Extracting Problem Linkages to Improve Knowledge Exchange between Science and Technology Domains using an Attention-based Language Model
Extracting Problem Linkages to Improve Knowledge Exchange between Science and Technology Domains using an Attention-based Language Model
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DOI：
10.48084/etasr.3598
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
H. Sasaki;S. Yamamoto;A. Agchbayar;Ν. Nkhbayasgalan
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文献类型：
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作者：
H. Sasaki;S. Yamamoto;A. Agchbayar;Ν. Nkhbayasgalan
科学技术活动可以被视为解决问题的活动，科学论文和专利出版物可以被视为分别提供从学术界和工业界解决问题中获得的显性知识。然而，即使在同一领域，一篇论文与专利技术对同一问题的处理方法也不一致。科学技术中信息孤岛的产生导致人类智力生产效率低下。因此，本研究探讨技术问题的见解是否可以与学术界分享以解决科学问题。我们提出了一个概念，使用连接科学和技术的知识发现的语言方法来链接这两个领域之间的问题。我们从计算语言学协会数据集中提取了科学论文，并从德温特创新平台中提取了专利文献。从这些中，使用基于注意力的语言模型识别并提取了成对的问题定义句子。例如，我们能够提取不一定来自科学论文的问题示例，例如社交网络数据分析中的注释困难，但可以通过论文之前的专利技术暗示。这些结果表明科学问题和工业解决方案可以提供相互的见解。推荐这种知识发现方法不仅可以使企业活动受益，而且可以把握研究趋势。
Science and technology activities can be considered problem-solving activities, and scientific papers and patent publications can be viewed as providing explicit knowledge gained from the problem-solving of academia and industry respectively. However, even in the same field, the approach to the same problem is not consistent between a paper and the patented technology. The creation of information silos in science and technology generates inefficiency in human intellectual production. Therefore, this study examines whether insights from technical problems can be shared with academics to solve scientific problems. We propose a concept to link the problems between these two domains using a linguistic approach for knowledge discovery that connects science and technology. We extracted scientific papers from the Association for Computational Linguistics dataset, and patent literature from the Derwent Innovation platform. From these, pairs of problem defining sentences were identified and extracted using an attention-based language model. For example, we were able to extract examples of issues that do not necessarily arise from scientific papers, such as annotation difficulties in the analysis of social network data, but can be hinted at by patented techniques prior to the paper. These results suggest that scientific problems and industrial solutions can provide mutual insight. This knowledge discovery approach is recommended not only for benefiting corporate activities but also for grasping research trends.