@article{WeberBergKheliletal.2022, author = {Weber, Stefan and Berg, Simeon and Khelil, Abdelmajid and Lehner, Thomas and Chebaane, Ahmed and Bilgin, Berkin and Ramadani, Kreshnik and Doering, Claudia}, title = {Ontology-based Semantic Matching for Technology Transfer}, series = {Research Notes on Data and Process Science}, journal = {Research Notes on Data and Process Science}, number = {3}, issn = {2702-508X}, doi = {10.57688/334}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:860-opus4-3342}, pages = {1 -- 9}, year = {2022}, abstract = {Recently, Technology Transfer plays a crucial role in transferring knowledge and technologies from academia to the industry and society through a centralized web repository. However, due to the large volume of the data transfer and lack of quality and inconsistency, the search engine may not easily be able to understand the user's interests that lead to insufficient and inappropriate matching results. This article proposes a novel ontology-based semantic web tool for universities to match collaboration requests to the most suitable available competencies. The proposed tool highly automates the matching process for a timely matching results without sacrificing their quality/accuracy.}, subject = {Wissens- und Technologietransfer}, language = {en} }