Search Query Ontology Segmentation for Comprehensible Data Structures
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Solution Overview
Problem
Current systems for presenting search query results are inefficient, as they often produce scattered and irrelevant information, requiring users to manually sift through multiple documents to extract useful data, leading to increased time and effort.
Innovation Solution
A system comprising a client device, an ontological databank with entity classes, and a server arrangement that segments search queries, identifies query concepts, determines data structures based on relationships, and renders results in a comprehensible visual format, allowing for inference and insight extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If search results are presented as a list or table of scattered documents, then comprehensive information retrieval is achieved, but information comprehension becomes rigorous and time-consuming
Solution Approach 1:
The system segments the scattered search results into organized groups based on entity classes and relationships. Query concepts are identified and tagged with entity classes, then results are clustered according to these entities, transforming the scattered list into structured, comprehendible groups that maintain comprehensive information while reducing analysis time.
Solution Approach 2:
The system introduces an intermediary processing layer that includes an ontological databank and relationship analysis module. This intermediary transforms raw scattered results into structured information by establishing relationships between query concepts and entities, then presenting results in an organized format that bridges the gap between comprehensive retrieval and easy comprehension.
2Ease of operation
If predefined filters are used to retrieve information, then filtering capability is provided, but the filters are not directly dependent on the query and irrelevant information remains
Solution Approach 1:
The system replaces static predefined filters with dynamic, query-dependent filtering. The filtering mechanism adapts to each specific query by identifying query concepts, determining their entity classes, and establishing relationships. This dynamic approach ensures filters are directly relevant to the user's specific information needs, eliminating irrelevant results while maintaining ease of operation.
Solution Approach 2:
The system changes the parameters of filtering from fixed predefined categories to dynamic parameters derived from query analysis. By analyzing query concepts and their relationships with entities in the ontological databank, the system adjusts filter parameters to match the specific context of each query, thereby eliminating irrelevant information while preserving useful results.
3Measurement precision
If each document is addressed individually to extract useful information, then thorough information extraction is achieved, but the process becomes rigorous and complex
Solution Approach 1:
The system merges the processing of multiple scattered documents by organizing results into entity-based groups. Instead of addressing each document individually, the system clusters results according to entity classes and relationships, allowing users to comprehend information in consolidated groups. This merging maintains thorough information extraction while significantly reducing processing complexity.
Solution Approach 2:
The system introduces a new dimension of organization based on entity relationships and ontology. Rather than processing documents in a linear or hierarchical manner, the system adds an entity-relationship dimension that groups information by semantic connections. This dimensional change transforms the complex task of individual document analysis into more manageable entity-based information clusters.
Data Source
AI summary
Disclosed is system for presenting information related to a search query, comprising: a client device configured to receive the search query; a database arrangement; an ontological databank and a server arrangement communicably coupled to the client device and the database arrangement, wherein the server arrangement is configured to: receive the search query, segment the search query into one or more query segments; identify one or more query concepts associated with one or more query segments, wherein each of the one or more query concepts are tagged with a corresponding entity class; determine a data structure for the information related to the search query based on one or more metrics of the relationships of the one or more query concepts, and render, on the client device, the information related to the search query presented in the data structure.