Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka Conference p

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MULTIFORM: Few-Shot Knowledge Graph Completion via Multi-modal Contextsrevious research on Knowledge Graph Completion (KGC) usually requires a large number of training instances for each relation. However, considering the accelerated growth of online information, there can be some relations that do not have enough training examples. In fact, in most real-world knowledg

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RDF Knowledge Base Summarization by Inducing First-Order Horn Rulese range of knowledge-based applications, such as query answering and KB indexing. The patterns of graph structure and/or association are commonly employed to summarize and reduce the number of triples. However, knowledge coverage is low in state-of-the-art techniques due to limited expressiveness of

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DeMis: Data-Efficient Misinformation Detection Using Reinforcement Learninglgorithms effectively, a large amount of training data is essential. Unfortunately, while unlabeled data are abundant, manually-labeled data are lacking for misinformation detection. In this paper, we propose DeMis, a novel reinforcement learning (RL) framework to detect misinformation on Twitter in

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SkipCas: Information Diffusion Prediction Model Based on Skip-Gramrowth size of the information cascade is widely used in the fields of preventing rumor spread, viral marketing, recommendation system and so on. However, most of the existing methods either cannot fully capture the structural representation of the cascade graph, or cannot effectively utilize the dyn

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