Research And Application Of Competency In Computer Field Based On Knowledge Graph

With the rapid development of the network and artificial intelligence,the computer field has entered a high-speed development era,and enterprises are recruiting more talents in the computer field more frequently,and universities are paying more and more attention to the talent training in the computer field.However,the job matching process is mainly based on keyword search.The returned recruitment information is scattered.The knowledge and skills required for the job cannot be fully displayed,which will affect job applicants' perception of the job and even career choices.In order to effectively solve the above problems,academia and industry provide objective scientific basis for recruitment and job search by defining the post competency model.In the paper,research will be carried out based on the characteristics of the computer field,and a knowledge graph will be constructed based on the recruitment data on the job site.The knowledge and skills required for different types of jobs will be extracted as post competency.Finally,the research and development application platform provides a semantic search service for recruitment information based on knowledge graph and a comprehensive display of post competency.Demonstrate the knowledge and skills required for the job and the corresponding level of mastery.The main contribution of this paper can be summarized as follows:1.The knowledge graph data schema and semantic relationship of recruitment information in the computer field are defined.The data schema includes different types of entities,inter-entity relationships,entity attributes,etc.,and introduces knowledge and skill entities into them,trying to incorporate job competence elements in the knowledge graph.2.A knowledge graph of the computer field is constructed.Crawl the recruitment information data in the computer field on the Internet and construct a domain dictionary.Use a neural network algorithm that combines convolution and bidirectional long-term and short-term memory to extract the knowledge in the knowledge graph and store the integrated knowledge data in the Neo4 j graph database.The experimental results show that the knowledge extraction method in this paper has a higher F1 value,and the constructed knowledge graph has rich entities and semantics.3.A method for extracting job competency based on knowledge graph is proposed.Extend the semantics of recruitment requirements text based on the knowledge graph.Based on manual annotation and pre-training models,the BERT model is used to classify recruitment information in the computer field into different categories of posts.Then use word2 vec to complete the classification of degree words.Finally,based on the co-occurrence matrix,the ability of different categories of posts in the computer field is extracted.Dig deep into the knowledge and skills required for each type of position and the corresponding mastery level.Experiments show that the results of this method are more in line with the actual situation of job hunting,and have good reference significance for job seekers to fully understand the position.4.A computer-based job competency management platform based on knowledge graph is built.This paper integrates the knowledge graph and job competence data,and synchronizes the data to Elasticsearch.With high-quality full-text search of Elasticsearch,platform provide job seekers with a fast and rich recruitment information semantic retrieval service,and use the Vue.js framework to build a job information list and job competency display platform with graphical visualization.The knowledge graph construction technology and post competency extraction technology proposed in this paper performed well in experimental data.The case of semantic retrieval of recruitment information based on knowledge graph has a good performance in terms of speed and semantic relevance.The post competency displayed through the visualization of knowledge graph can comprehensively demonstrate the knowledge and skills required for different positions in the computer field.The established platform can provide high-quality recruitment information retrieval and job competency display for job seekers.