We propose a human-centred process for knowledge discovery from unstructured text that makes use of formal concept analysis and emergent self-organizing maps. The knowledge discovery process is conceptualized and interpreted as successive iterations through the concept-knowledge (C-K) theory design square. To illustrate its effectiveness, we report on a real-life case study of using the process at the Amsterdam-Amstelland police in the Netherlands aimed at distilling concepts to identify domestic violence from the unstructured text in actual police reports. The case study allows us to show how the process was not only able to uncover the nature of a phenomenon such as domestic violence, but also enabled analysts to identify many types of anomaly in the practice of policing. We will illustrate how the insights obtained from this exercise resulted in major improvements in the management of domestic violence cases.
The explosive growth of the Internet has led to a dramatic increase in data sources for (competitive) technology intelligence. Appropriate implementation and use of IT tools to gather and analyze these data is of key importance for the creation of actionable technology intelligence. A strategy to optimize investments in the identified technologies becomes of paramount importance if an organization wants to match knowledge and ideas originating from outside of the organization with internal core competences. Such a strategy can create competitive advantage by effectively linking technology intelligence to open innovation.
We show how VIB, a life sciences research organization, has established technology intelligence processes to identify a multitude of external technologies of interest, which are subsequently “probed” for their potential and fit with VIB using real options reasoning, thereby supporting open innovation. Our methodology may be useful for other organizations which are considering implementing open innovation approaches.
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