Bilingual Publishing Group

Machine Learning Meets the Semantic Web DOI:  https://doi.org/10.30564/aia.v3i1.3178 Abstract Remarkable progress in research has shown the efficiency of Knowledge Graphs (KGs) in extracting valuable external knowledge in various domains. A Knowledge Graph (KG) can illustrate high-order relations that connect two objects with one or multiple related attributes. The emerging Graph Neural Networks (GNN) can extract both object characteristics and relations from KGs. This paper presents how Machine Learning (ML) meets the Semantic Web and how KGs are related to Neural Networks and Deep Learning. The paper also highlights important aspects of this area of research, discussing open issues such as the bias hidden in KGs at different levels of graph representation. Keywords:  Knowledge graph, Semantic web, Ontology, Machine learning, Deep learning, Graph neural networks

SGT: Session-based Recommendation with GRU and Transformer DOI:  https://doi.org/10.30564/jcsr.v5i2.5610 Received: 30 March 2023 | Revised: 5 April 2023 | Accepted: 11 April 2023 | Published Online: 20 April 2023 Abstract Session-based recommendation aims to predict user preferences based on anonymous behavior sequences. Recent research on session-based recommendation systems has mainly focused on utilizing attention mechanisms on sequential patterns, which has achieved significant results. However, most existing studies only consider individual items in a session and do not extract information from continuous items, which can easily lead to the loss of information on item transition relationships. Therefore, this paper proposes a session-based recommendation algorithm (SGT) based on Gated Recurrent Unit (GRU) and Transformer, which captures user interests by learning continuous items in the current session and utilizes all item transitions on sessions in a more refined way. By com...