SLFTD: A Subjective Logic Based Framework for Truth Discovery

• Published in: New Trends in Databases and Information Systems pp. 102 - 110
• Main Authors: Zhang, Danchen, Zadorozhny, Vladimir I., Oleshchuk, Vladimir A.
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Communications in Computer and Information Science
• Subjects: Data fusion; Subjective logic; Truth discovery
• ISBN: 9783030302771; 3030302776
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-030-30278-8_13

Finding truth from various conflicting candidate values provided by different data sources is called truth discovery, which is of vital importance in data integration. Several algorithms have been proposed in this area, which usually have similar procedure: iteratively inferring the truth and provider’s reliability on providing truth until converge. Therefore, an accurate provider’s reliability evaluation is essential. However, no work pays attention to “how reliable this provider continuously providing truth”. Therefore, we introduce subjective logic, which can record both (1) the provider’s reliability of generating truth, and (2) reliability of provider continuously doing so. Our proposed methods provides a better evaluation for data providers, and based on which, truth are discovered more accurately. Our framework can handle both categorical and numerical data, and can identify truth in either a generative or discriminative way. Experiments on two popular real world datasets, Book and Population, validates that our proposed subjective logic based framework can discover truth much more accurately than state-of-art methods.