Multi-Feature Quality Evaluation Model for Enterprise Knowledge
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Solution Overview
Problem
Existing knowledge recommendation systems in enterprises face challenges in accurately evaluating the quality of knowledge documents, often recommending low-quality content due to the uneven quality of internal documents and the lack of effective multi-dimensional feature analysis.
Innovation Solution
A method and apparatus for constructing a quality evaluation model that acquires knowledge content samples in various forms, extracts statistical, semantic, and image features, and constructs a quality evaluation model to accurately recommend high-quality knowledge by utilizing these multi-dimensional features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If statistical features only are used for knowledge quality evaluation, then the evaluation process is simple, but the recommendation accuracy is low
Solution Approach 1:
The patent transitions from single-dimensional statistical feature evaluation to multi-dimensional feature evaluation by incorporating semantic features (text understanding), image features (visual content analysis), and statistical features. This dimensional expansion enables comprehensive quality assessment of knowledge documents, resolving the contradiction between evaluation simplicity and recommendation accuracy.
Solution Approach 2:
The patent creates a composite evaluation model that integrates multiple feature types (statistical, semantic, image) analogous to composite materials. Each feature type contributes different evaluation dimensions, and their combination produces a more accurate and robust quality assessment than any single feature type alone, thereby improving recommendation accuracy while maintaining systematic evaluation structure.
2Measurement precision
If multi-dimensional features are extracted and used, then the recommendation accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent segments the knowledge document processing into distinct feature extraction modules: statistical feature extraction, semantic feature extraction, and image feature extraction. Each module handles a specific type of feature independently, which organizes the complex processing task into manageable segments. This segmentation reduces processing complexity by providing clear modular boundaries while maintaining the benefits of multi-dimensional feature analysis for accurate recommendations.
Data Source
AI summary
Embodiments of the present disclosure disclose a method and apparatus for constructing a quality evaluation model, an electronic device and a computer-readable storage medium. A specific implementation mode of the method comprises: acquiring samples of knowledge contents; extracting statistical features, semantic features, and image features respectively from the samples of knowledge contents; and constructing a quality evaluation model for knowledge according to the statistical features, the semantic features, and the image features. On the basis of the prior art, this implementation mode additionally uses semantic features and image features of knowledge contents to construct a more accurate quality evaluation model based on multi-dimensional features that characterize the actual quality of a knowledge, which may well discover some brief but very useful summary knowledge in an enterprise and may recommend high-quality knowledge more accurately for employees in the enterprise.