**4.4. Functional layer**

The functional layer provides the ontology-based citation knowledge service function, which currently includes a personal center module, a platform management module, and a knowledge service module.

The personal center provides new user registration and user data modification function. Platform management is the function of monitoring the entire knowledge service system that is used to operate the knowledge bases and databases. It mainly includes two modules: a knowledge base management module and a database management module. The knowledge service module includes the core functions of the knowledge service system, including ontology-based knowledge retrieval, knowledge navigation, and knowledge push modules.

#### **4.5. The model for citation knowledge base**

The citation knowledge base provides data protection used for the construction of the domain ontology base, knowledge retrieval, knowledge recommendation, and other knowledge services. It stores the information about the citation resources gleaned during the knowledge acquisition. In addition to providing the user with the basis of the information, the citation knowledge base also contains other relevant information, such as the authors' personal pro-

The user database contains the registration information of the users. This system carries out a user demand investigation when the user registers. It can add user preferences and extract as a conceptual feature the input word phrase(s) of the user. It also performs ontological

The ontology base contains the lightweight cube citation ontology base, the domain ontology base, and the user requirement ontology base. It simplifies the entity level, builds a convenient, simple citation ontology, organizes, stores, and queries data in a machine-readable mode. For example, the terms "dc: title," "fabio: hasTranslatedTitle," "bibo: pageStart," and "bibo: pageEnd," respectively, define the title of the journal, the English title, the start page, and other related attributes. These describe the citation in detail and realize the knowledge

The domain ontology base contains the domain ontology, which includes class, property, and instance of domain ontology, as well as the ontological semantics of citation resources. Song and Zhang [37] agree that ontology can represent the complex semantic relations in the content of the information resources; it has a solid concept of hierarchical structure that supports

User requirements ontology conducts user need surveys for users and obtains user preferences directly. It analyzes users' search behavior, retrieves content, analyzes the users' prefer-

The semantic association layer will mainly analyze the content and related characteristics of the data, using Jena as the core processing tool, based on the pre-built domain ontology model. The information in the citation knowledge base is marked by Jena and uses Jena for reasoning. Finally, the SPARQL language is used to retrieve the information that has been marked. The semantic layer is based on the user requirement ontology and the user database

The functional layer provides the ontology-based citation knowledge service function, which currently includes a personal center module, a platform management module, and a knowl-

logical reasoning. It is helpful for us to organize and retrieve information.

ences, obtains the user database, and builds the users' need ontology.

and implements user requirements through scenario reasoning.

file, which allows it to reduce secondary retrieval.

mapping.

204 Scientometrics

**4.2. Ontology layer**

association of the citation information.

**4.3. Semantic association layer**

**4.4. Functional layer**

edge service module.

The functions of the proposed knowledge base include the following model: collecting literature from the citation database; extracting other relevant information from the authors' home page and organization page and other information carriers; introducing lightweight cube citation ontology to extract consensus citation elements; simplifying the entity level; organizing, storing, and querying data in a machine-readable mode to produce a list of concept features; establishing the relationship between the concept feature list and the domain ontology map; associating the citation knowledge data with the domain knowledge; performing the semantic processing of information after the semantic annotation, expansion, and synthesis; using ontology for formalization; mining implicit semantics through semantic reasoning; and forming a citation knowledge base ultimately. The model for citation knowledge base is shown in **Figure 7**.

**Figure 7.** The model for citation knowledge base.

#### **4.6. 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 recommendations is ultimately achieved, as shown in **Figure 8**.

In order to reduce the effect of concept drift on the prediction effect, a triggering mechanism can be used to detect conceptual drift. Such change detection is based on statistics. It tracks the process of change in the user need concept set, removing the old data and re-adding the

The Impact on Citation Analysis Based on Ontology and Linked Data

http://dx.doi.org/10.5772/intechopen.76377

207

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

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

This work was supported by a grant from the national social science foundation of China (No.

of an ontology-based citation knowledge service system in the near future.

detected data to the users' requirement base to improve the prediction accuracy.

**5. Conclusion**

and innovations.

**Acknowledgements**

16BTQ073).

**Author details**

Ming Xiao\*, Zeshun Shi and Shanshan Wang

\*Address all correspondence to: ming\_xiao@bnu.edu.cn

School of Government, Beijing Normal University, Beijing, P.R. China

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 the sequence and the concept has drifted over time.

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].

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

In order to reduce the effect of concept drift on the prediction effect, a triggering mechanism can be used to detect conceptual drift. Such change detection is based on statistics. It tracks the process of change in the user need concept set, removing the old data and re-adding the detected data to the users' requirement base to improve the prediction accuracy.
