INDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding Shuwen Liu 1, Bernardo Cuenca Grau , Ian Horrocks , and Egor V. Kostylev2 1Department of Computer Science, University of Oxford, UK {shuwen.liu, bernardo.cuenca.grau, ian.horrocks}@cs.ox.ac.uk
The paper proposes the Monotonic Graph Neural Network (MGNN) model that can be exploited to learn tasks on knowledge graphs. Knowledge graphs are referred to as ...
Library for deep learning on graphs. ... a variety of domain-specific projects including DGL-KE for learning large-scale knowledge graph embeddings, ...
12.07.2021 · Here, we propose a novel graph neural network (GNN)-based model, named KG4SL, by incorporating knowledge graph (KG) message-passing into SL prediction. The KG was constructed using 11 kinds of entities including genes, compounds, diseases, biological processes and 24 kinds of relationships that could be pertinent to SL.
KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction Xuan Lin 1, Zhe Quan;, Zhi-Jie Wang2;, Tengfei Ma1 and Xiangxiang Zeng1 1College of Information Science and Engineering, Hunan University 2College of Computer Science, Chongqing University fjack lin, quanzhe, tfma, xzengg@hnu.edu.cn, cszjwang@yahoo.com
Knowledge graph (KG) is an effective way of organizing the useful information in those literature so that they can be retrieved efficiently. It also bridges the heterogeneous biomedical concepts that are involved in the D3 process. In this chapter we will review the existing biomedical KG and introduce how GNN techniques can facilitate the D3 ...
extremely large-scale, a scalable knowledge graph repre-sentation framework implemented on distributed learning system is in urgent demand. To integrate above main idea together, we propose KGNN, a distributed framework for graph neural knowledge represen-tation with graph neural network (GNN) based encoder and knowledge aware decoder.
The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context ...
Knowledge graph (KG) is an effective way of organizing the useful information in those literature so that they can be retrieved efficiently. It also bridges the heterogeneous biomedical concepts that are involved in the D3 process. In this chapter we will review the existing biomedical KG and introduce how GNN techniques can facilitate the D3 ...
INDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding Shuwen Liu 1, Bernardo Cuenca Grau , Ian Horrocks , and Egor V. Kostylev2 1Department of Computer Science, University of Oxford, UK {shuwen.liu, bernardo.cuenca.grau, ian.horrocks}@cs.ox.ac.uk 2Department of Informatics, University of Oslo egork@ifi.uio.no Abstract
new attention-based GNN module for reasoning. Our joint reasoning algorithm on the working graph simultaneously updates the representation of both the KG entities and the QA context node, bridging the gap between the two sources of information. We evaluate QA-GNN on two question an-swering datasets that require reasoning with