Personalized Search Query Augmentation via User Ontology
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Solution Overview
Problem
Existing search query systems rely on historical or common search terms, failing to account for individual users' unique patterns of association between topics, which limits the relevance of search query augmentations.
Innovation Solution
A method that performs semantic and syntactic analysis on a user's social media contributions to identify topic entities and their relationships, generating an ontology model that suggests additional search query terms based on the user's specific association patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If search query systems use historical or common search terms for augmentation, then the system complexity remains low, but the personalization and relevance of search results deteriorate
Solution Approach 1:
The system performs preliminary semantic and syntactic analysis on user social media contributions to build an ontology model before search queries are executed. This pre-processing of user data creates a personalized knowledge base that enables tailored search augmentations without adding complexity during the actual search operation
Solution Approach 2:
An ontology model serves as an intermediary layer between raw user social media data and search query augmentation. This model abstracts and structures user association patterns, enabling personalized search enhancements while shielding the system from the complexity of directly processing raw social media content
2Measurement precision
If semantic and syntactic analysis is performed on user content to identify topic entities and relationships, then the relevance of search query augmentations improves, but the processing time and computational resources increase
Solution Approach 1:
Semantic and syntactic analysis are performed in advance on user social media contributions to extract topic entities and relationships before search queries are submitted. This preliminary processing creates a ready-to-use ontology model that enables fast, relevant search augmentations without requiring intensive processing during the search operation itself
Solution Approach 2:
The system extracts only the essential topic entities and relationships from user social media content through semantic and syntactic analysis, separating these key elements from the rest of the content. This extraction focuses computational resources on the most relevant information needed for search augmentation, reducing overall processing requirements
Data Source
AI summary
A method of augmenting a user's search query based on analysis of content contributions of the user. One or more processors gather content contributions made to a media source by a user. Topic entities are identified from content contributed by the user. Semantic and syntactic analysis are performed on the content, and attributes of topic entities are determined based on the analysis. The topic entities are grouped based on similar and/or related attributes of the topic entities. The one or more processors determine relationships between the topic entities based on the grouping and the semantic analysis of the content contributed by the user of the media source. An ontology model is generated, based on the topic entities and their interrelationships, and responsive to determining the user initiates a search query, the one or more processors present additional topic entities as selection choices for augmenting the search query.