Summary of Online Detection Of Anomalies in Temporal Knowledge Graphs with Interpretability, by Jiasheng Zhang et al.
Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability
by Jiasheng Zhang, Rex Ying, Jie Shao
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Databases (cs.DB); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Temporal Knowledge Graphs (TKGs) are valuable resources for capturing evolving relationships among entities, but they often contain noise, necessitating robust anomaly detection mechanisms. Existing dynamic graph anomaly detection approaches struggle to capture the rich semantics introduced by node and edge categories within TKGs, while TKG embedding methods lack interpretability, undermining the credibility of anomaly detection. Moreover, these methods falter in adapting to pattern changes and semantic drifts resulting from knowledge updates. To tackle these challenges, we introduce AnoT, an efficient TKG summarization method tailored for interpretable online anomaly detection in TKGs. AnoT begins by summarizing a TKG into a novel rule graph, enabling flexible inference of complex patterns in TKGs. When new knowledge emerges, AnoT maps it onto a node in the rule graph and traverses the rule graph recursively to derive the anomaly score of the knowledge. The traversal yields reachable nodes that furnish interpretable evidence for the validity or the anomalous of the new knowledge. Overall, AnoT embodies a detector-updater-monitor architecture, encompassing a detector for offline TKG summarization and online scoring, an updater for real-time rule graph updates based on emerging knowledge, and a monitor for estimating the approximation error of the rule graph. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to detect unusual patterns in big networks that change over time. These networks are called Temporal Knowledge Graphs (TKGs). Right now, detecting unusual patterns in TKGs can be tricky because they contain lots of noise and changing relationships between things. To make this task easier, the researchers created a new method called AnoT. AnoT works by first summarizing the network into a simpler form, then using that to find unusual patterns. This process is repeated every time new information comes in, so it can adapt to changes. The results show that AnoT does a better job than other methods at detecting unusual patterns and making sense of why they’re unusual. |
Keywords
* Artificial intelligence * Anomaly detection * Embedding * Inference * Semantics * Summarization
