Titlebook: Database and Expert Systems Applications; 33rd International C Christine Strauss,Alfredo Cuzzocrea,Ismail Khalil Conference proceedings 202

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Deep Active Learning Framework for Crowdsourcing-Enhanced Image Classification and Segmentation crowdsourcing provides a new way to obtain manually labeled data with the advantages of lower annotation costs and faster annotation speed very recently, especially in the field of computer vision for image classification and segmentation. Therefore, it is necessary to investigate how to combine ma

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Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning it almost impossible to have reliable algorithms for automated stock trading. The lack of reliable labelled data that considers physical and physiological factors that dictate the ups and downs of the market, has hindered the supervised learning attempts for dependable predictions. To learn a good

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DeepCore: A Comprehensive Library for Coreset Selection in Deep Learningownstream tasks such as data-efficient learning, continual learning, neural architecture search, active learning, etc. However, many existing coreset selection methods are not designed for deep learning, which may have high complexity and poor generalization performance. In addition, the recently pr

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Context Iterative Learning for Aspect-Level Sentiment Classificationn words in long sentences. According to extensive research, irrelevant words are far removed from the central words. This paper proposes a solution: First, we design the Context Iterative Learning network (CILN). Context attention module (CAM) is proposed, which employs Context Features Dynamic Mask

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Jointly Learning Propagating Features on the Knowledge Graph for Movie Recommendationuse an attention-based multi-hop propagation mechanism to take users and movies as center nodes and extend their attributes along with the connections of the knowledge graph by recursively calculating the different contributions of their neighbors. We use two real-world datasets to show the effectiv

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Automated Process Knowledge Graph Construction from BPMN Modelstransformation tool with a real-world industrial use case focused on quality management in plastic injection molding for the automotive sector. We use BPMN2KG for process-centric integration of dispersed production systems data that results in an integrated knowledge graph that can be queried using

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CAKE: A Context-Aware Knowledge Embedding Model of Knowledge Graphe the “attention” mechanism in our model. In this work, the learned embeddings of entities and relations are applied to link prediction and triple classification in experiments and our model shows the best performance compared with multiple baselines.

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The Digitalization of Bioassays in the Open Research Knowledge Graphsays with 5,514 unique property-value pairs for 103 predicates shows competitive performance. . As a result, semantified assay collections can be surveyed on the ORKG platform via tabulation or chart-based visualizations of key property values of the chemicals and compounds offering smart knowledge