Domain Knowledge Ontology for Automated IT Problem Solving
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Solution Overview
Problem
Current systems lack an efficient method to capture and reuse contextual domain knowledge, leading to repetitive work and collaboration bottlenecks in IT enterprises, as existing methods for capturing practical knowledge are time-consuming and require significant effort.
Innovation Solution
A system comprising an input module, processor, and memory that captures domain knowledge through various sources, transforms it into ontologies using natural language processing and template matching, creates a contextual domain knowledge lookup table, and applies a planning algorithm to recommend solution pipelines based on problem definitions in PDDL files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If domain knowledge is captured through manual methods from domain experts, then knowledge can be obtained, but the process is time-consuming and requires significant effort
Solution Approach 1:
The system creates a digital copy of domain knowledge by automatically generating ontologies from existing problem descriptions and solution pipelines. Instead of manually interviewing domain experts, the system copies and structures knowledge from documented problem-solving instances, transforming unstructured text into structured ontological representations that can be reused across multiple problems.
Solution Approach 2:
The system performs preliminary action by pre-processing and structuring domain knowledge into ontologies and lookup tables in advance. The CDK lookup table is created beforehand to store mappings between contextual knowledge and solution pipelines, so that when new problems arise, the knowledge is already organized and ready for rapid retrieval and application without requiring time-consuming manual capture at the moment of problem-solving.
2Adaptability or versatility
If teams start afresh without reusing existing knowledge, then new problems can be addressed, but repetitive work occurs and collaboration bottlenecks arise
Solution Approach 1:
The system implements feedback by automatically analyzing new problem descriptions, comparing them against the CDK lookup table containing historical problem-solving knowledge, and recommending relevant pipelines. This feedback loop enables teams to learn from past solutions and apply proven approaches to new problems, reducing repetitive work while maintaining adaptability through the automated matching and recommendation process.
Solution Approach 2:
The ontology-based knowledge representation creates a universal framework that can handle multiple types of problems across different domains. The same CDK lookup table and recommendation mechanism serve diverse problem-solving needs, allowing the system to be universally applicable rather than requiring separate manual knowledge capture processes for each problem type.
3Loss of information
If contextual domain knowledge is not captured systematically, then individual expertise remains isolated, but knowledge cannot be reused across different locations and teams
Solution Approach 1:
The system merges isolated individual expertise into a collective organizational knowledge base by automatically capturing domain knowledge from multiple sources and consolidating it into a unified CDK lookup table. The ontology structure combines contextual knowledge, problem descriptions, and solution pipelines into an integrated framework that makes knowledge available across different locations and teams, transforming individual silos into shared organizational assets.
Data Source
AI summary
One of the major artifacts that pushed Information Technology companies ahead of its competitors is undoubtedly contextual domain knowledge. When a new development problem comes to an IT team, how problem solving and steps of action can be automatically formulated is the major area of research. A method and system for utilizing domain knowledge to identify solution to a problem has been provided. The problem is reformulated as recommending a workflow like a pipeline of connected steps, by leveraging contextual domain knowledge and technical knowledge, finally planning and scheduling solutions steps, given a problem of a domain & use case. This is achieved by Contextual sequence-aware recommendation of steps, backed by semantic web technologies and pattern recognition steps. Finally a plan is derived by automated planning techniques which can be executed based on software orchestration by connecting a repository of re-usable annotated code blocks.