Anomaly Detection and Classification in Knowledge Graphs

TL;DR

This paper introduces SEKA, an unsupervised method for detecting anomalies in knowledge graphs, utilizing a novel adaptation of the Path Rank Algorithm and a taxonomy of anomaly types to improve data quality.

Contribution

The paper presents SEKA, an unsupervised anomaly detection approach, and TAXO, a taxonomy of anomaly types, enhancing knowledge graph quality assessment.

Findings

SEKA outperforms baseline methods on four real-world KGs.

CPRA efficiently detects anomalies with high accuracy.

Taxonomy TAXO classifies diverse anomaly types in KGs.

Abstract

Anomalies such as redundant, inconsistent, contradictory, and deficient values in a Knowledge Graph (KG) are unavoidable, as these graphs are often curated manually, or extracted using machine learning and natural language processing techniques. Therefore, anomaly detection is a task that can enhance the quality of KGs. In this paper, we propose SEKA (SEeking Knowledge graph Anomalies), an unsupervised approach for the detection of abnormal triples and entities in KGs. SEKA can help improve the correctness of a KG whilst retaining its coverage. We propose an adaption of the Path Rank Algorithm (PRA), named the Corroborative Path Rank Algorithm (CPRA), which is an efficient adaptation of PRA that is customized to detect anomalies in KGs. Furthermore, we also present TAXO (TAXOnomy of anomaly types in KGs), a taxonomy of possible anomaly types that can occur in a KG. This taxonomy provides a classification of the anomalies discovered by SEKA with an extensive discussion of possible data quality issues in a KG. We evaluate both approaches using the four real-world KGs YAGO-1, KBpedia, Wikidata, and DSKG to demonstrate the ability of SEKA and TAXO to outperform the baselines.