Searchable Concept Network for Semantic Ad Targeting
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Solution Overview
Problem
Current search engines and online advertising systems face challenges in providing relevant search results and targeted advertising, as they rely on outdated computational models that fail to accurately match user queries with suitable advertisements in a semantic context.
Innovation Solution
The development of a searchable concept network system that extracts and quantifies entities and relations, uses machine learning models to identify candidate concepts and relations, and computes significance, allowing for the generation of context-specific ads and exploration of user intent through a navigable graph interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computational models (Boolean or vector space) are used for search and advertising, then the system is simple to implement, but the relevance and accuracy of matching user queries with advertisements deteriorates
Solution Approach 1:
The patent segments the search and advertising system into distinct modules: a search module that processes user queries through a concept network to identify relevant concepts and entities, and an advertising module that separately matches users with advertisements based on the same conceptual framework. This segmentation allows each module to specialize in its function while maintaining overall system coherence, improving relevance accuracy without overwhelming complexity.
Solution Approach 2:
The patent introduces a concept network as an intermediary layer between user queries and both search results and advertisements. This concept network serves as a mediator that transforms traditional keyword-based matching into semantic concept-based matching, enabling more accurate relevance assessment while providing a unified framework that manages the complexity of integrating search and advertising functions.
2Reliability
If a concept network with semantic context is implemented, then the relevance of search results and advertising improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and indexing documents to build the concept network structure before actual search and advertising operations. Entities, relations, and concepts are identified and organized in advance, creating a ready-to-query semantic framework. This pre-computation reduces the complexity of real-time processing during user interactions while maintaining high reliability in ad targeting.
Solution Approach 2:
The patent changes the fundamental parameters of the search and advertising system from keyword-based metrics to concept-based semantic parameters. Instead of matching based on exact keyword occurrence or simple vector similarity, the system evaluates relationships between concepts, entities, and their semantic contexts. This parameter transformation enables more accurate ad targeting while the modular architecture manages the associated computational complexity.
3Adaptability or versatility
If traditional search models are used, then the system operates quickly with simple processing, but the ability to understand user intent and provide contextualized results deteriorates
Solution Approach 1:
The patent extracts the semantic understanding function from the traditional search processing pipeline and places it in the concept network layer. The concept network pre-computes and stores semantic relationships, entity associations, and contextual information separately from the query processing path. This extraction allows the system to understand user intent through conceptual relationships without adding significant processing time to the actual search operation.
Data Source
AI summary
Systems and methods for providing a searchable concept network are provided. One such system includes a concept network application, which is hosted at least partially on a server. The concept network application may include or access an indexer for indexing text corpus. The text corpus is analyzed to extract concepts, as well as significance, relevancy and relationship information between concepts. The concepts and associated significance, relevancy and relationship information are utilized to construct a concept network, which is stored in one or more indexes or databases accessible to the concept network application. One or more user computer devices are provided access to the application, which receives a query from the user, and in response, provides the user with a relevant portion of the concept network. User behavior or interaction within the concept network may be detected and utilized in providing relevant advertisement information.