Modal Logic, Probability and Machine Learning Systems for Metadata Extraction
Artificial intelligence, since its inception, has had two major subfields, namely: logical reasoning and machine learning. Despite this, the interactions between these two fields have been relatively limited. In this paper, we highlight the need for closer integration of logical reasoning and machin...
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| Format: | Article |
| Language: | English |
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Accademia Piceno Aprutina dei Velati
2024-12-01
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| Series: | Ratio Mathematica |
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| Online Access: | http://eiris.it/ojs/index.php/ratiomathematica/article/view/1592 |
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| author | Simone Cuconato |
| author_facet | Simone Cuconato |
| author_sort | Simone Cuconato |
| collection | DOAJ |
| description | Artificial intelligence, since its inception, has had two major subfields, namely: logical reasoning and machine learning. Despite this, the interactions between these two fields have been relatively limited. In this paper, we highlight the need for closer integration of logical reasoning and machine learning. In our approach, logical reasoning tools such as probabilistic modal logic, are employed to provide qualitative feedback on the extracted descriptive metadata. The logical system we consider emerges from combining of S5 modal logic with the formulas of the infinite-valued Łukasiewicz logic and the unary modality P that describes the behaviour of probability functions. The result is a well-motivated system of probabilistic modal logic, that defines a probability distribution over possible worlds of the truth value of metadata extracted from precision medicine approach to Alzheimer’s disease articles through machine learning systems. |
| format | Article |
| id | doaj-art-8ee2a332d0b149eb87457b5dfe2f1307 |
| institution | Kabale University |
| issn | 1592-7415 2282-8214 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Accademia Piceno Aprutina dei Velati |
| record_format | Article |
| series | Ratio Mathematica |
| spelling | doaj-art-8ee2a332d0b149eb87457b5dfe2f13072025-02-01T06:51:01ZengAccademia Piceno Aprutina dei VelatiRatio Mathematica1592-74152282-82142024-12-0153010.23755/rm.v53i0.1592948Modal Logic, Probability and Machine Learning Systems for Metadata ExtractionSimone Cuconato0Department of Humanities, University of CalabriaArtificial intelligence, since its inception, has had two major subfields, namely: logical reasoning and machine learning. Despite this, the interactions between these two fields have been relatively limited. In this paper, we highlight the need for closer integration of logical reasoning and machine learning. In our approach, logical reasoning tools such as probabilistic modal logic, are employed to provide qualitative feedback on the extracted descriptive metadata. The logical system we consider emerges from combining of S5 modal logic with the formulas of the infinite-valued Łukasiewicz logic and the unary modality P that describes the behaviour of probability functions. The result is a well-motivated system of probabilistic modal logic, that defines a probability distribution over possible worlds of the truth value of metadata extracted from precision medicine approach to Alzheimer’s disease articles through machine learning systems.http://eiris.it/ojs/index.php/ratiomathematica/article/view/1592modal logic, probability, logical reasoning, machine learning systems |
| spellingShingle | Simone Cuconato Modal Logic, Probability and Machine Learning Systems for Metadata Extraction Ratio Mathematica modal logic, probability, logical reasoning, machine learning systems |
| title | Modal Logic, Probability and Machine Learning Systems for Metadata Extraction |
| title_full | Modal Logic, Probability and Machine Learning Systems for Metadata Extraction |
| title_fullStr | Modal Logic, Probability and Machine Learning Systems for Metadata Extraction |
| title_full_unstemmed | Modal Logic, Probability and Machine Learning Systems for Metadata Extraction |
| title_short | Modal Logic, Probability and Machine Learning Systems for Metadata Extraction |
| title_sort | modal logic probability and machine learning systems for metadata extraction |
| topic | modal logic, probability, logical reasoning, machine learning systems |
| url | http://eiris.it/ojs/index.php/ratiomathematica/article/view/1592 |
| work_keys_str_mv | AT simonecuconato modallogicprobabilityandmachinelearningsystemsformetadataextraction |