A Semantic Measure for Outlier Detection in Knowledge Graph
نویسندگان
چکیده
soumission à Episciences Nowadays, there is a growing interest in data mining and information retrieval applications from Knowledge Graphs (KG). However, the latter (KG) suffers several quality problems such as accuracy, completeness, different kinds of errors. In DBpedia, are issues related to quality. Among them, we focus on following: entities classes they do not belong to. For instance, query get all class Person also returns group entities, whereas these should be Group. We call “outliers.” The discovery outliers crucial for learning understanding. This paper proposes new outlier detection method that finds entities. define semantic measure favors real (inliers) with positive values while penalizing negative improving it frequent rare itemsets. Our outperforms FPOF (Frequent Pattern Outlier Factor) ones. Experiments show efficiency our approach. De nos jours, il existe un intérêt croissant pour les d'exploration de données et recherche d'informations partir graphes connaissances Cependant, ces derniers souffrent plusieurs problèmes qualité tels que la précision, complétude différents types d'erreurs. Dans liés des données. Parmi eux, nous concentrons sur le suivant: entités se trouvent dans auxquelles elles n'appartiennent pas. Par exemple, requête obtenir toutes classe retourne aussi groupe, tandis celles-ci devraient être Nous appelons "outliers". La découverte mal classées est cruciale l'apprentissage compréhension classes. Cet article propose une nouvelle méthode détection qui permet trouver entités. définissons mesure sémantique favorise réelles avec valeurs positives tout en pénalisant négatives l'améliorons d'itemsets fréquents rares. Notre plus performante celle du Factor). Les expérimentations prouvent l'efficacité notre approche.
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ژورنال
عنوان ژورنال: ARIMA
سال: 2022
ISSN: ['1638-5713']
DOI: https://doi.org/10.46298/arima.8679