Project Information Access Using NLP for Accurate Expert Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information retrieval systems in large organizations face challenges such as time-consuming content maintenance, insufficient granularity in employee profiles, inconsistency across departments, and reliance on manual skill descriptors, leading to inefficient communication and information retrieval.
Innovation Solution
A project information access system using an electronic computing device that automatically matches various formulations of information requests to relevant resources by employing natural language processing and machine learning algorithms, providing two-way communication and matching project information data with worker contact details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If wikis and departmental websites are used for information retrieval, then information accessibility is improved, but time expense for content maintenance increases
Solution Approach 1:
The system automatically crawls and extracts information from multiple sources including wikis, departmental websites, and employee profiles without requiring manual content maintenance. The automated information aggregation and matching system serves itself by continuously updating the knowledge base without human intervention, thus improving information accessibility while eliminating maintenance time expense
Solution Approach 2:
The system pre-processes and structures information from various organizational sources in advance, creating a ready-to-query knowledge base. By performing information extraction, normalization, and indexing beforehand, the system eliminates the need for reactive content maintenance while ensuring information is immediately accessible when queries are submitted
2Ease of manufacture
If employee profiles use general skill descriptors, then profile creation is simplified, but profile distinguishability decreases
Solution Approach 1:
The system segments employee expertise into hierarchical categories: broad skill areas, specific technologies, and detailed project experiences. This segmentation allows profiles to be created with simple descriptors while the system automatically breaks down general skills into granular components for precise matching, thereby maintaining ease of profile creation while achieving high distinguishability
Solution Approach 2:
The system adds temporal and contextual dimensions to skill descriptors by tracking when employees applied specific skills and in what project contexts. This transforms static, one-dimensional skill tags into multi-dimensional expertise profiles that are both easy to create and highly distinguishable for matching purposes
3Adaptability or versatility
If departments maintain separate websites with different layouts and jargon, then departmental autonomy is preserved, but inter-departmental communication becomes inconsistent
Solution Approach 1:
The system creates a universal communication layer that operates across all departmental websites simultaneously. The standardized query interface and unified matching algorithm work consistently across diverse departmental structures, preserving each department's autonomy while ensuring consistent information retrieval experiences throughout the organization
Solution Approach 2:
The system acts as an intermediary layer between users and diverse departmental websites. It translates various departmental jargons and layouts into a unified understanding through automated information extraction and normalization, thereby maintaining departmental independence while ensuring communication consistency across the entire organization
4Ease of operation
If search bars are used for information retrieval, then keyword-based searching is enabled, but retrieval accuracy decreases due to wording sensitivity
Solution Approach 1:
The system replaces the mechanical keyword-matching mechanism of traditional search bars with semantic understanding based on natural language processing and machine learning. This substitution maintains the simplicity of user input while dramatically improving retrieval accuracy by understanding the intent and context behind queries rather than merely matching keywords
Solution Approach 2:
The system changes the matching parameters from exact keyword correspondence to semantic similarity and contextual relevance. By transforming the matching criteria from rigid textual equality to flexible semantic equivalence, the system preserves ease of operation while achieving high retrieval accuracy even when query wording varies
Data Source
Figure 1
Figure 2
AI summary
The invention relates to a method for providing a project information access system (14) by an electronic computing device (10). The method comprises the steps of: providing at least one project information datum (18) by the electronic computing device (10), where each of the at least one project information datum (18) is associated with at least one worker contact detail (20); providing a communication platform (26) by the electronic computing device (10) for two-way communication with a user (28) of the project information access system (14); receiving a project information request (34) from the user (28) by the electronic computing device (10) via the communication platform (26); matching the project information request (34) by the electronic computing device (10) to at least one matching datum (38) of the at least one project information datum (18); and providing the at least one matching datum (38) and/or at least one matching worker contact detail (40) corresponding to the at least one matching datum (38) to the user (28) via the communication platform (26) by the electronic computing device (10), depending on the project information request (34).