Research organisations and their research outputs have been growing considerably in the past decades. This large body of knowledge attracts various stakeholders, e.g., for knowledge sharing, technology transfer, or potential collaborations. However, due to the large amount of complex knowledge created, traditional methods of manually curating catalogues are often out of time, imprecise, and cumbersome. Finding domain experts and knowledge within any larger organisation, scientific and also industrial, has thus become a serious challenge. Hence, exploring an institutions domain knowledge and finding its experts can only be solved by an automated solution. This work presents the scheme of an automated approach for identifying scholarly experts based on their publications and, prospectively, their teaching materials. Based on a search engine, this approach is currently being implemented for two universities, for which some examples are presented. The proposed system will be helpful for finding peer researchers as well as starting points for knowledge exploitation and technology transfer. As the system is designed in a scalable manner, it can easily include additional institutions and hence provide a broader coverage of research facilities in the future.

翻译：过去几十年来，研究机构及其研究成果数量大幅增长。这一庞大的知识体系吸引了各类利益相关者，例如用于知识共享、技术转移或潜在合作。然而，由于产生的复杂知识数量庞大，传统的手工编目方法往往存在时效性差、精度不足且操作繁琐的问题。因此，在大型组织（包括科研机构和工业企业）中寻找领域专家和知识已成为一项严峻挑战。为此，唯有通过自动化解决方案才能解决机构领域知识的探索与专家识别问题。本文提出了一种基于研究者出版物及其教学材料（作为未来扩展方向）自动识别学术专家的方案框架。该方案基于搜索引擎技术，目前已在两所大学实施，并给出了具体案例。所提出的系统将有助于发现同行研究者，并为知识应用与技术转移提供切入点。由于系统采用可扩展架构设计，未来可轻松纳入更多机构，从而实现对科研设施的更广泛覆盖。