Workforce Planning Knowledge Graph for Project Matching
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Solution Overview
Problem
Current workforce planning systems lack an efficient method to match new projects with previous projects based on timing, costs, and skills, leading to suboptimal resource allocation and increased costs.
Innovation Solution
A system generates a knowledge graph from historical project data, representing relationships between projects and skills, and uses semantic similarity metrics to identify matching previous projects for new ones, thereby determining the required personnel and generating a resource plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If workforce planning is performed without using knowledge graphs and semantic similarity metrics, then the process is simpler, but project matching accuracy and resource allocation efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-processing historical project data into structured knowledge graphs before actual project matching is needed. This includes extracting entities, relationships, and attributes from unstructured data, and pre-computing semantic similarity metrics, so that when a new project arrives, the matching process can quickly retrieve and compare against pre-organized knowledge without performing complex analysis in real-time.
Solution Approach 2:
The patent introduces knowledge graphs as an intermediary layer between raw historical project data and the project matching process. The knowledge graph serves as a mediator that transforms unstructured data into structured representations with defined schemas, entities, and relationships, enabling efficient semantic similarity computation without directly comparing raw data texts.
2Productivity
If manual methods are used to match projects and allocate resources, then the system is easier to implement, but productivity and scalability worsen
Solution Approach 1:
The patent replaces manual mechanical methods of project matching and resource allocation with automated computational systems. Instead of manually reviewing historical projects and assigning resources, the system uses natural language processing, knowledge graph construction, and semantic similarity algorithms to automatically match projects and recommend resource allocation, dramatically improving productivity and scalability.
Solution Approach 2:
The system transforms unstructured project data into structured parameters by extracting key attributes such as project description, skills required, timeline, budget, and outcomes. This parameterization enables systematic comparison and matching of projects based on quantifiable criteria rather than manual assessment, improving both efficiency and consistency in workforce planning.
3Measurement precision
If comprehensive historical data is analyzed for each new project, then matching accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary analysis of historical project data by pre-processing and organizing it into knowledge graphs before actual project matching is needed. This includes pre-extracting entities, relationships, and attributes from unstructured data, and pre-computing semantic similarity metrics, so that when a new project arrives, the matching process can quickly retrieve and compare against pre-organized knowledge without performing complex analysis in real-time.
Solution Approach 2:
The patent segments the comprehensive historical data into structured components within the knowledge graph, including separate entities for projects, skills, resources, and relationships between them. This segmentation allows the system to efficiently query and compare specific attributes (such as skills required) without processing the entire historical dataset, reducing time and computational resources while maintaining matching accuracy.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for workforce planning. The methods, systems, and apparatus include actions of obtaining historical project data describing previous projects, generating a knowledge graph based at least on the historical project data, obtaining future project data describing a future project, identifying a particular previous project that matches the future project based at least on the knowledge graph and the data describing the future project, generating similarity scores between the previous projects and the future project, determining that the similarity score between the particular previous project and the future project satisfies a similarity threshold, identifying the particular previous project as matching the future project, identifying personnel for the future project based at least on the skills needed for the particular previous project, and generating a resource plan for the future project based at least on the identified personnel.