Cross-Language Knowledge Search via Semantic Role Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing natural language processing systems face difficulties in performing deep linguistic and semantic analysis of user queries, leading to inadequate results and inefficiencies in cross-language knowledge search and extraction, as they often rely on simple keyword translation and lack robust semantic comparison.
Innovation Solution
A system that employs a Semantic Analyzer for semantically analyzing user requests and documents, using an expanded subject-action-object format (eSAO) and linguistic knowledge base for part-of-speech tagging, parsing, and semantic analysis, which preserves lexical and grammatical characteristics to build search patterns and indexes, enabling accurate translation and cross-language knowledge retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple keyword translation is used for cross-language search, then the system complexity is reduced, but the measurement precision of semantic comparison deteriorates
Solution Approach 1:
The patent segments the search process into distinct stages: keyword extraction, semantic role assignment, and structured pattern matching. By dividing the complex semantic analysis into manageable components (subjects, actions, objects, attributes), the system achieves precise cross-language comparison without requiring overly complex overall architecture
Solution Approach 2:
The patent introduces an intermediary semantic structure (eSAO - expanded subject-action-object format) that acts as a bridge between different languages. This intermediate representation preserves lexical and grammatical characteristics while enabling accurate translation and comparison across languages without direct complex language-to-language mapping
2Measurement precision
If deep linguistic and semantic analysis is performed on user queries, then the measurement precision of knowledge extraction is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary part-of-speech tagging and semantic role assignment on both user queries and document corpus before the actual search process. This preprocessing creates ready-to-use semantic structures that accelerate the subsequent search and matching operations, reducing processing time during query execution
Solution Approach 2:
The patent applies different levels of analysis depth to different parts of the query and document processing. Critical semantic elements (subjects, actions, objects) receive deep linguistic analysis while less important elements use simpler processing, optimizing the balance between precision and processing time
3Reliability
If robust semantic comparison is implemented for cross-language search, then the reliability of knowledge retrieval is improved, but the device complexity increases
Solution Approach 1:
The patent creates a universal semantic framework (eSAO structure) that handles multiple languages and search types through a single unified system. This multi-functional approach improves retrieval reliability across different languages while avoiding the complexity of separate specialized systems for each language or search type
Data Source
AI summary
A system and method for cross-language knowledge searching. The system has a Semantic Analyzer, a natural language user request/document search pattern/semantic index Generator, a user request search pattern Translator and a Knowledge Base Searcher. The system also provides automatic semantic analysis and semantic indexing of natural language user requests/documents on knowledge recognition and cross-language relevant to user request knowledge extraction/searching. System functionality is ensured by Linguistic Knowledge Base as well as by a number of unique bilingual dictionaries of concepts/objects and actions.