A Computational Theory for Life-Long Learning of Semantics

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 an...

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Bibliographic Details
Published inArtificial General Intelligence Vol. 10999; pp. 217 - 226
Main Authors Sutor, Peter, Summers-Stay, Douglas, Aloimonos, Yiannis
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2018
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Dynamic systems
Hyperdimensional computing
Incremental learning
Knowledge representation
Semantic vectors
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Summary: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.
ISBN:3319976753
9783319976754
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-97676-1_21