Summary of Anomaly Detection and Classification in Knowledge Graphs, by Asara Senaratne et al.
Anomaly Detection and Classification in Knowledge Graphs
by Asara Senaratne, Peter Christen, Pouya Omran, Graham Williams
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel unsupervised approach for detecting abnormal triples and entities in Knowledge Graphs (KGs) is proposed in this paper. The method, called SEKA, aims to enhance the quality of KGs by identifying and correcting inconsistencies, contradictions, and deficiencies. Building on the Path Rank Algorithm (PRA), a customized adaptation called CPRA is developed to detect anomalies in KGs. Additionally, a taxonomy of anomaly types, referred to as TAXO, is presented to classify and discuss potential data quality issues in KGs. The proposed methods are evaluated using four real-world KGs, demonstrating their ability to outperform baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to find mistakes in large collections of information called Knowledge Graphs (KGs). These graphs can be used for artificial intelligence and machine learning applications, but they often contain errors. The team created two tools: SEKA, which finds unusual patterns or inconsistencies in the data, and TAXO, a system that categorizes these mistakes into different types. They tested their methods using four large databases of information and showed that their approach is better than previous methods. |
Keywords
» Artificial intelligence » Machine learning » Unsupervised
