirements and time consumption increase
Solution Approach 1:
The system performs preliminary actions by automatically generating candidate keywords and category suggestions based on the user's initial query intent. The category suggestion generator pre-computes relevant categories from the service registry hierarchy, and the keyword extractor pre-identifies potential keywords from service metadata, so users don't need to manually specify everything from scratch. This reduces user input time while maintaining query precision through automated preprocessing.
Solution Approach 2:
The system implements iterative feedback loops where initial search results and relevance assessments are fed back into the query processing mechanism. The system learns from user interactions and result relevance, automatically refining keyword selections and category suggestions for subsequent searches. This feedback mechanism allows the system to improve query precision over time without requiring increased user input effort.
3Productivity
If automatic keyword generation is used from web service metadata, then the keyword specification becomes efficient, but the keyword accuracy may deteriorate without manual verification
Solution Approach 1:
The system enables self-service by allowing the automated keyword generation and category suggestion processes to serve themselves through iterative refinement. The query processor automatically evaluates generated keywords against search results and relevance metrics, then self-corrects by adjusting keyword weights and selections in subsequent iterations. This self-service mechanism maintains keyword accuracy without requiring manual verification while preserving generation efficiency.
Solution Approach 2:
Automated keyword generation is enhanced through feedback loops where the system evaluates the effectiveness of generated keywords in producing relevant search results. Based on this feedback, the system automatically refines its keyword extraction and generation processes, adjusting which metadata fields are prioritized and how keywords are weighted. This feedback-driven automation maintains or improves keyword accuracy while preserving the efficiency benefits of automatic generation.
4Measurement precision
If users navigate complex category hierarchies to find web services, then the category selection can be precise, but the ease of operation deteriorates
Solution Approach 1:
The category suggestion generator performs preliminary action by automatically identifying and presenting the most relevant categories from the complex hierarchy based on the user's query and the service registry structure. Instead of requiring users to manually navigate through multiple hierarchy levels, the system pre-computes and presents a shortened list of highly relevant category suggestions, allowing users to select precise categories with minimal navigation effort.
Solution Approach 2:
The system adds another dimension to category navigation by implementing a relevance-based ranking overlay on the traditional category hierarchy. Instead of requiring linear navigation through hierarchy levels, users can access categories ranked by relevance to their query, effectively adding a relevance dimension that cuts through the hierarchical complexity. This allows precise category selection without requiring users to traverse the full hierarchical path.
Data Source
AI summary
Provided are techniques for providing recommendations to improve a query. A query with query keywords and selected categories is received. In response to determining that the selected categories are ranked high with reference to query relevance indicator values for each of the selected categories, a query relevance indicator of the query is calculated with each subcategory using keyword relevance indicators, each subcategory is ranked based on the query relevance indicators, and the ranked subcategories are provided for use in selecting new categories to be submitted with the query.