**5. Conclusion**

**4.6. The model for user recommendation**

206 Scientometrics

mendations is ultimately achieved, as shown in **Figure 8**.

the sequence and the concept has drifted over time.

**Figure 8.** The model for user recommendation.

This system uses the user registration information, the user need investigation, and the user log flow to obtain the user knowledge base. The user-related knowledge is analyzed, and the feature extraction is carried out. The users' requirement ontology is constructed and mapped with the domain ontology and users' characteristics. The mapping relationship is established, and the knowledge is organically related. This system uses the user knowledge base for knowledge extraction. The knowledge resources are classified and semantic associations are created. Entities are stored in the user requirement base, and ontology based on user recom-

In actual cases, data is usually collected in an ordered sequence. The distribution of the data is not static but changes over time. As certain factors change due to environmental factors, the regular pattern that the data has followed also changes; this is known as a concept change. The concept of "concept drift" [38] is that the rules that the data follows have changed throughout

Because the users' actual operation is uncontrollable and does not follow any existing model, any new factors may have an impact on the users' operation, and concept drift in the users' data acquisition process is inevitable; therefore, the user model requires regular evolution [39].

In this chapter, we proposed a new citation analysis framework based on ontology and linked data. By combining these technologies into a new semantic web with citation analysis method, we were able to improve the traditional citation analysis method (which relies heavily on citation databases). Rapid advancements in semantic publishing [40] and projects like the open citation corpus [41] have made it possible to mark massive amounts of citation information as machine-readable RDF triples. In the future, we plan to design further experiments to verify the feasibility of the proposed method. We hope that introducing ontology and linked data into citation analysis will yield optimal results while facilitating new technological developments and innovations.

An ontology-based citation knowledge service system uses ontology technology, knowledge navigation, knowledge recommendation, and other technologies and methods to organize, store, and query data in a machine-readable mode. It can successfully search knowledge across resource types and databases. Through the semantic relevance and knowledge navigation of various resources, we can render resources more granular, standardized, and automated. Using the methods of concept drift to track changes in users' needs achieves their information needs, and knowledge integration services provide users with more personalized and comprehensive services. This chapter constructs a framework of an ontology-based citation knowledge service system, aiming to provide new ideas for the development of knowledge services offered by traditional citation retrieval systems. We will focus on the realization of an ontology-based citation knowledge service system in the near future.
