Results 81 to 90 of about 468,662 (346)
Egyptian Informatics Journal, 2023 Knowledge graph completion employs existing triples to deduce missing data, thereby enriching and enhancing graph completeness. Recent research has revealed that using hyperbolic representation learning in knowledge graph completion yields superior ...
Xiaodong Zhang +3 more
doaj +1 more source
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
, 2018 Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE.
Bi, Jinbo +5 more
core +1 more source
Molecular Oncology, EarlyView.Clinical trials on PARP inhibitors in urothelial carcinoma (UC) showed limited efficacy and a lack of predictive biomarkers. We propose SLFN5, SLFN11, and OAS1 as UC‐specific response predictors. We suggest Talazoparib as the better PARP inhibitor for UC than Olaparib.
Jutta Schmitz +15 more
wiley +1 more source
Hamming Distance Encoding Multihop Relation Knowledge Graph Completion
IEEE Access, 2020 Knowledge graphs (KGs) play an important role in many real-world applications like information retrieval, question answering, relation extraction, etc. To reveal implicit knowledge from a knowledge graph (KG), viz.
Panfeng Chen +4 more
doaj +1 more source
Augmenting Embedding Projection With Entity Descriptions for Knowledge Graph Completion
IEEE Access, 2021 Extra information, such as hierarchical entity types, entity descriptions or some text corpus are recently used to enhance Knowledge Graph Completion (KGC).
Junfan Chen +3 more
doaj +1 more source
Enhancing Knowledge Graph Completion By Embedding Correlations
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017 Despite their large sizes, modern Knowledge Graphs (KGs) are still highly incomplete. Statistical relational learning methods can detect missing links by "embedding" the nodes and relations into latent feature tensors. Unfortunately, these methods are unable to learn good embeddings if the nodes are not well-connected.
Pal, Soumajit, Urbani, Jacopo
openaire +2 more sources
Dual targeting of RET and SRC synergizes in RET fusion‐positive cancer cells
Molecular Oncology, EarlyView.Despite the strong activity of selective RET tyrosine kinase inhibitors (TKIs), resistance of RET fusion‐positive (RET+) lung cancer and thyroid cancer frequently occurs and is mainly driven by RET‐independent bypass mechanisms. Son et al. show that SRC TKIs significantly inhibit PAK and AKT survival signaling and enhance the efficacy of RET TKIs in ...
Juhyeon Son +13 more
wiley +1 more source
A Knowledge Graph Completion Method Based on Fusing Association Information
IEEE Access, 2022 Knowledge graph is a carrier of knowledge. The knowledge graph completion task is to predict the links between entities to make the knowledge graph more complete.
Yuhao Wang, Erping Zhao, Wei Wang
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Knowledge Graph Completion Using Embeddings
, 2023 These slides summarize the current research conducted on Knowledge Graph Completion using Embeddings which was presented at Knowledge Graph Conference 2023.
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Conflict-aware multilingual knowledge graph completion
Knowledge-Based Systems, 2023 Knowledge graph completion (KGC), a task that aims at predicting missing links with existing information inside a knowledge graph (KG), has emerged as a popular research area in recent years. While many existing works have demonstrated effectiveness on a single knowledge graph KGC, limited effort has been devoted to exploring the potentially ...
Weihang Zhang +3 more
openaire +2 more sources