A Computational Theory for Life-Long Learning of Semantics
Abstract
Semantic vectors are learned from data to express semantic relationships between elements of information, for the purpose of solving and informing downstream tasks. Other models exist that learn to map and classify supervised data. However, the two worlds of learning rarely interact to inform one another dynamically, whether across types of data or levels of semantics, in order to form a unified model. We explore the research problem of learning these vectors and propose a framework for learning the semantics of knowledge incrementally and online, across multiple mediums of data, via binary vectors. We discuss the aspects of this framework to spur future research on this approach and problem.
Cite
@article{arxiv.1806.10755,
title = {A Computational Theory for Life-Long Learning of Semantics},
author = {Peter Sutor and Douglas Summers-Stay and Yiannis Aloimonos},
journal= {arXiv preprint arXiv:1806.10755},
year = {2018}
}
Comments
Submitted and accepted to AGI 2018. Length 10 pages with 2 figures