Bara Diop ; Cheikh Talibouya Diop ; Lamine Diop - A Semantic Measure for Outlier Detection in Knowledge Graph

arima:8679 - Revue Africaine de Recherche en Informatique et Mathématiques Appliquées, April 11, 2022, Volume 35, Data Intelligibilty, Business Intelligence and Semantic Web - https://doi.org/10.46298/arima.8679
A Semantic Measure for Outlier Detection in Knowledge Graph

Authors: Bara Diop ; Cheikh Talibouya Diop ; Lamine Diop

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.


Volume: Volume 35, Data Intelligibilty, Business Intelligence and Semantic Web
Published on: April 11, 2022
Accepted on: March 20, 2022
Submitted on: November 5, 2021
Keywords: Knowledge graph,Pattern Mining,Itemset,Outlier Detection,[INFO]Computer Science [cs]


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