Subjects: Library Science，Information Science >> Library Science submitted time 2023-07-26 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] The article aims to explore the factors and their mechanisms influencing the generation of co-word network for interdisciplinary field, and to reveal micro-level mechanisms of knowledge connection in interdisciplinary field.[Method/process] Borrowing network embedding theory, the article summarizes the factors into network structure factors (endogenous variables) and keywords' attribute factors (exogenous variables). Exponential random graph model is constructed based on these factors to perform an empirical analysis on the field of Medical Informatics.[Result/conclusion] The results show that the influence of network structure factors on the co-occurrence relationship generation is greater than that of keywords' attributes. Preferential attachment and transitive mechanism have significant positive effect. Keywords tend to be connected with the newer ones. In addition, the keywords of Medical Informatics tend to establish co-occurrence relations with the keywords from basic disciplines, while the keywords from basic disciplines tend to be connected with the keywords in their own disciplines. The conclusions are helpful to understand the formation process of knowledge systems in interdisciplinary fields and the interactions of interdisciplinary knowledge.
Subjects: Library Science，Information Science >> Library Science submitted time 2023-07-26 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] The Formation of patent citation is necessary to understand innovation networks. The independence assumption set by the Conventional regression model for observed objects cannot integrate the structural effect factors of the network into the model to provide comprehensive statistical inference. ERGMs (exponential random graph model) represent a methodological innovation of statistical inference for networks given their ability to model actor attributes along with endogenous self-organizational processes and exogenous network covariates.[Method/process] In this paper, ERGMs are applied to systematic inspect the five mechanisms affecting patent citation formation in a sample of Nelarabine drug. The five mechanisms contain main effect, difference effect of citation lag, and activity effect, transitivity effect and network covariates.[Result/conclusion] We find that five different types of mechanisms play diverse roles in patent citation formation. And three of effects among these mechanisms have significant impacts on citation formation of nelarabine drug:network covariates based on shared inventors and shared patent family membership, and transitivity effect. In addition, some aided mechanism play a supporting role on patent citation formation, such as difference of time lag, main effects of number of claims and reference.
Subjects: Library Science，Information Science >> Library Science submitted time 2023-07-26 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] Although there have been efforts of scholars to answer the question, what’s determinants of patent citation formation are not solved satisfactorily. Scholars find formation of patent citation is influenced by the structure characteristics of patent citation network. However, the current framework of statistical inference methods based on logistical regression is failing to incorporate the above factors, so an innovative method need to be introduced. [Method/process] From a tie formation perspective, patent citation formation represents three broad category of tie formation processes: attribute-based processes, self-organizing network processes and covariates processes. Furthermore, based on these processes, the paper establishes a mapping relationship between those processes with particular types of configurations. Finally, a framework is proposed for understanding the complexity of patent citation formation. [Result/conclusion] The paper introduces a framework for understanding patent citation formation, which lays the groundwork for statistical network modeling in the future. In addition, broadly network configuration selection from the framework offers significant opportunities to extend existing bibliometrics and open new pathways in complexity of scientific network analysis.
Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] FinTech has developed rapidly in the information and data era, and the number of patents has continued to increase. At the same time, its field crossover and blurred borders characteristics have also increased the difficulty of patent analysis. Therefore, it is necessary to construct a suitable identification and classification method, so as to accurately and efficiently process the continuously growing large volume data. [Method/process] This paper firstly sorted out the innovation categories based on the connotation and function of FinTech, and thus clarified the scope and boundaries of FinTech patents. Then, it constructed a FinTech patent recognition and classification model based on machine learning algorithms, combining text filtering and manual interpretation. [Result/conclusion] This paper proposes a patent recognition and classification process, based on machine learning algorithms, which is more accurately and efficiently. By analyzing the obtained FinTech patent classification data, the research also summarizes the current FinTech development status.
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