Schema Independent Relational Learning

Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence/independence of several algorithms on existing benchmark and real-world datasets under (de) compositions

Early Sibling Play Interactions as a Source of Developmental Support for Toddlers: Observation of Young Children\u27s Developmental Support During Play with Toddler Siblings

The sibling relationship is a unique and important context for infant and early child development. Despite the important role of siblings and the unique aspects of the sibling relationship, sibling interactions are largely overlooked by scholars as a resource of potential developmental support. Identifying and fostering developmentally supportive interaction (DSI) behaviors in sibling relationships may expand available supports for children’s early development and may also support family well-being. This study used a sample of 15 child-toddler sibling pairs to identify DSI behaviors in interactions between young children and their toddler-aged siblings, determine if and how well DSI behaviors could be observed, determine the similarities and differences between DSIs in child-toddler and caregiver-child interactions, and identify child factors that were associated with DSI behaviors. Caregivers completed a questionnaire online in Qualtrics, answering questions about their children and family, their children’s sibling relationship, and their children’s play skills. Caregivers then recorded and submitted 10-minute videos of their young children playing together, these videos were coded by research assistants who were trained to identify DSI behaviors using an established measure of caregiver-child interaction quality, the Parenting Interactions with Children: Checklist of Observations Linked to Outcomes (PICCOLO). Older siblings across the 15 sibling pairs were observed engaging in each DSI behavior and research assistants were able to reliably code videos for behaviors in the Affection, Responsiveness, and Encouragement domains. When compared to an adult comparison sample, DSI behaviors in young sibling interactions were less frequent, less complex, and lower quality than in adult-child interactions. Younger brothers received more encouragement support from older siblings than younger sisters. Older children who were older siblings provided more developmental support than younger children who were older siblings. Older siblings interacted with more warmth when the age gap was larger than when it was smaller. Older siblings reported by their caregivers to have higher levels of empathy/concern engaged in fewer DSI behaviors and older siblings reported by their caregivers to have higher levels of conflict/aggression engaged in more DSI behaviors. These results may provide guidance for supporting developmentally supportive sibling interactions at home and in intervention

2018 EURēCA Program Book

Includes schedule and listing of posters

Apprentissage de Concept a partir d'Exemples (tres) Ambigus

National audienceDans cet article nous explorons l'incompletude des donnees dans le cadre de l'apprentissage de concepts propositionnels. Nous suivons l'idee de H. Hirsh qui etend le paradigme de l'espace des versions : dans cette extension une hypothese doit etre compatible (dans un sens a definir au cas par cas) avec toutes les informations relatives aux exemples. Nous proposons une representation de ces informations qui rend non seulement compte de situations ou les donnes sont manquantes mais aussi de situations plus generales d'ambiguite dans lesquelles l'exemple est cache au sein d'un ensemble d'instances virtuelles. Nous presentons un nouvel algorithme, LEa, qui apprend un concept DNF (monotone) existentiel a partir d'un ensemble d'exemples ambigus. Nous comparons LEa a J48 et Naive Bayes sur des problemes usuels rendus incomplets a divers degres. Résumé français : Dans cet article nous explorons l'incompletude des donnees dans le cadre de l'apprentissage de concepts propositionnels. Nous suivons l'idee de H. Hirsh qui etend le paradigme de l'espace des versions : dans cette extension une hypothese doit etre compatible (dans un sens a definir au cas par cas) avec toutes les informations relatives aux exemples. Nous proposons une representation de ces informations qui rend non seulement compte de situations ou les donnes sont manquantes mais aussi de situations plus generales d'ambiguite dans lesquelles l'exemple est cache au sein d'un ensemble d'instances virtuelles. Nous presentons un nouvel algorithme, LEa, qui apprend un concept DNF (monotone) existentiel a partir d'un ensemble d'exemples ambigus. Nous comparons LEa a J48 et Naive Bayes sur des problemes usuels rendus incomplets a divers degres

HiER 2015. Proceedings des 9. Hildesheimer Evaluierungs- und Retrievalworkshop

Die Digitalisierung formt unsere Informationsumwelten. Disruptive Technologien dringen verstärkt und immer schneller in unseren Alltag ein und verändern unser Informations- und Kommunikationsverhalten. Informationsmärkte wandeln sich. Der 9. Hildesheimer Evaluierungs- und Retrievalworkshop HIER 2015 thematisiert die Gestaltung und Evaluierung von Informationssystemen vor dem Hintergrund der sich beschleunigenden Digitalisierung. Im Fokus stehen die folgenden Themen: Digital Humanities, Internetsuche und Online Marketing, Information Seeking und nutzerzentrierte Entwicklung, E-Learning

2018 EURēCA Winners

List of awardee