Abstract
Knowledge bases (KBs) have become fundamental resources for artificial intelligence, natural language processing, and data-driven decision-making. However, KBs are prone to various forms of bias that can negatively influence downstream applications, leading to unfair or discriminatory outcomes. Bias in KBs may arise due to data imbalance, human annotation errors, or skewed representation of entities and relationships. This paper presents a comprehensive review of existing methods for bias detection and mitigation in knowledge bases. We categorize techniques into data-driven, embedding-based, and algorithmic approaches, discussing their advantages and limitations. We also highlight benchmark datasets used for evaluating bias and propose a framework for integrating bias mitigation in KB construction. Finally, we discuss open challenges and future research directions.
Keywords: Knowledge bases, Bias detection, Bias mitigation, Fairness, Embedding, Knowledge representation
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