Knowledge Representation Concept Disambiguation for Relevant Search

The patent segments the information retrieval process into multiple stages: initial broad retrieval using simple keywords, followed by progressive filtering and refinement using user context information, knowledge representations, and semantic analysis. This segmentation allows the system to handle large volumes of information by processing it in manageable steps, first expanding the search scope and then progressively narrowing it down to relevant results.

The patent introduces knowledge representations (KRs) as intermediary structures that mediate between raw search queries and the vast database of indexed web pages. These KRs serve as an intermediate layer that organizes and pre-processes information, allowing the system to efficiently match user context with relevant content without having to manually review every page, thus reducing the difficulty of locating relevant information.

The patent applies local quality by tailoring search results to individual users based on their specific context information, preferences, and historical data. Instead of providing a uniform comprehensive list to all users, the system customizes the information presentation for each user, ensuring relevance while maintaining completeness for that specific user's needs. This selective completeness reduces irrelevant information overload by filtering out content that doesn't match the user's profile.

The system dynamically changes retrieval parameters based on user context information, knowledge representations, and semantic analysis results. By adjusting parameters such as relevance thresholds, result ordering, and filtering criteria in response to user behavior patterns and contextual data, the system maintains information completeness while adapting to reduce irrelevant content, thereby preventing user overload.

The patent implements preliminary action by pre-processing and organizing information into knowledge representations before actual search queries are processed. The system pre-establishes semantic relationships, categorizes content, and prepares contextual data structures in advance. This preliminary organization reduces the computational complexity during actual search operations, as the heavy lifting of analysis has already been performed, thereby achieving high relevance accuracy without proportionally increasing system complexity.

The system creates simplified copies or abstract representations of complex information structures through knowledge representations. Instead of working with the full complexity of raw web pages and their interrelationships during search, the system uses condensed KR copies that capture essential semantic meaning. This copying approach maintains relevance accuracy by preserving critical information relationships while significantly reducing the computational complexity of analysis operations.