Abstract: The personnel departments of organizations are always confronted with the challenge of efficiently finding out suitable candidates from massive resumes.Some business departments usually express their demand for talents as tags,such as “with rich search engine development experience”,“graduated from 985 universities” and so on.These requirements cannot be done by SQL queries or keyword search.To fill this gap,this paper proposed a resume analysis and search method based on semantic Web.By the method of domain knowledge base auxiliary information extraction,resume information is semantically analyzed and labels for suitable candidates are automatically generated.In addition,on a large personnel scale,we proposed a method of multi-level cache by which the performance has been greatly improved.Meanwhile,it is applied to nearly ten thousand personnel agency resumes and experiments show the effectiveness.

Key words: Semantic Web,RDF,SPARQL,Performance optimization