Knowledge engineering with image data in real-world settings
We report on experiences in adding ML-trained visual recognition modules to a human-oriented image semantic annotation tool which creates RDF descriptions of images and scene contents. We conclude that ML cannot replace expert humans but can aid them in various ways, some unexpected. Semantic markup systems can be to designed to align human and machine blind spots. Finally, we briefly outline directions for future work.
|, , , , , ,|
|CEUR Workshop Proceedings|
|2021 AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering, AAAI-MAKE 2021|
|Organisation||Distributed and Interactive Systems, CWI|
Warren, M, Shamma, D.A, & Hayes, P.J. (2021). Knowledge engineering with image data in real-world settings. In Proceedings of the AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering.