Open Vocabulary Video Anomaly Detection (OVVAD) aims to detect and categorize both base and novel anomalies. However, there are two specific challenges related to novel anomalies that remain unexplored by existing methods. The first challenge is detection ambiguity, where the model struggles to assign accurate anomaly scores to unfamiliar anomalies. The second challenge is categorization confusion, where novel anomalies are often miscategorized as visually similar base instances. To address the aforementioned challenges, we investigate supportive information from multiple sources, aiming to reduce detection ambiguity by leveraging multiple levels of visual data with matching textual information. Additionally, we propose introducing relationships between labels to guide the encoding of new labels, thereby enhancing the alignment between novel videos and their corresponding labels, which helps reduce categorization confusion. Our resulting Anomize framework effectively addresses these challenges, achieving superior performance on UCF-Crime and XD-Violence datasets, demonstrating its strength in OVVAD.