Analogical Proportions: Why They Are Useful in AI Article Swipe
Henri Prade
,
Gilles Richard
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2021/621
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2021/621
This paper presents a survey of researches in analogical reasoning whose building block are analogical proportions which are statements of the form “a is to b as c is to d”. They have been developed in the last twenty years within an Artificial Intelligence perspective. After discussing their formal modeling with the associated inference mechanism, the paper reports the main results obtained in various AI domains ranging from computational linguistics to classification, including image processing, I.Q. tests, case based reasoning, preference learning, and formal concepts analysis. The last section discusses some new theoretical concerns, and the potential of analogical proportions in other areas such as argumentation, transfer learning, and XAI.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.24963/ijcai.2021/621
- https://www.ijcai.org/proceedings/2021/0621.pdf
- OA Status
- gold
- Cited By
- 13
- References
- 74
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3187582633
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3187582633Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.24963/ijcai.2021/621Digital Object Identifier
- Title
-
Analogical Proportions: Why They Are Useful in AIWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-01Full publication date if available
- Authors
-
Henri Prade, Gilles RichardList of authors in order
- Landing page
-
https://doi.org/10.24963/ijcai.2021/621Publisher landing page
- PDF URL
-
https://www.ijcai.org/proceedings/2021/0621.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.ijcai.org/proceedings/2021/0621.pdfDirect OA link when available
- Concepts
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Analogical reasoning, Argumentation theory, Computer science, Artificial intelligence, Inference, Perspective (graphical), Natural language processing, Machine learning, Linguistics, Analogy, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 3, 2023: 1, 2022: 3, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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74Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.including | 72 |
| abstract_inverted_index.inference | 53 |
| abstract_inverted_index.learning, | 81, 107 |
| abstract_inverted_index.potential | 96 |
| abstract_inverted_index.reasoning | 9 |
| abstract_inverted_index.Artificial | 42 |
| abstract_inverted_index.analogical | 8, 14, 98 |
| abstract_inverted_index.associated | 52 |
| abstract_inverted_index.discussing | 46 |
| abstract_inverted_index.mechanism, | 54 |
| abstract_inverted_index.preference | 80 |
| abstract_inverted_index.reasoning, | 79 |
| abstract_inverted_index.researches | 6 |
| abstract_inverted_index.statements | 18 |
| abstract_inverted_index.linguistics | 69 |
| abstract_inverted_index.processing, | 74 |
| abstract_inverted_index.proportions | 15, 99 |
| abstract_inverted_index.theoretical | 92 |
| abstract_inverted_index.Intelligence | 43 |
| abstract_inverted_index.perspective. | 44 |
| abstract_inverted_index.computational | 68 |
| abstract_inverted_index.argumentation, | 105 |
| abstract_inverted_index.classification, | 71 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.83953013 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |