Factual Inconsistencies in Multilingual Wikipedia Tables
It addresses the issue of factual inaccuracies in Wikipedia for users and AI systems that rely on it as a knowledge source, but the work is incremental as it focuses on tabular data without introducing new methods for resolution.
This study tackled the problem of factual inconsistencies in multilingual Wikipedia tables, finding that independent updates across languages lead to discrepancies that affect reliability, with a methodology developed to categorize and analyze these inconsistencies using quantitative and qualitative metrics on a sample dataset.
Wikipedia serves as a globally accessible knowledge source with content in over 300 languages. Despite covering the same topics, the different versions of Wikipedia are written and updated independently. This leads to factual inconsistencies that can impact the neutrality and reliability of the encyclopedia and AI systems, which often rely on Wikipedia as a main training source. This study investigates cross-lingual inconsistencies in Wikipedia's structured content, with a focus on tabular data. We developed a methodology to collect, align, and analyze tables from Wikipedia multilingual articles, defining categories of inconsistency. We apply various quantitative and qualitative metrics to assess multilingual alignment using a sample dataset. These insights have implications for factual verification, multilingual knowledge interaction, and design for reliable AI systems leveraging Wikipedia content.