Conversational Search Ontology Mapping for CMS Intent Accuracy
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Solution Overview
Problem
Conversational search via chatbots in content management systems faces challenges in understanding user intent and extracting relevant content, leading to less than optimal results, especially when compared to web content searches, due to difficulties in indexing and parsing data within CMS.
Innovation Solution
A conversational search system that trains a deep learning model to analyze user queries, generates a domain ontology, tags content keywords with metadata, and maps user queries to relevant content keywords for accurate search results, dynamically generating outlines and hyperlinks within chatbot dialogue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conversational search is implemented in CMS using traditional methods, then the system structure remains simple, but the accuracy of understanding user intent and extracting relevant content deteriorates
Solution Approach 1:
The system performs preliminary actions by training a deep learning model beforehand to learn semantic analysis of user queries. The model is pre-trained to identify intents and entities in queries against the CMS, enabling accurate understanding of user intent when queries are actually submitted. This preliminary training phase resolves the contradiction by preparing the system in advance to handle complex semantic analysis without requiring complex processing during actual search operations.
Solution Approach 2:
The patent introduces a domain ontology as an intermediary between user queries and CMS content. The ontology serves as a mediator that bridges the gap between natural language queries and structured CMS data, enabling accurate mapping of user intent to relevant content. This intermediary structure resolves the contradiction by providing a systematic framework that simplifies the complex task of intent understanding and content extraction.
2Manufacturing precision
If traditional search methods are used in CMS, then the implementation process remains simple, but the fidelity of search results to user interests deteriorates
Solution Approach 1:
The system performs preliminary actions by generating a domain ontology and tagging CMS content keywords with metadata before actual search operations. This pre-processing phase creates a structured framework that enables high-fidelity search results. By preparing the ontology and metadata in advance, the system achieves precise matching between user queries and CMS content without requiring complex real-time processing during search execution.
Solution Approach 2:
The patent replaces traditional mechanical search methods with deep learning-based semantic analysis. Instead of relying on simple keyword matching algorithms, the system uses trained neural networks to understand user intent and extract relevant content. This substitution of mechanical search with intelligent semantic analysis resolves the contradiction by achieving high-fidelity results through learned patterns rather than complex rule-based systems.
3Measurement precision
If deep learning models and domain ontology are implemented, then user intent understanding improves, but the complexity of indexing and parsing CMS data increases
Solution Approach 1:
The patent segments the complex task of CMS indexing and parsing into distinct phases: generating domain ontology from CMS content, tagging content keywords with metadata based on the ontology, and then using the trained deep learning model to map user queries to the ontology. This segmentation resolves the contradiction by breaking down the complex indexing process into manageable steps that can be performed systematically, reducing overall complexity while maintaining high accuracy in user intent understanding.
Data Source
AI summary
In an approach for a conversational search in a content management system, a processor trains a deep learning model to learn semantic analysis of a plurality of user queries to identify intents and entities in the user queries. A processor analyzes the content management system to extract content keywords to generate a domain ontology. A processor augments the domain ontology based on the identified intents and entities in the user queries by the deep learning model. A processor tags the content keywords with metadata based on the domain ontology. A processor maps the intents and entities extracted from a current user query of a user to the content keywords extracted from the content management system to form a metadata keyword. A processor searches the content management system for a content based on the metadata keyword. A processor returns a search result for the current user query.