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Journal article
An overview of distance and similarity functions for structured data
The Artificial intelligence review, v 53(7), pp 5309-5351
01 Oct 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The notions of distance and similarity play a key role in many machine learning approaches, and artificial intelligence in general, since they can serve as an organizing principle by which individuals classify objects, form concepts and make generalizations. While distance functions for propositional representations have been thoroughly studied, work on distance functions for structured representations, such as graphs, frames or logical clauses, has been carried out in different communities and is much less understood. Specifically, a significant amount of work that requires the use of a distance or similarity function for structured representations of data usually employs ad-hoc functions for specific applications. Therefore, the goal of this paper is to provide an overview of this work to identify connections between the work carried out in different areas and point out directions for future work.
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Details
- Title
- An overview of distance and similarity functions for structured data
- Creators
- Santiago Ontanon - Drexel University
- Publication Details
- The Artificial intelligence review, v 53(7), pp 5309-5351
- Publisher
- Springer Nature
- Number of pages
- 43
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000516794300001
- Scopus ID
- 2-s2.0-85080087242
- Other Identifier
- 991019167979904721
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- Web of Science research areas
- Computer Science, Artificial Intelligence