AUGUR: Forecasting the Emergence of New Research Topics

Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall

Tracing the evolution of digitalisation research in business and management fields: Bibliometric analysis, topic modelling and deep learning trend forecasting

Research on digitalisation trends and digital topics has become one of the most prolific streams of research within the fields of business and management during the course of the past few years. The purpose of this study is to provide a general picture of the intellectual structure and the conceptual space of this research realm. To this purpose, 6067 publications related to digital topics, indexed in the business and management categories of Web of Science (WoS), and dated from 1990 to 2020 are explored based on the approaches of bibliometric analysis, topic modelling and trend forecasting. The results of the bibliometric analysis comprise insights into the publication and citation structure, the most productive authors, the most productive universities, the most productive countries, the most productive journals, the most cited studies and the most prevalent themes and sub-themes on digitalisation in business and management. In addition, the outcomes of the topic modelling give new knowledge on the latent topical structure along with the rising, falling and fluctuating trends of this literature. In addition, the results of the trend forecasting enable readers to have a glimpse of how the underlying trends of the literature will probably change within the next years until 2025. These results provide guidance and orientation for both academics and practitioners who are initiating or currently developing their efforts in this discipline.info:eu-repo/semantics/acceptedVersio

Pragmatic Ontology Evolution: Reconciling User Requirements and Application Performance

Increasingly, organizations are adopting ontologies to describe their large catalogues of items. These ontologies need to evolve regularly in response to changes in the domain and the emergence of new requirements. An important step of this process is the selection of candidate concepts to include in the new version of the ontology. This operation needs to take into account a variety of factors and in particular reconcile user requirements and application performance. Current ontology evolution methods focus either on ranking concepts according to their relevance or on preserving compatibility with existing applications. However, they do not take in consideration the impact of the ontology evolution process on the performance of computational tasks – e.g., in this work we focus on instance tagging, similarity computation, generation of recommendations, and data clustering. In this paper, we propose the Pragmatic Ontology Evolution (POE) framework, a novel approach for selecting from a group of candidates a set of concepts able to produce a new version of a given ontology that i) is consistent with the a set of user requirements (e.g., max number of concepts in the ontology), ii) is parametrised with respect to a number of dimensions (e.g., topological considerations), and iii) effectively supports relevant computational tasks. Our approach also supports users in navigating the space of possible solutions by showing how certain choices, such as limiting the number of concepts or privileging trendy concepts rather than historical ones, would reflect on the application performance. An evaluation of POE on the real-world scenario of the evolving Springer Nature taxonomy for editorial classification yielded excellent results, demonstrating a significant improvement over alternative approaches

Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions

Artificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With the proliferation of digital computing devices and the emergence of big data, AI is increasingly offering significant opportunities for society and business organizations. The growing interest of scholars and practitioners in AI has resulted in the diversity of research topics explored in bulks of scholarly literature published in leading research outlets. This study aims to map the intellectual structure and evolution of the conceptual structure of overall AI research published in Technological Forecasting and Social Change (TF&SC). This study uses machine learning-based structural topic modeling (STM) to extract, report, and visualize the latent topics from the AI research literature. Further, the disciplinary patterns in the intellectual structure of AI research are examined with the additional objective of assessing the disciplinary impact of AI. The results of the topic modeling reveal eight key topics, out of which the topics concerning healthcare, circular economy and sustainable supply chain, adoption of AI by consumers, and AI for decision-making are showing a rising trend over the years. AI research has a significant influence on disciplines such as business, management, and accounting, social science, engineering, computer science, and mathematics. The study provides an insightful agenda for the future based on evidence-based research directions that would benefit future AI scholars to identify contemporary research issues and develop impactful research to solve complex societal problems

Detecting Emerging Technologies in Artificial Intelligence Scientific Ecosystem Using an Indicator-based Model

Early identification of emergent topics is of eminent importance due to their potential impacts on society. There are many methods for detecting emerging terms and topics, all with advantages and drawbacks. However, there is no consensus about the attributes and indicators of emergence. In this study, we evaluate emerging topic detection in the field of artificial intelligence using a new method to evaluate emergence. We also introduce two new attributes of collaboration and technological impact which can help us use both paper and patent information simultaneously. Our results confirm that the proposed new method can successfully identify the emerging topics in the period of the study. Moreover, this new method can provide us with the score of each attribute and a final emergence score, which enable us to rank the emerging topics with their emergence scores and each attribute score

