Full Citation

Title: IncompFuse: a logical framework for historical information fusion with inaccurate data sources

Citation Type: Journal Article

Publication Year: 2020

ISBN:

ISSN: 0925-9902

DOI: 10.1007/s10844-019-00569-6

NSFID:

PMCID:

PMID:

Abstract: We propose a novel framework, called INCOMPFUSE, that significantly improves the accuracy of existing methods for reconstructing aggregated historical data from inaccurate historical reports. INCOMPFUSE supports efficient data reliability assessment using the incompatibility probability of historical reports. We provide a systematic approach to define this probability based on properties of the data and relationships between the reports. Our experimental study demonstrates high utility of the proposed framework. In particular, we were able to detect noisy historical reports with very high detection accuracy.

Url: https://link.springer.com/article/10.1007/s10844-019-00569-6

Url: http://link.springer.com/10.1007/s10844-019-00569-6

User Submitted?: No

Authors: Xu, Jiawei; Zadorozhny, Vladimir; Grant, John

Periodical (Full): Journal of Intelligent Information Systems

Issue:

Volume: 54

Pages: 463-481

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Population Data Science

Countries: