Node-based Knowledge Graph Contrastive Learning for Medical Relationship Prediction

• URL: http://arxiv.org/abs/2310.10138v1
• Date: Mon, 16 Oct 2023 07:27:43 GMT
• Title: Node-based Knowledge Graph Contrastive Learning for Medical Relationship Prediction
• Authors: Zhiguang Fan, Yuedong Yang, Mingyuan Xu, Hongming Chen
• Abstract summary: We propose a novel node-based contrastive learning method for knowledge graph embedding, NC-KGE. NC-KGE enhances knowledge extraction in embeddings and speeds up training convergence by constructing appropriate contrastive node pairs on Knowledge Graphs. For downstream task such as biochemical relationship prediction, we have incorporated a relation-aware attention mechanism into NC-KGE.
• Score: 5.935975805403955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The embedding of Biomedical Knowledge Graphs (BKGs) generates robust representations, valuable for a variety of artificial intelligence applications, including predicting drug combinations and reasoning disease-drug relationships. Meanwhile, contrastive learning (CL) is widely employed to enhance the distinctiveness of these representations. However, constructing suitable contrastive pairs for CL, especially within Knowledge Graphs (KGs), has been challenging. In this paper, we proposed a novel node-based contrastive learning method for knowledge graph embedding, NC-KGE. NC-KGE enhances knowledge extraction in embeddings and speeds up training convergence by constructing appropriate contrastive node pairs on KGs. This scheme can be easily integrated with other knowledge graph embedding (KGE) methods. For downstream task such as biochemical relationship prediction, we have incorporated a relation-aware attention mechanism into NC-KGE, focusing on the semantic relationships and node interactions. Extensive experiments show that NC-KGE performs competitively with state-of-the-art models on public datasets like FB15k-237 and WN18RR. Particularly in biomedical relationship prediction tasks, NC-KGE outperforms all baselines on datasets such as PharmKG8k-28, DRKG17k-21, and BioKG72k-14, especially in predicting drug combination relationships. We release our code at https://github.com/zhi520/NC-KGE.

Related papers

• Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion [69.55700751102376]
  Few-shot knowledge graph completion (FKGC) aims to predict missing facts for unseen relations with few-shot associated facts. Existing FKGC methods are based on metric learning or meta-learning, which often suffer from the out-of-distribution and overfitting problems. In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC)
  arXiv  Detail & Related papers  (2023-04-17T11:42:28Z)
• Enhancing Embedding Representations of Biomedical Data using Logic Knowledge [6.295638112781736]
  In this paper, we exploit logic rules to enhance the embedding representations of knowledge graph models on the PharmKG dataset. An R2N uses the available logic rules to build a neural architecture that reasons over KGE latent representations. In the experiments, we show that our approach is able to significantly improve the current state-of-the-art on the PharmKG dataset.
  arXiv  Detail & Related papers  (2023-03-23T13:38:21Z)
• Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation [92.73584302508907]
  We propose a knowledge graph with Dynamic structure and nodes to facilitate medical report generation with Contrastive Learning. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation.
  arXiv  Detail & Related papers  (2023-03-18T03:53:43Z)
• Analysis of Drug repurposing Knowledge graphs for Covid-19 [0.0]
  This study proposes a set of candidate drugs for COVID-19 using Drug repurposing knowledge graph (DRKG) DRKG is a biological knowledge graph constructed using a vast amount of open source biomedical knowledge. nodes and relation embeddings are learned using knowledge graph embedding models and neural network and attention related models.
  arXiv  Detail & Related papers  (2022-12-07T19:14:17Z)
• Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure [36.507635518425744]
  We propose a knowledge graph contrastive learning framework based on relation-symmetrical structure, KGE-SymCL. Our framework mines symmetrical structure information in KGs to enhance the discriminative ability of KGE models.
  arXiv  Detail & Related papers  (2022-11-19T16:30:29Z)
• KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion [37.92814873958519]
  Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the textitde-facto standard for knowledge graph completion. Most existing KGE methods suffer from the sparsity challenge, where it is harder to predict entities that appear less frequently in knowledge graphs. We propose a novel framework to alleviate the widespread sparsity in KGs with graph context and contrastive learning.
  arXiv  Detail & Related papers  (2022-08-16T09:17:40Z)
• Learning Intents behind Interactions with Knowledge Graph for Recommendation [93.08709357435991]
  Knowledge graph (KG) plays an increasingly important role in recommender systems. Existing GNN-based models fail to identify user-item relation at a fine-grained level of intents. We propose a new model, Knowledge Graph-based Intent Network (KGIN)
  arXiv  Detail & Related papers  (2021-02-14T03:21:36Z)
• RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding [50.010601631982425]
  This paper extends the random walk model (Arora et al., 2016a) of word embeddings to Knowledge Graph Embeddings (KGEs) We derive a scoring function that evaluates the strength of a relation R between two entities h (head) and t (tail) We propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph.
  arXiv  Detail & Related papers  (2021-01-25T13:31:29Z)
• Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction [54.93890176891602]
  We propose a diStance-aware Molecule graph Attention Network (S-MAN) tailored to drug-target binding affinity prediction. As a dedicated solution, we first propose a position encoding mechanism to integrate the topological structure and spatial position information into the constructed pocket-ligand graph. We also propose a novel edge-node hierarchical attentive aggregation structure which has edge-level aggregation and node-level aggregation.
  arXiv  Detail & Related papers  (2020-12-17T17:44:01Z)
• Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing [22.035580011316746]
  This paper proposes an inductive model for learning informative relation embeddings in graph structured data. The proposed model significantly outperforms the RGCN and state-of-the-art KGE models in few-shot learning tasks. We pose the drug discovery task as link prediction and learn embeddings for the biological entities that partake in the drug-repurposing knowledge graph.
  arXiv  Detail & Related papers  (2020-07-20T16:48:51Z)
• Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs [96.73259297063619]
  We consider a novel formulation, zero-shot learning, to free this cumbersome curation. For newly-added relations, we attempt to learn their semantic features from their text descriptions. We leverage Generative Adrial Networks (GANs) to establish the connection between text and knowledge graph domain.
  arXiv  Detail & Related papers  (2020-01-08T01:19:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.