Lessons learned (and questions raised) from an interdisciplinary Machine Translation approach

Linked Open Data (LOD) has ultimate benets in various elds of computer science and es- pecially the large area of Natural Language Processing (NLP) might be a very promising use case for it, as it widely relies on formalized knowledge. Previously, the author has published 1 a fast-forward combinatorial ap- proach he called \Semantic Web based Machine Trans- lation" (SWMT), which tried to solve a common prob- lem in the NLP-subeld of Machine Translation (MT) with world knowledge that is, in form of LOD, inherent in the Web of Data. This paper rst introduces this practical idea shortly and then summarizes the lessons learned and the questions raised through this approach and prototype, regarding the Semantic Web tool stack and design principles. Thereby, the author aimes at fos- tering further discussions with the international LOD community.