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Crowdsourced Truth Discovery in the Presence of Hierarchies for Knowledge Fusion

Title
Crowdsourced Truth Discovery in the Presence of Hierarchies for Knowledge Fusion
Author
김영훈
Keywords
Computer Science - Databases; I.2.6
Issue Date
2019-04
Publisher
EDBT
Citation
Proceedings of the 22nd International Conference on Extending Database Technology, Page. 205-216
Abstract
Existing works for truth discovery in categorical data usually assume that claimed values are mutually exclusive and only one among them is correct. However, many claimed values are not mutually exclusive even for functional predicates due to their hierarchical structures. Thus, we need to consider the hierarchical structure to effectively estimate the trustworthiness of the sources and infer the truths. We propose a probabilistic model to utilize the hierarchical structures and an inference algorithm to find the truths. In addition, in the knowledge fusion, the step of automatically extracting information from unstructured data (e.g., text) generates a lot of false claims. To take advantages of the human cognitive abilities in understanding unstructured data, we utilize crowdsourcing to refine the result of the truth discovery. We propose a task assignment algorithm to maximize the accuracy of the inferred truths. The performance study with real-life datasets confirms the effectiveness of our truth inference and task assignment algorithms.
URI
https://arxiv.org/abs/1904.10217?http://repository.hanyang.ac.kr/handle/20.500.11754/122370
DOI
10.5441/002/edbt.2019.19
Appears in Collections:
COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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