Mapping the technology evolution path: a novel model for dynamic topic detection and tracking

Discovery of topic evolution path and semantic relationship based on patent entity representation

Purpose

This paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.

Design/methodology/approach

Firstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.

Findings

In the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.

Originality/value

This work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.

A Review of Technological Forecasting from the Perspective of Complex Systems

Technology forecasting (TF) is an important way to address technological innovation in fast-changing market environments and enhance the competitiveness of organizations in dynamic and complex environments. However, few studies have investigated the complex process problem of how to select the most appropriate forecasts for organizational characteristics. This paper attempts to fill this research gap by reviewing the TF literature based on a complex systems perspective. We first identify four contexts (technology opportunity identification, technology assessment, technology trend and evolutionary analysis, and others) involved in the systems of TF to indicate the research boundary of the system. Secondly, the four types of agents (field of analysis, object of analysis, data source, and approach) are explored to reveal the basic elements of the systems. Finally, the visualization of the interaction between multiple agents in full context and specific contexts is realized in the form of a network. The interaction relationship network illustrates how the subjects coordinate and cooperate to realize the TF context. Accordingly, we illustrate suggest five trends for future research: (1) refinement of the context; (2) optimization and expansion of the analysis field; (3) extension of the analysis object; (4) convergence and diversification of the data source; and (5) combination and optimization of the approach.

Advanced Technology Evolution Pathways of Nanogenerators: A Novel Framework Based on Multi-Source Data and Knowledge Graph

As an emerging nano energy technology, nanogenerators have been developed rapidly, which makes it crucial to analyze the evolutionary pathways of advanced technology in this field to help estimate the development trend and direction. However, some limitations existed in previous studies. On the one hand, previous studies generally made use of the explicit correlation of data such as citation and cooperation between patents and papers, which ignored the rich semantic information contained in them. On the other hand, the progressive evolutionary process from scientific grants to academic papers and then to patents was not considered. Therefore, this paper proposes a novel framework based on a separated three-layer knowledge graph with several time slices using grant data, paper data, and patent data. Firstly, by the representation learning method and clustering algorithm, several clusters representing specific technologies in different layers and different time slices can be obtained. Then, by calculating the similarity between clusters of different layers, the evolutionary pathways of advanced technology from grants to papers and then to patents is drawn. Finally, this paper monitors the pathways of some developed technologies, which evolve from grants to papers and then to patents, and finds some emerging technologies under research.

A Multi-RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency-Based Scientific Influence Modeling

Computer science discipline includes many research fields, which mutually influence and promote each other's development. This poses two great challenges of predicting the research topics of each research field. One is how to model fine-grained topic representation of a research field. The other is how to model research topic of different fields and keep the semantic consistency of research topics when learning the scientific influence context from other related fields. Unfortunately, the existing research topic prediction approaches cannot handle these two challenges. To solve these problems, we employ multiple different Recurrent Neural Network chains which model research topics of different fields and propose a research topic prediction model based on spatial attention and semantic consistency-based scientific influence modeling. Spatial attention is employed in field topic representation which can selectively extract the attributes from the field topics to distinguish the importance of field topic attributes. Semantic consistency-based scientific influence modeling maps research topics of different fields to a unified semantic space to obtain the scientific influence context of other related fields. Extensive experiment results on five related research fields in the computer science (CS) discipline show that the proposed model is superior to the most advanced methods and achieves good topic prediction performance.

Tracking and analysis of discourse dynamics and polarity during the early Corona pandemic in Iran

It has not been long since a new disease called COVID-19 has hit the international community. Unknown nature of the virus, evidence of its adaptability and survival in new conditions, its widespread prevalence and also lengthy recovery period, along with daily notifications of new infection and fatality statistics, have created a wave of fear and anxiety among the public community and authorities. These factors have led to extreme changes in the social discourse in a rather short period of time. The analysis of this discourse is important to reconcile the society and restore ordinary conditions of mental peace and health. Although much research has been done on the disease since its international pandemic, the sociological analysis of the recent public phenomenon, especially in developing countries, still needs attention. We propose a framework for analyzing social media data and news stories oriented around COVID-19 disease. Our research is based on an extensive Persian data set gathered from different social media networks and news agencies in the period of January 21-April 29, 2020. We use the Latent Dirichlet Allocation (LDA) model and dynamic topic modeling to understand and capture the change of discourse in terms of temporal subjects. We scrutinize the reasons of subject alternations by exploring the related events and adopted practices and policies. The social discourse can highly affect the community morale and polarization. Therefore, we further analyze the polarization in online social media posts, and detect points of concept drift in the stream. Based on the analyzed content, effective guidelines are extracted to shift polarization towards positive. The results show that the proposed framework is able to provide an effective practical approach for cause and effect analysis of the social discourse.