Enhanced Knowledge Base Indexing for Configuration Error Search
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Solution Overview
Problem
Current search engines and customer support systems face challenges in accurately identifying and analyzing configuration errors in knowledge base articles due to their text-based matching techniques, which struggle with domain-specific language, abbreviations, and conditional constraints, leading to incomplete or outdated solutions and increased costs.
Innovation Solution
A system and method for generating an enhanced index for knowledge base documents using filters such as tokenizers, synonym filters, and named-entity filters to preprocess and index documents, allowing for more precise searches and improved query results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If text-based matching techniques are used to search knowledge base articles, then the search engine can process queries efficiently, but it fails to accurately identify configuration errors due to domain-specific language, abbreviations, and synonyms
Solution Approach 1:
The system performs preliminary analysis of knowledge base articles by extracting configuration parameters, constraints, and relationships before search queries are received. This pre-processing creates a structured representation of the articles that can be efficiently matched against incoming queries, resolving the contradiction between processing efficiency and accuracy.
Solution Approach 2:
The system introduces an intermediary layer between the raw text-based search queries and the knowledge base articles. This intermediary involves translating and normalizing both the queries and articles into a common configuration parameter representation, enabling accurate matching while maintaining efficient processing.
2Reliability
If customer support representatives manually search and analyze articles, then they can provide comprehensive solutions, but it results in increased costs and longer wait times
Solution Approach 1:
The system enables self-service by automatically analyzing knowledge base articles and generating structured responses to customer queries. The automated system performs the analysis and matching functions that would otherwise require manual intervention from customer support representatives, reducing wait times while maintaining solution quality.
Solution Approach 2:
The system replaces the mechanical manual search and analysis process with an automated computational system. The automated system uses algorithms to extract configuration parameters, constraints, and relationships from articles, substituting the manual cognitive work of customer support representatives with automated processing.
3Ease of manufacture
If expert-defined rules are used in the knowledge base, then the system can provide structured guidance, but the rules become incomplete and outdated as software evolves
Solution Approach 1:
The system transitions from static expert-defined rules to a dynamic knowledge representation that automatically adapts to software changes. The system extracts current configuration parameters and constraints from actual software documentation and articles, allowing the knowledge base to evolve automatically with the software without requiring manual rule updates.
Solution Approach 2:
The system incorporates feedback mechanisms where the extracted configuration parameters and constraints from knowledge base articles are used to validate and update the knowledge representation. This feedback loop ensures that the system continuously improves its accuracy as new information becomes available, addressing the completeness issue.
4Adaptability or versatility
If conventional search engines use text-based matching, then they can handle general queries well, but they struggle with domain-specific technical terminology and conditional constraints
Solution Approach 1:
The system applies local quality by treating different parts of the knowledge base articles differently. Instead of uniform text processing, the system specifically identifies and processes configuration parameters, constraints, and relationships as distinct elements with specialized handling, while maintaining general query processing capabilities for other parts of the text.
Data Source
AI summary
Disclosed herein is a system and method for generating an enhanced index for documents contained in a knowledge base. Documents or configuration snapshots are tokenized and then passed through a number of filters. The filters modify the token stream to generate an enhanced token stream for the document by removing words from the stream and adding domain level knowledge to the tokens in the stream. The token stream is then added to the index for the document so that searches can be completed against the knowledge base when searches are generated from configuration snapshots or from inputted search queries.