Results 1 to 10 of about 17,025 (214)
Multi-domain knowledge graph embeddings for gene-disease association prediction [PDF]
Journal of Biomedical Semantics, 2023 Background Predicting gene-disease associations typically requires exploring diverse sources of information as well as sophisticated computational approaches.
Susana Nunes +2 more
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Survey on graph embeddings and their applications to machine learning problems on graphs [PDF]
PeerJ Computer Science, 2021 Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model.
Ilya Makarov +3 more
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Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
Data Science and Engineering, 2019 Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Unsupervised graph embedding techniques aim
Stephen Bonner +5 more
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Grad-CAM based deep learning analytics for image-level colon disease classification based on graph neural networks and vision transformers [PDF]
Frontiers in PhysiologyIntroductionAccurate classification of colonoscopic images is essential for early detection and characterization of colorectal diseases. Recent advances in deep learning, particularly transformer-based architectures and graph neural networks (GNNs ...
Chaohui Zhen +7 more
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Representing a Heterogeneous Pharmaceutical Knowledge-Graph with Textual Information
Frontiers in Research Metrics and Analytics, 2021 We deal with a heterogeneous pharmaceutical knowledge-graph containing textual information built from several databases. The knowledge graph is a heterogeneous graph that includes a wide variety of concepts and attributes, some of which are provided in ...
Masaki Asada +3 more
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A Graph Convolutional Network for Session Recommendation Model Based on Improved Transformer
IEEE Access, 2023 Graph convolutional networks are widely used for session-based recommendation (SBR) of products, aimed at solving anonymous sequence recommendation problems.
Xiaoyan Zhang, Teng Wang
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Machines, 2022 In order to find a suitable designer team for the collaborative design crowdsourcing task of a product, we consider the matching problem between collaborative design crowdsourcing task network graph and the designer network graph.
Dianting Liu, Danling Wu, Shan Wu
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Neuro-Symbolic Word Embedding Using Textual and Knowledge Graph Information
Applied Sciences, 2022 The construction of high-quality word embeddings is essential in natural language processing. In existing approaches using a large text corpus, the word embeddings learn only sequential patterns in the context; thus, accurate learning of the syntax and ...
Dongsuk Oh, Jungwoo Lim, Heuiseok Lim
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On the genera of polyhedral embeddings of cubic graph [PDF]
Discrete Mathematics & Theoretical Computer Science, 2021 In this article we present theoretical and computational results on the existence of polyhedral embeddings of graphs. The emphasis is on cubic graphs. We also describe an efficient algorithm to compute all polyhedral embeddings of a given cubic graph and
Gunnar Brinkmann +2 more
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Future Internet, 2023 Educational content recommendation is a cornerstone of AI-enhanced learning. In particular, to facilitate navigating the diverse learning resources available on learning platforms, methods are needed for automatically linking learning materials, e.g., in
Xiu Li +4 more
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