知识深度和广度的主题建模方法:分析技术知识的轨迹

A. Suominen
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引用次数: 2

摘要

技术评估和计划要求我们能够可靠地(但间接地)度量组织中嵌入的知识。将知识嵌入到公司中越来越具有挑战性,但在当前跨学科和复杂的技术环境中也越来越重要。现有的公司知识操作方法是基于专利数据和分析专利分类。然而,这些方法有很大的局限性。本研究利用7家大型电信公司的专利全文数据,共157,718项专利,研究知识的深度和广度。数据分析采用潜狄利克雷分配,一种无监督学习方法。使用技术多样性度量对结果进行量化,显示公司知识的时间变化。研究结果表明,企业知识的可操作性与专利数量无关,企业有其特定的知识发展轨迹。与现有的基于专利分类的方法相比,该方法提供了一种分析公司知识轨迹的新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Topic modelling approach to knowledge depth and breadth: Analyzing trajectories of technological knowledge
Technology assessment and planning requires that we can reliably, but indirectly, measure knowledge embedded in the organization. Operationalizing knowledge embedded into companies is increasingly challenging but also more and more relevant in the current cross-disciplinary and complex technological environment. Existing approaches for operationalizing company knowledge are based on patent data and analyzing patent classifications. These approaches have, however, significant limitations. In this study, knowledge depth and breadth is studied using full-text patent data from seven large telecommunication companies totaling 157,718 patents. The data was analyzed with Latent Dirichlet Allocation, an unsupervised learning method. The results are quantified using a technological diversity metric, showing temporal changes in companies knowledge. The result show how the operationalization of company knowledge is independent of patent count and that companies have their specific trajectory of knowledge development. The approach offers a novel method of analyzing the knowledge trajectory of a company, compared to existing patent classification based methods.
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