Existing approaches to model innovation ecosystems have been mostly restricted to qualitative and small-scale levels or, when relying on traditional innovation indicators such as patents and questionnaire-based survey, suffered from a lack of timeliness, granularity, and coverage. Websites of firms are a particularly interesting data source for innovation research, as they are used for publishing information about potentially innovative products, services, and cooperation with other firms. Analyzing the textual and relational content on these websites and extracting innovation-related information from them has the potential to provide researchers and policy-makers with a cost-effective way to survey millions of businesses and gain insights into their innovation activity, their cooperation, and applied technologies. For this purpose, we propose a web mining framework for consistent and reproducible mapping of innovation ecosystems. In a large-scale pilot study we use a database with 2.4 million German firms to test our framework and explore firm websites as a data source. Thereby we put particular emphasis on the investigation of a potential bias when surveying innovation systems through firm websites if only certain firm types can be surveyed using our proposed approach. We find that the availability of a websites and the characteristics of the website (number of subpages and hyperlinks, text volume, language used) differs according to firm size, age, location, and sector. We also find that patenting firms will be overrepresented in web mining studies. Web mining as a survey method also has to cope with extremely large and hyper-connected outlier websites and the fact that low broadband availability appears to prevent some firms from operating their own website and thus excludes them from web mining analysis. We then apply the proposed framework to map an exemplary innovation ecosystem of Berlin-based firms that are engaged in artificial intelligence. Finally, we outline several approaches how to transfer firm website content into valuable innovation indicators.

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用于创新生态系统映射的网络挖掘：框架和大规模试点研究

现有的创新生态系统建模方法大多局限于定性和小规模层面，或者在依赖专利和问卷调查等传统创新指标时，缺乏及时性、粒度和覆盖面。公司网站是创新研究的一个特别有趣的数据源，因为它们用于发布有关潜在创新产品、服务以及与其他公司合作的信息。分析这些网站上的文本和相关内容并从中提取与创新相关的信息有可能为研究人员和政策制定者提供一种经济高效的方式来调查数百万企业并深入了解他们的创新活动、合作和应用技术。以此目的，我们提出了一个网络挖掘框架，用于对创新生态系统进行一致且可重复的映射。在一项大规模试点研究中，我们使用包含 240 万家德国公司的数据库来测试我们的框架并探索公司网站作为数据源。因此，如果只有某些公司类型可以使用我们提出的方法进行调查，我们会特别强调在通过公司网站调查创新系统时对潜在偏见的调查。我们发现网站的可用性和网站的特征（子页面和超链接的数量、文本量、使用的语言）因公司规模、年龄、位置和行业而异。我们还发现专利公司在网络挖掘研究中的比例过高。作为一种调查方法，网络挖掘还必须应对超大和超连接的异常网站，以及低宽带可用性似乎阻止一些公司运营自己的网站，从而将它们排除在网络挖掘分析之外的事实。然后，我们应用所提出的框架来绘制柏林从事人工智能的公司的示范性创新生态系统。最后，我们概述了如何将公司网站内容转化为有价值的创新指标的几种方法。