The distribution semantics in probabilistic logic programming and probabilistic description logics: a survey
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| Název: | The distribution semantics in probabilistic logic programming and probabilistic description logics: a survey |
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| Autoři: | Bellodi, Elena |
| Zdroj: | Intelligenza Artificiale. 17:143-156 |
| Informace o vydavateli: | SAGE Publications, 2023. |
| Rok vydání: | 2023 |
| Témata: | distribution semantics, probabilistic description logics, probabilistic logic programming, Statistical relational learning, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology |
| Popis: | Representing uncertain information is crucial for modeling real world domains. This has been fully recognized both in the field of Logic Programming and of Description Logics (DLs), with the introduction of probabilistic logic languages and various probabilistic extensions of DLs respectively. Several works have considered the distribution semantics as the underlying semantics of Probabilistic Logic Programming (PLP) languages and probabilistic DLs (PDLs), and have then targeted the problem of reasoning and learning in them. This paper is a survey of inference, parameter and structure learning algorithms for PLP languages and PDLs based on the distribution semantics. A few of these algorithms are also available as web applications. |
| Druh dokumentu: | Article |
| Popis souboru: | application/pdf |
| ISSN: | 2211-0097 1724-8035 |
| DOI: | 10.3233/ia-221072 |
| Přístupová URL adresa: | https://content.iospress.com/articles/intelligenza-artificiale/ia221072 https://doi.org/10.3233/IA-221072 https://hdl.handle.net/11392/2528770 |
| Přístupové číslo: | edsair.doi.dedup.....bc694cdbe97b12eaac7dd35a3b9133f4 |
| Databáze: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | Representing uncertain information is crucial for modeling real world domains. This has been fully recognized both in the field of Logic Programming and of Description Logics (DLs), with the introduction of probabilistic logic languages and various probabilistic extensions of DLs respectively. Several works have considered the distribution semantics as the underlying semantics of Probabilistic Logic Programming (PLP) languages and probabilistic DLs (PDLs), and have then targeted the problem of reasoning and learning in them. This paper is a survey of inference, parameter and structure learning algorithms for PLP languages and PDLs based on the distribution semantics. A few of these algorithms are also available as web applications. |
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| ISSN: | 22110097 17248035 |
| DOI: | 10.3233/ia-221072 |