Learning probabilistic rules for answering why-questions

Abdus Salam; Rolf Schwitter; Mehmet Orgun

Learning probabilistic rules for answering why-questions

Abdus Salam, Rolf Schwitter, Mehmet Orgun

School of Computing

Research output: Contribution to conference › Paper

Abstract

Why-questions try to get to the bottom of the matter and ask for explanations. In this paper, we show how we can learn the complete structure of probabilistic rules for a question-answering system from example interpretations. These rules are then used by a meta-interpreter to find answers in the form of explanations for why-questions for a particular application domain.

Original language	English
Number of pages	9
Publication status	Published - 2019
Event	1st International Workshop on Interpretability: Methodologies and Algorithms (IMA 2019) part of AI 2019 and AusDM 2019 - Adelaide, Australia Duration: 2 Dec 2019 → 2 Dec 2019

Conference

Conference	1st International Workshop on Interpretability: Methodologies and Algorithms (IMA 2019) part of AI 2019 and AusDM 2019
Country/Territory	Australia
City	Adelaide
Period	2/12/19 → 2/12/19

Keywords

why-questions
explainability
probabilistic logic programming
probabilistic rule learning

Cite this

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title = "Learning probabilistic rules for answering why-questions",

abstract = "Why-questions try to get to the bottom of the matter and ask for explanations. In this paper, we show how we can learn the complete structure of probabilistic rules for a question-answering system from example interpretations. These rules are then used by a meta-interpreter to find answers in the form of explanations for why-questions for a particular application domain.",

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author = "Abdus Salam and Rolf Schwitter and Mehmet Orgun",

year = "2019",

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TY - CONF

T1 - Learning probabilistic rules for answering why-questions

AU - Salam, Abdus

AU - Schwitter, Rolf

AU - Orgun, Mehmet

PY - 2019

Y1 - 2019

N2 - Why-questions try to get to the bottom of the matter and ask for explanations. In this paper, we show how we can learn the complete structure of probabilistic rules for a question-answering system from example interpretations. These rules are then used by a meta-interpreter to find answers in the form of explanations for why-questions for a particular application domain.

AB - Why-questions try to get to the bottom of the matter and ask for explanations. In this paper, we show how we can learn the complete structure of probabilistic rules for a question-answering system from example interpretations. These rules are then used by a meta-interpreter to find answers in the form of explanations for why-questions for a particular application domain.

KW - why-questions

KW - explainability

KW - probabilistic logic programming

KW - probabilistic rule learning

UR - https://sites.google.com/view/ima2019/ima-2019-workshop-program

M3 - Paper

T2 - 1st International Workshop on Interpretability: Methodologies and Algorithms (IMA 2019) part of AI 2019 and AusDM 2019

Y2 - 2 December 2019 through 2 December 2019

ER -

Learning probabilistic rules for answering why-questions

Abstract

Conference

Keywords

Other files and links

Answering why-questions using probabilistic logic programming

Cite this