Analysis of Technology Trends Basedon Diverse Data Sources

Segev, Aviv; Jung, Sukhwan; Choi, Sukgeun

doi:10.1109/tsc.2014.2338855

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…This suggests the entities related to both technologies are experiencing initial hype with the public while the researchers have already passed this stage and show diminished interests in the same entities. Such differences are validated by the authors' previous research on the different time windows for technology growth curves in different datasets, where the technology's development starts with the academic domain and the public inherits the changes afterward [1], [18].…”

Section: Extracting Technology-related Entity Trendsmentioning

confidence: 59%

“…The authors' previous work utilized a combination of forecasts from various datasets to show that the predictive power of different forecasts varies based on the nature of the dataset used. Changes in technology term frequencies in public datasets such as news, books, and web pages are preceded by more academic datasets such as academic articles and patents, resulting in a longer predictive time window for technology growth prediction [1].…”

Section: Related Workmentioning

confidence: 99%

“…The authors previously showed technology trends in different datasets contain distinct patterns while sharing an overall shape on different time windows [1]. The comparison of the proposed method on two datasets analyzes the differences in the list of entities deemed relevant to them.…”

Section: Introductionmentioning

confidence: 99%

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Untitled

2020

IJWesT

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Technological developments are not isolated and are influenced not only by similar technologies but also by many entities, which are sometimes unforeseen by the experts in the field. The authors propose a method for identifying technology-relevant entities with trend curve analysis. The method first utilizes the tangential connection between terms in the encyclopedic dataset to extract technology-related entities with varying relation distances. Changes in their term frequencies within 389 million academic articles and 60 billion web pages are then analyzed to identify technology-relevant entities, incorporating the degrees and changes in both academic interests and public recognitions. The analysis is performed to find entities both significant and relevant to the technology of interest, resulting in the discovery of 40 and 39 technologyrelevant entities, respectively, for unmanned aerial vehicle and hyperspectral imaging with 0.875 and 0.5385 accuracies. The case study showed the proposed method can capture hidden relationships between seemingly unrelated entities.

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Section: Extracting Technology-related Entity Trendsmentioning

confidence: 59%

Section: Related Workmentioning

confidence: 99%

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Untitled

2020

IJWesT

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“…A previously proposed technology trend analysis approach with multiple data sources shows that while different data sources exhibit different forecast speeds, predicting the growth and shrinking in technology trends is possible extrapolating on a previously known technology growth curve [37]. A network-based approach was proposed to overcome the rigidity of trend-based forecasting where the prediction is dependent on the type and shape of the technology growth curve used.…”

Section: Identifying and Predicting New Topicsmentioning

confidence: 99%

Analyzing the generalizability of the network-based topic emergence identification method

Jung

Segev

2022

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Topic evolution helps the understanding of current research topics and their histories by automatically modeling and detecting the set of shared research fields in academic publications as topics. This paper provides a generalized analysis of the topic evolution method for predicting the emergence of new topics, which can operate on any dataset where the topics are defined as the relationships of their neighborhoods in the past by extrapolating to the future topics. Twenty sample topic networks were built with various fields-of-study keywords as seeds, covering domains such as business, materials, diseases, and computer science from the Microsoft Academic Graph dataset. The binary classifier was trained for each topic network using 15 structural features of emerging and existing topics and consistently resulted in accuracy and F1 over 0.91 for all twenty datasets over the periods of 2000 to 2019. Feature selection showed that the models retained most of the performance with only one-third of the tested features. Incremental learning was tested within the same topic over time and between different topics, which resulted in slight performance improvements in both cases. This indicates there is an underlying pattern to the neighbors of new topics common to research domains, likely beyond the sample topics used in the experiment. The result showed that network-based new topic prediction can be applied to various research domains with different research patterns.

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