论文标题

通过自动化知识发现,提取和融合来提高公司估值

Improving Company Valuations with Automated Knowledge Discovery, Extraction and Fusion

论文作者

Weichselbraun, Albert, Kuntschik, Philipp, Hörler, Sandro

论文摘要

在生物技术,药房和医疗技术领域内进行公司估值是一项具有挑战性的任务,尤其是在考虑生物技术初创企业在进入新市场时面临的独特风险时。因此,专门从事全球估值服务的公司将估值模型和过去的经验与异构指标和指标相结合,可提供对公司业绩的见解。本文说明了自动化知识发现,提取和数据融合如何用于(i)获得其他指标,以提供有关公司产品开发工作成功的见解,以及(ii)支持劳动密集型数据策划过程。我们采用深层的Web知识获取方法来识别和收集有关临床试验的数据,这些数据隐藏在专有搜索界面后面,并将提取的数据集成到行业伙伴的公司估值本体论中。此外,集中的网络爬网和浅层语义解析收益率有关公司的关键人员和各自的联系数据,并将相关变化通知域专家,然后将其纳入行业合作伙伴的公司数据中。

Performing company valuations within the domain of biotechnology, pharmacy and medical technology is a challenging task, especially when considering the unique set of risks biotech start-ups face when entering new markets. Companies specialized in global valuation services, therefore, combine valuation models and past experience with heterogeneous metrics and indicators that provide insights into a company's performance. This paper illustrates how automated knowledge discovery, extraction and data fusion can be used to (i) obtain additional indicators that provide insights into the success of a company's product development efforts, and (ii) support labor-intensive data curation processes. We apply deep web knowledge acquisition methods to identify and harvest data on clinical trials that is hidden behind proprietary search interfaces and integrate the extracted data into the industry partner's company valuation ontology. In addition, focused Web crawls and shallow semantic parsing yield information on the company's key personnel and respective contact data, notifying domain experts of relevant changes that get then incorporated into the industry partner's company data.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源