Improving Company Valuations with Automated Knowledge Discovery, Extraction and Fusion

TL;DR

This paper presents a method that uses automated knowledge discovery, extraction, and data fusion to enhance company valuation processes in biotech by uncovering additional indicators and streamlining data curation.

Contribution

It introduces a novel approach combining deep web knowledge acquisition, semantic parsing, and data fusion to improve valuation accuracy and efficiency in biotech companies.

Findings

Enhanced indicators for biotech company success identified

Automated data extraction reduces manual curation effort

Improved integration of clinical trial data into valuation models

Abstract

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.