An automatic skills standardization method based on subject expert knowledge extraction and semantic matching

Bernabé-Moreno, Juan; Tejeda-Lorente, Álvaro; Herce-Zelaya, Julio; Porcel, Carlos; Herrera‐Viedma, Enrique

doi:10.1016/j.procs.2019.12.060

Cited by 9 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to map employees' competencies, they use radar charts to visualize their mapping. In another work, Bernabé-Moreno et al (2019) propose natural language processing (NLP) and semantic matching as powerful methods to extract skills for any job automatically in order to empower human resource management. These studies underscore the hypothesis presented in this article, advocating the significance of competency mapping as a tool for communities of practice within consulting firms to enhance KM.…”

Section: Intelligent Competency Mapping Modelsmentioning

confidence: 99%

Intelligent competency mapping for improving knowledge management in consulting firms

Mirafzal,

Fhal,

Wadhera

et al. 2024

Proc. Des. Soc.

View full text Add to dashboard Cite

This study explores the application of competency mapping models, incorporating in knowledge management for consulting firms. It evaluates 15 different models, focusing on their suitability for consulting contexts based on data collection, advantages, risks, and limitations. The findings indicate that AI and ML-enhanced competency mapping models are particularly more effective in consulting firms. Finally, the article proposes three key applications of these models for improving knowledge management in consulting firms via empowering communities and collaboration.

show abstract

Section: Intelligent Competency Mapping Modelsmentioning

confidence: 99%

Intelligent competency mapping for improving knowledge management in consulting firms

Mirafzal,

Fhal,

Wadhera

et al. 2024

Proc. Des. Soc.

View full text Add to dashboard Cite

show abstract

“…Configuration e-commerce data description text features compared with ordinary text, IED configuration data description text usually involves e-commerce proper nouns [ 14 ]. Misdivision of words is easy to occur in the word segmentation stage, which leads to misclustering of word vectors by the language model [ 15 ].…”

Section: E-commerce Text Mining Under Big Datamentioning

confidence: 99%

Influence of online E-commerce interaction on consumer satisfaction based on big data algorithm

Yuan

Tian

2023

Heliyon

View full text Add to dashboard Cite

“…P represents the number of times the word appears in the corpus, and represents the probability of the word appearing in the context of the word [29]. Assuming that the word vector of the word sum is known, the similarity is calculated [30]. When the difference is small, it is proved that the word vector and the cooccurrence matrix are more consistent, and the word vector can accurately grasp the context information:…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Semantic Matching Efficiency of Supply and Demand Text on Cross‐Border E‐Commerce Online Technology Trading Platforms

Chen

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

With the innovation of global trade business models, more and more foreign trade companies are transforming and developing in the direction of cross-border e-commerce. However, due to the limitation of platform language processing and analysis technology, foreign trade companies encounter many bottlenecks in the process of transformation and upgrading. From the perspective of the semantic matching efficiency of e-commerce platforms, this paper improves the logical and technical problems of cross-border e-commerce in the operation process and uses semantic matching efficiency as the research object to conduct experiments on the QQP dataset. We propose a graph network text semantic analysis model TextSGN based on semantic dependency analysis for the problem that the existing text semantic matching method does not consider the semantic dependency information between words in the text and requires a large amount of training data. The model first analyzes the semantic dependence of the text and performs word embedding and one-hot encoding on the nodes (single words) and edges (dependencies) in the semantic dependence graph. On this basis, in order to quickly mine semantic dependencies, an SGN network block is proposed. The network block defines the way of information transmission from the structural level to update the nodes and edges in the graph, thereby quickly mining semantics dependent information allows the network to converge faster, train classification models on multiple public datasets, and perform classification tests. The experimental results show that the accuracy rate of TextSGN model in short text classification reaches 95.2%, which is 3.6% higher than the suboptimal classification method; the accuracy rate is 86.16%, the F 1 value is 88.77%, and the result is better than other methods.

show abstract

An automatic skills standardization method based on subject expert knowledge extraction and semantic matching

Cited by 9 publications

References 6 publications

Intelligent competency mapping for improving knowledge management in consulting firms

Intelligent competency mapping for improving knowledge management in consulting firms

Influence of online E-commerce interaction on consumer satisfaction based on big data algorithm

Semantic Matching Efficiency of Supply and Demand Text on Cross‐Border E‐Commerce Online Technology Trading Platforms

Contact Info

Product

Resources

About