Publication Type
Journal Article
Publication Date (Issue Year)
2022
Journal Name
Revue Africaine de La Recherche En Informatique et Mathématiques Appliquées
Abstract
Nowadays, there is a growing interest in data mining and information retrieval applications from Knowledge Graphs (KG). However, the latter (KG) suffers from several data quality problems such as accuracy, completeness, and different kinds of errors. In DBpedia, there are several issues related to data quality. Among them, we focus on the following: several entities are in classes they do not belong to. For instance, the query to get all the entities of the class Person also returns group entities, whereas these should be in the class Group. We call such entities “outliers.” The discovery of such outliers is crucial for class learning and understanding. This paper proposes a new outlier detection method that finds these entities. We define a semantic measure that favors the real entities of the class (inliers) with positive values while penalizing outliers with negative values and improving it with the discovery of frequent and rare itemsets. Our measure outperforms FPOF (Frequent Pattern Outlier Factor) ones. Experiments show the efficiency of our approach.
Keywords
Outlier, Detection, Knowledge Graph
Rsif Scholar Name
Bara Diop
Thematic Area
ICTs Including Big Data and Artificial Intelligence
Africa Host University (AHU)
University of Gaston Berger (UGB), Senegal
Funding Statement
This research was supported by the Partnership for skills in Applied Sciences, Engineering and Technology (PASET) - Regional Scholarship and Innovation Fund (RSIF).
Recommended Citation
Diop, B., Diop, C. T., & Diop, L. (2022). A semantic measure for outlier detection in knowledge graph. Revue Africaine de La Recherche En Informatique et Mathématiques Appliquées, 35 https://doi.org/10.46298/arima.8679