The evolution and diffusion of technological knowledge by developing new Artificial Intelligence and text mining methods

通过开发新的人工智能和文本挖掘方法来发展和传播技术知识

• 批准号: 2102454
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2102454
• 关键词: evolution; diffusion; technological; knowledge; developing

In my research I intend to study the evolution and diffusion of technological knowledge by developing new Artificial Intelligence and text mining methods. One of the main challenges in the study of technological innovation is the measurement of innovative activities and of novel technological outputs. Since new products and manufacturing processes necessarily entail some degree of novelty and greatly vary across sectors and technological domains, the design of indicators that can measure the inputs and outputs of technological innovative processes presents challenges that have currently only been partially addressed. I propose to study the evolution and diffusion of technological knowledge by analysing large volumes of corpora of text. Given that scientific and technological knowledge can be considered embedded in language and vocabularies, the evolutionary patterns of novel ideas can be potentially traced in large volumes of textual data by extending the capabilities of existing Artificial Intelligence and text mining methods for information extraction. Available methods make use of set-theoretic, algebraic and probabilistic mathematical models in order to extract topics from collections of documents and classify unstructured textual data. Despite representing powerful tools for large scale content analysis, these methods are not suited for the identification and measurements of novel scientific and technological ideas. The main limitation is found in the very outputs of these methods, which in their general form are represented by clusters of frequently co-occurring and statistically correlated terms that are hardly interpretable and devoid of meaning. I plan to build on extant literature in cognitive psychology in order to extend these models and realise computational methods for the automated extraction of meaning and interpretable information from large corpora of text. Studies of concepts and categorization have a long tradition in cognitive psychology and offer theories and extensive experimental evidence for how individuals make sense of the large amounts of information they constantly and increasingly must deal with. Knowledge, and technological knowledge in particular, is conveniently categorized in hierarchical classification systems that minimize the cognitive costs of storage, processing and retrieval of information. I intend to show how novelty can be described and measured in terms of the elements of these classification systems and how novel ideas can therefore be traced within relevant textual data. Evolutional patterns of ideas and scientific and technological concepts, as they could be represented over time or across space and knowledge domains, would then offer a rich and interpretable description of technological innovation processes and indicators to measure their inputs and outputs. Building on studies of concepts and categorization, this theoretical framework would thus guide the development of methods for the automated extraction of meaning from collection of documents, which will likely leverage and expand the capabilities of existing methods in Artificial Intelligence and text mining. The data for the development, testing and validation of such methods is largely available and include patents' abstracts and descriptions, technical documentations, and the full texts of publications in trade and academic journals. The methods I intend to develop would be particularly suited for the study of the evolution and diffusion of scientific and technological knowledge and contribute to the field of Artificial Intelligence and text mining.

在我的研究中，我打算通过开发新的人工智能和文本挖掘方法来研究技术知识的演变和传播。技术创新研究的主要挑战之一是创新活动和新技术产出的衡量。由于新产品和制造工艺必然具有一定程度的新奇，而且各部门和技术领域之间差异很大，因此，设计能够衡量技术创新过程的投入和产出的指标是一项挑战，目前仅得到部分解决。我建议通过分析大量的文本语料库来研究技术知识的演变和传播。鉴于科学和技术知识可以被认为是嵌入在语言和词汇中的，通过扩展现有人工智能和文本挖掘方法的信息提取能力，可以在大量的文本数据中跟踪新思想的进化模式。现有的方法利用集合论，代数和概率数学模型，以提取主题的文档集合和分类非结构化的文本数据。尽管代表了大规模内容分析的强大工具，但这些方法不适合识别和测量新的科学和技术思想。主要的局限性在于这些方法的输出，它们的一般形式是由一组频繁共现和统计相关的术语表示的，这些术语很难解释，也没有意义。我计划建立在认知心理学的现存文献，以扩展这些模型，并实现从大型文本语料库中自动提取意义和可解释信息的计算方法。对概念和分类的研究在认知心理学中有着悠久的传统，并为个体如何理解他们不断且越来越多地必须处理的大量信息提供了理论和广泛的实验证据。知识，特别是技术知识，被方便地分为等级分类系统，最大限度地减少了存储，处理和检索信息的认知成本。我打算展示如何根据这些分类系统的要素来描述和衡量新奇，以及如何在相关的文本数据中追踪新颖的想法。思想和科技概念的演变模式，由于可以在时间上或在空间和知识领域上表现出来，因此可以提供丰富和可解释的技术创新过程描述和衡量其投入和产出的指标。基于对概念和分类的研究，这个理论框架将指导从文档集合中自动提取含义的方法的开发，这可能会利用和扩展人工智能和文本挖掘中现有方法的能力。用于开发、测试和验证这些方法的数据在很大程度上是可用的，包括专利摘要和说明、技术文件以及贸易和学术期刊上的出版物全文。我打算开发的方法将特别适合于科学和技术知识的演变和传播的研究，并有助于人工智能和文本挖掘领域。