| With the development of social software, characterized by openness in applications, penetration of technology and interaction of information dissemination, more Web2.0 websites influence and change the way people work and study. Meanwhile, attributes of Web2.0, such as openness, user participation, rich user experience, and decentralization, make more people create information and issue them on the Internet. However, accompanied by proliferation of information sources and information, problems occur, for example, information is chaotic and its purity and credibility decreases, accuracy of search engine declines; and on the other hand, users hope to timely and accurately acquire what they need through interactive, collaborative and convenient ways. Faced with these problems, focusing on information filtering and personalized aggregation, Web3.0 emerges, whose core idea is personality, precision and intelligence. And its emergence brings new hope for solutions to these problems.As Web3.0 is on the basis of Web2.0, Folksonomy, developed under Web2.0 environment, is still one of the main methods for information classification. Though it could provide users with convenient, free tagging label and information retrieval; defects also exist, such as diversity, fuzziness and flat structure of tags, lack of semantic relations. These defects restrict realization of Web3.0's core idea, therefore, it is essential and of great importance to research on the optimization of Folksonomy considering new requirements of Web3.0.Based on this, comprehensively integrating multi-disciplinary theories, such as sociology, linguistics, mathematics and computer science, and with methods of empirical analysis, statistics, social network analysis and data mining, this paper fully uses Chinese corpus to study the optimization of Folksonomy. The main content includes eight chapters:In chapter 1, this paper illustrates the background of selecting the topic, the research status and the research significance, and then puts forward the research purpose and content, and introduces the research methods and ideas, giving the main innovation points of the research.In chapter 2, it is a brief review of correlation theory involved in the research. Firstly, it illustrates the definition and connotation, the operating mechanism, the types and the basic features of folksonomy; Then, it gives an inductive introduction of the basic ideology, architecture of semantic web and ontology; And then, it analyses and summarizes the emergence, connotation, features, supporting technology and status of Web3.0; Lastly, it analyses the relationship of folksonomy, semantic web and Web3.0, providing an orientation for the following study.In chapter 3, this paper firstly analyses the connotation and the features of tags. Through the typical Chinese web 2.0 website, it carries out the empirical research of tags, and mainly focus on the language features of tags, the distribution rule of tags, the relationship of tags, users and resource, the quality and criterion of tags, the taxonomy of tags and the recommendation of tags. Consequently, it finds out the operating mechanism and the deficiencies of folksonomy, providing basis for the formation of train of thought in the following research.In chapter 4, this paper firstly compares and analyses the similarities and differences between folksonomy and controlled language of information retrieval. Then it illustrates the construction method of the tag library, meanwhile, it introduces the Chinese semantic dictionary Tongyici Cilin into the construction method of the tag library, standardizing the terms in the tag library by comparing similarities between terms. And then, it discusses the control of user annotation from several aspects such as the recommendation of tags, the user management mechanism and the processing of spam tag. At last, it analyses the optimum selecting mechanism of user tags. The research purpose of this chapter is to improve the quality of tags, so as to lay the foundation for mining the semantics between tags.In Chapter 5, firstly, I analyze the automatic categorization process of tag resources in detail and construct the algorithm model of the automatic categorization of Tag resources. In the automatic classification model of tag resources, I borrow the thought of automatic text categorization to use the tag frequency as vector space of resources, and (?)e in the synonym words to express the semantics of vector as to improve the accuracy of the automatic classification; then, I study on how to use the tag Library to construct the tag hierarchy, and through the content management system Drupal, I introduce the realization method of the tag hierarchy; at last, I analyze the thought of facet match. This chapter mainly research on the combination of the traditional classification thoughts and the construction of tag hierarchy to provide tags and navigation for users and improve the retrieval efficiency.In chapter 6, at first, I study on the clustering analysis and related algorithm; at second, I discuss the automatic clustering of tag or tag resources from the aspects of tag co-occurrence analysis, the vector expressions of tag and association rules mining. On the choose of clustering algorithm, I take advantages of different clustering algorithm comprehensively, use different clustering algorithm to different data, and do theoretical verification on data set of tag sample to prove the feasibility of these algorithms. This part of the research lays a solid foundation for the combination of ontology and tag.In chapter 7,I firstly discuss and analyze the construction of the concept space of tags, and apply different algorithms to the construction of the hierarchy space and mesh space of tags respectively. And I testify its availability and validity with examples; then, I analyze mapping mechanism and methods of the tag and ontology, and in the process of conceptual matching, I absorb another Chinese semantic dictionary CNKI to compare the similarity between concepts to increase the reliability of the matching degree; lastly, from the semantic control of tags, I put forward the concept of tag ontology model and compare the current typical tag ontology model closely. At the same time, taking SIOC ontology model as an example, I introduce the semantic control process of tags. The main part of this chapter is about the extraction of semantic relationship of tags, and the results increase the process of the final implementation of semantic web (Web3.0).Chapter 8 summarizes the main research contents, conclusions and innovations of the paper, analyzes the insufficiency during the research, and finally, looks forward to the future research focus and direction.
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