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Online toxicity, evolving through complex multimodal items like memes, poses a significant challenge. Integrating expert knowledge into moderation systems is increasingly crucial for identifying the nuances of toxic symbology, particularly in memes. Despite the wealth of available expertise, its structured integration into automated systems for online content interpretation remains underdeveloped. This paper introduces the OnTox ontology and OnToxKG Knowledge Graph to address gaps in addressing online toxic symbology. OnTox defines the multimodal semantics of 799 potentially toxic symbols. OnToxKG, a multimodal knowledge graph, integrates these symbols with commonsense sources like Wikidata and WordNet. We demonstrate the practical applications of these resources in automatically analyzing meme toxicity.

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OnToxKG: An Ontology-Based Knowledge Graph of Toxic Symbols and Their Manifestations

  • 会议论文

  • 发表日期：

    2026年

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  • Delfina S. Martinez Pandiani,
  • Erik Tjong Kim Sang,
  • Davide Ceolin

摘要

Online toxicity, evolving through complex multimodal items like memes, poses a significant challenge. Integrating expert knowledge into moderation systems is increasingly crucial for identifying the nuances of toxic symbology, particularly in memes. Despite the wealth of available expertise, its structured integration into automated systems for online content interpretation remains underdeveloped. This paper introduces the OnTox ontology and OnToxKG Knowledge Graph to address gaps in addressing online toxic symbology. OnTox defines the multimodal semantics of 799 potentially toxic symbols. OnToxKG, a multimodal knowledge graph, integrates these symbols with commonsense sources like Wikidata and WordNet. We demonstrate the practical applications of these resources in automatically analyzing meme toxicity.