Research And Application Of Standardized Knowledge Service In Book Publishing

Standards,as an important document that regulates people's production activities in social life,play an important role in promoting the development of the industry.The news and publishing industry is booming,and the number of industry standards for book publishing have gradually increased.With a large number of industry standards for book publishing,we have fully utilized knowledge engineering methods and knowledge service tools to mine the knowledge in the standards and organized the knowledge reasonably so that the standard content is structured and digitized,and standard retrieval efficiency is improved.It will help speed up the process of knowledge sharing within the industry,promote the dissemination of knowledge,and promote the progress and development of the industry.This article takes the book publishing industry standard as the entry point,explores standard structured storage solutions,knowledge extraction methods and knowledge fusion technologies,and builds a standardized service platform for book publishing.First,it analyzes the industry standards of book publishing in depth and designs a structured storage solution.Then,a multi-strategy knowledge extraction scheme is proposed for the industry standards of book publishing to ensure the accuracy and coverage of the extracted results.At the same time,the K-means clustering algorithm based on the longest distance to select the initial clustering center is proposed and applied to the knowledge fusion process.Finally,a professional vocabulary and knowledge ontology database in the field of book publishing was established,and a standardized knowledge service system in the field of book publishing was designed and implemented to visualize the industry knowledge of book publishing.This paper improves the traditional clustering algorithm,effectively avoids the dependence of the traditional K-means algorithm on the initial clustering center selection,reduces the number of iterations,reduces the algorithm running time,and improves the algorithm's comprehensive optimization level.The experimental results show that the knowledge obtained after extraction can find synonyms and near-knowledge knowledge through improved clustering algorithm,eliminate knowledge redundancy,and achieve the goal of knowledge fusion.The establishment of a standard knowledge service platform can change the current status of manually retrieving standard knowledge and establish standard content relationships to build an industry standard knowledge network system.