TY - GEN
T1 - Taxonomy of Anomaly Types in Knowledge Graphs
AU - Senaratne, Asara
AU - Christen, Peter
AU - Omran, Pouya
AU - Williams, Graham
PY - 2026
Y1 - 2026
N2 - Detecting anomalies in Knowledge Graphs (KG) is a challenging task as the patterns of anomalies are unpredictable, unknown, diverse, likely rare, and often with no ground truth labels available. Hence, it is important to identify the types of such anomalies occurring in a KG, so domain experts can adopt measures to prevent anomalies occurring during KG construction, or remove anomalies from already constructed KGs, while also discovering knowledge. In such a process we can obtain a classification among these identified anomalies such that we know what anomalies are to be forwarded to domain experts for correction, and what can be corrected via automatic or semi-automatic techniques. However, to the best of our knowledge, there is no such pre-defined classification of possible common anomalies that could arise in a KG, which we could directly use to support anomaly classification. Hence, in this paper, we propose a taxonomy of possible anomaly types that can occur in a KG using the real-world KGs YAGO-1, DSKG, Wikidata and KBpedia.
AB - Detecting anomalies in Knowledge Graphs (KG) is a challenging task as the patterns of anomalies are unpredictable, unknown, diverse, likely rare, and often with no ground truth labels available. Hence, it is important to identify the types of such anomalies occurring in a KG, so domain experts can adopt measures to prevent anomalies occurring during KG construction, or remove anomalies from already constructed KGs, while also discovering knowledge. In such a process we can obtain a classification among these identified anomalies such that we know what anomalies are to be forwarded to domain experts for correction, and what can be corrected via automatic or semi-automatic techniques. However, to the best of our knowledge, there is no such pre-defined classification of possible common anomalies that could arise in a KG, which we could directly use to support anomaly classification. Hence, in this paper, we propose a taxonomy of possible anomaly types that can occur in a KG using the real-world KGs YAGO-1, DSKG, Wikidata and KBpedia.
KW - Anomaly classification
KW - Knowledge Graph refinement
KW - Data quality
KW - Anomaly grouping
UR - http://www.scopus.com/inward/record.url?scp=105020244169&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-99554-5_28
DO - 10.1007/978-3-031-99554-5_28
M3 - Conference contribution
SN - 978-3-031-99553-8
VL - 15832
T3 - Lecture Notes in Computer Science
SP - 151
EP - 155
BT - The Semantic Web
A2 - Curry, Edward
A2 - McCrae, John
A2 - Presutti, Valentina
A2 - Alam, Mehwish
A2 - Colpaert, Pieter
A2 - Xavier Parreira, Josiane
A2 - Collarana, Diego
A2 - Sabou, Marta
A2 - Harth, Andreas
A2 - Lisena, Pasquale
PB - Spinger
CY - Cham, Switzerland
T2 - The Semantic Web Extended Conference, ESWC 25
Y2 - 1 June 2025 through 5 June 2025
ER -