  Industrial processes produce a considerable volume of data and thus
information. Whether it is structured sensory data or semi- to unstructured
textual data, the knowledge that can be derived from it is critical to the
sustainable development of the industrial process. A key challenge of this
sustainability is the intelligent management of the generated data, as well as
the knowledge extracted from it, in order to utilize this knowledge for
improving future procedures. This challenge is a result of the tailored
documentation methods and domain-specific requirements, which include the need
for quick visibility of the documented knowledge. In this paper, we utilize the
expert knowledge documented in chip-design failure reports in supporting user
access to information that is relevant to a current chip design. Unstructured,
free, textual data in previous failure documentations provides a valuable
source of lessons-learned, which expert design-engineers have experienced,
solved and documented. To achieve a sustainable utilization of knowledge within
the company, not only the inherent knowledge has to be mined from unstructured
textual data, but also the relations between the lessons-learned, uncovering
potentially unknown links. In this research, a knowledge graph is constructed,
in order to represent and use the interconnections between reported design
failures. A search engine is developed and applied onto the graph to answer
queries. In contrast to mere keyword-based searching, the searchability of the
knowledge graph offers enhanced search results beyond direct matches and acts
as a mean for generating explainable results and result recommendations.
Results are provided to the design engineer through an interactive search
interface, in which, the feedback from the user is used to further optimize
relations for future iterations of the knowledge graph.