Scientometric Analysis of Technology & Innovation Management Literature

The management of technology and innovation has become an attractive and promising field within the management discipline. Therefore, much insight can be gained by reviewing the Technology & Innovation Management (TIM) research in leading TIM journals to identify and classify the key TIM issues by meta-categories and to identify the current trends. Based on a comprehensive scientometric analysis of 5,591 articles in 10 leading TIM specialty journals from 2005 to 2014, this research revealed several enlightening findings. First, the United States is the major producer of TIM research literature, and the greatest number of papers was published in Research Policy. Among the researchers in the field, M. Song is the most prolific author. Second, the TIM field often plays a bridging role in which the integration of ideas can be grouped into 10 clusters: innovation and firms, new product development (NPD) and marketing strategy, project management, patenting and industry, emerging technologies, science policy, social networks, system modeling and development, business strategy, and knowledge transfer. Third, the connectivity among these terms is highly clustered and a network-based perspective revealed that six new topic clusters are emerging: NPD, technology marketing, patents and intellectual property rights, university-industry cooperation, technology forecasting and roadmapping, and green innovation. Finally, chronological trend analysis of key terms indicates a change in emphasis in TIM research from information systems/technologies to the energy sector and green innovation. The results of the study improve our understanding of the structure of TIM as a field of practice and an academic discipline. This insight provides direction regarding future TIM research opportunities

Discovering Computer Science Research Topic Trends using Latent Dirichlet Allocation

Before conducting a research project, researchers must find the trends and state of the art in their research field. However, that is not necessarily an easy job for researchers, partly due to the lack of specific tools to filter the required information by time range. This study aims to provide a solution to that problem by performing a topic modeling approach to the scraped data from Google Scholar between 2010 and 2019. We utilized Latent Dirichlet Allocation (LDA) combined with Term Frequency-Indexed Document Frequency (TF-IDF) to build topic models and employed the coherence score method to determine how many different topics there are for each year’s data. We also provided a visualization of the topic interpretation and word distribution for each topic as well as its relevance using word cloud and PyLDAvis. In the future, we expect to add more features to show the relevance and interconnections between each topic to make it even easier for researchers to use this tool in their research projects

PICES Press, Vol. 17, No. 1, January 2009

Major Outcomes from the 2008 PICES Annual Meeting: A Note from the Chairman (pdf, 0.1 Mb) PICES Science – 2008 (pdf, 0.1 Mb) 2008 PICES Awards (pdf, 0.3 Mb) Charles B. Miller – A Selective Biography (pdf, 0.4 Mb) Latest and Upcoming PICES Publications (pdf, 0.1 Mb) 2008 OECOS Workshop in Dalian (pdf, 0.2 Mb) PICES Calendar (pdf, 0.1 Mb) 2008 PICES Workshop on “Climate Scenarios for Ecosystem Modeling (II)” (pdf, 0.1 Mb) PICES/ESSAS Workshop on “Marine Ecosystem Model Inter-Comparisons” (pdf, 0.2 Mb) Highlights of the PICES Seventeenth Annual Meeting (pdf, 0.5 Mb) 2008 PICES Summer School on “Ecosystem-Based Management” (pdf, 0.3 Mb) 4th PICES Workshop on “The Okhotsk Sea and Adjacent Areas” (pdf, 0.2 Mb) PICES WG 21 Rapid Assessment Surveys (pdf, 0.4 Mb) PICES Interns (pdf, 0.3 Mb) PICES @ Oceans in a High CO2 World (pdf, 0.1 Mb) Coping with Global Change in Marine Social–Ecological Systems: An International Symposium (pdf, 0.1 Mb) The State of the Western North Pacific in the First Half of 2008 (pdf, 1.3 Mb) State of the Northeast Pacific through 2008 (pdf, 0.3 Mb) The Bering Sea: Current Status and Recent Events (pdf, 0.2 Mb) An Opinion Born of Years of Observing Timeseries Observations (pdf, 0.1 Mb) New Chairman for the PICES Fishery Science Committee (pdf, 0.1 Mb