Abstract
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 technology-relevant 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
Highlights
Identification of relevant terms for a specific technology plays a crucial role in technology funds and government research grants, allowing them to better direct their investments to encourage technological developments beneficial to the target technology
The authors propose a method of identifying any type of entities related to a given technology based on their trend curves
The results showed that the entities with recursive relationships in Wikipedia have connections to the target technology not directly observed by either of their encyclopedic descriptions
Summary
Identification of relevant terms for a specific technology plays a crucial role in technology funds and government research grants, allowing them to better direct their investments to encourage technological developments beneficial to the target technology. The current work proposes an approach for identifying any type of entities relevant to the given technology, based on the trend curves of related entities found from recursive encyclopedic connections to the technology This perspective offers a novel approach of technology trend analysis, granting a possibility of detecting seemingly unrelated entities that cannot be found with conventional means. The proposed method offers a means of identifying significant entities relevant to a given technology based on term frequency and degree of usage growth. It analyzes technology- relevant entities from Wikipedia in the whole domain of academic articles (academia) and web pages (web) with the help of Google search engine, incorporating both the academic interests and public recognitions of the given entities, each representing the earliest and the latest predictive time windows. The use of Wikipedia allows the use of document similarities when filtering for entities with more relevance to the target technology
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