Data quality in reasoning

Stephanie Inglis; Ehud Reiter; Somayajulu Sripada

Data quality in reasoning

Stephanie Inglis, Ehud Reiter, Somayajulu Sripada

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Abstract

Decision making using data is dependent on the quality of the data being used to make those decisions. Currently, data-to-text recommendation systems do not take this into consideration. Unsatisfactory recommendations are likely to cause further damage, which could have a detrimental effect economically or from a health and safety perspective. Highlighting quality issues in data-to-text systems will allow readers to consider this.

Original language	English
Journal	CEUR Workshop Proceedings
Volume	2151
Early online date	1 Jan 2018
Publication status	Published - 1 Jan 2018
Event	1st SICSA Workshop on Reasoning, Learning and Explainability, ReaLX 2018 - Aberdeen, United Kingdom Duration: 27 Jun 2018 → 27 Jun 2018

Keywords

Data quality
Data-to-text
Reasoning

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title = "Data quality in reasoning",

abstract = "Decision making using data is dependent on the quality of the data being used to make those decisions. Currently, data-to-text recommendation systems do not take this into consideration. Unsatisfactory recommendations are likely to cause further damage, which could have a detrimental effect economically or from a health and safety perspective. Highlighting quality issues in data-to-text systems will allow readers to consider this.",

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T1 - Data quality in reasoning

AU - Inglis, Stephanie

AU - Reiter, Ehud

AU - Sripada, Somayajulu

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Decision making using data is dependent on the quality of the data being used to make those decisions. Currently, data-to-text recommendation systems do not take this into consideration. Unsatisfactory recommendations are likely to cause further damage, which could have a detrimental effect economically or from a health and safety perspective. Highlighting quality issues in data-to-text systems will allow readers to consider this.

AB - Decision making using data is dependent on the quality of the data being used to make those decisions. Currently, data-to-text recommendation systems do not take this into consideration. Unsatisfactory recommendations are likely to cause further damage, which could have a detrimental effect economically or from a health and safety perspective. Highlighting quality issues in data-to-text systems will allow readers to consider this.

KW - Data quality

KW - Data-to-text

KW - Reasoning

UR - http://www.scopus.com/inward/record.url?scp=85054937349&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85054937349

SN - 1613-0073

VL - 2151

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - 1st SICSA Workshop on Reasoning, Learning and Explainability, ReaLX 2018

Y2 - 27 June 2018 through 27 June 2018

ER -

Data quality in reasoning

Abstract

Keywords

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