TIRA: Truth Inference via Reliability Aggregation on Object-Source Graph

doi:10.1109/TKDE.2022.3225308

2023-11-01

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DOI：10.1109/TKDE.2022.3225308

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摘要

En 中文

Crowdsourcing platforms collect massive dirty claims that are provided by sources for crowdsourced objects, which prompts truth inference to be proposed for crowdsourcing data denoising. Although current graph-based truth-inference methods achieve remarkable success by capturing complex crowdsourcing relationships, they typically suffer from two challenges: 1) They fail to obtain complete crowdsourcing relationships because of the structural limitations of crowdsourcing relationship graphs; 2) Their vector initialization methods for objects and sources are disturbed by claim noise, which limits them from obtaining correct object and source semantics. To cope with these challenges, we propose a novel Truth-Inference method via Reliability Aggregation (TIRA) on an object-source graph. Specifically, we propose a hierarchical graph auto-encoder to adapt to a reasonable object-source graph, which enables TIRA to capture complete crowdsourcing relationships from multiple perspectives. To better guide TIRA, we design a vector initialization method based on source reliabilities to map the denoised claims to a representation space of objects and sources. Finally, TIRA aggregates the reliability information on an object-source graph to generate object embeddings for truth inference. We conducted extensive experiments on 12 real-world datasets. The experimental results demonstrate that our method significantly outperforms 12 state-of-the-art baselines in terms of the accuracy and weighted_F1.

Keyword:

Crowdsourcing

Reliability engineering

Representation learning

Estimation

Semantics

Behavioral sciences

Task analysis

hierarchical graph auto-encoder

reliability aggregation

truth inference