Improving Company Valuations with Automated Knowledge Discovery, Extraction and Fusion
This work addresses the problem of labor-intensive and data-scarce company valuations for specialized valuation services in biotech and related industries, representing an incremental improvement by applying existing methods to a new domain.
The paper tackled the challenge of company valuations in biotechnology, pharmacy, and medical technology by using automated knowledge discovery, extraction, and data fusion to obtain additional indicators for product development success and support data curation processes, resulting in the integration of clinical trial data and key personnel information into an industry partner's valuation ontology.
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.