Keyword Generation System Using Ensemble AI for B2B Content Syndication
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Solution Overview
Problem
Existing keyword extraction techniques struggle to accurately identify and rank keywords relevant to specific business categories and topics in the B2B domain, due to the unbounded target space and lack of domain focus.
Innovation Solution
A keyword generation system and method that uses ensemble artificial learning techniques, combining unsupervised and supervised machine learning, to classify content into B2B categories and re-rank keywords based on their relevance to specific topics, using a proprietary Topic Classifier model and Candidate Extractor and scorer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword extraction techniques are used, then the process is simple and fast, but the accuracy and relevance of extracted keywords to specific B2B domains is low
Solution Approach 1:
The system segments the B2B domain into multiple subdomains (e.g., biotechnology, Finance, Healthcare, Technology) and applies domain-specific keyword extraction for each. This segmentation allows the system to improve keyword accuracy by considering subdomain-specific terminology and context, rather than treating all B2B content uniformly.
Solution Approach 2:
The system introduces an intermediary classification layer that first identifies the B2B subdomain of the content, then applies appropriate keyword extraction techniques tailored to that subdomain. This intermediary step mediates between the general B2B domain and specific keyword extraction, improving overall accuracy while managing complexity through modular design.
2Measurement precision
If domain-specific keyword extraction is implemented, then keyword relevance to B2B subdomains improves, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary classification of content into B2B subdomains before executing keyword extraction. By pre-identifying the relevant subdomain, the system can then apply only the necessary domain-specific extraction techniques, avoiding the computational overhead of analyzing all possible domains for every piece of content.
Solution Approach 2:
The system applies different keyword extraction strategies tailored to each B2B subdomain's specific characteristics and terminology. Instead of using a uniform approach across all domains, each subdomain receives localized processing optimized for its unique features, improving keyword relevance while efficiently allocating computational resources.
3Adaptability or versatility
If multiple B2B subdomains are considered, then the coverage and applicability of keyword extraction improves, but the difficulty of maintaining domain expertise increases
Solution Approach 1:
The system divides the extensive B2B domain into manageable subdomains (biotechnology, Finance, Healthcare, Technology), making it feasible to maintain domain expertise for each segment. This segmentation allows the system to cover multiple domains while keeping the knowledge requirements for each subset tractable.
Solution Approach 2:
The system employs a universal framework that can handle multiple B2B subdomains through a common architecture. The classification mechanism and extraction pipeline are designed to be domain-agnostic, adapting to different subdomains through configuration rather than requiring completely separate systems for each domain.
Data Source
AI summary
A system and method for more granular keyword generation wherein the keyword generation may be used for content syndication or other activities. The keyword generation may be performed using a plurality of artificial intelligence models including a topic classifier, a keyword scorer and a keyword ranker and recommender. The keyword generation system and method may discover keywords weighted by business categories.