Biomedical Knowledge Graph Embeddings with Negative Statements

• URL: http://arxiv.org/abs/2308.03447v1
• Date: Mon, 7 Aug 2023 10:08:25 GMT
• Title: Biomedical Knowledge Graph Embeddings with Negative Statements
• Authors: Rita T. Sousa, Sara Silva, Heiko Paulheim, Catia Pesquita
• Abstract summary: Explicitly considering negative statements has been shown to improve performance on tasks such as entity summarization. We propose a novel approach, TrueWalks, to incorporate negative statements into the knowledge graph representation learning process. We present a novel walk-generation method that is able to not only differentiate between positive and negative statements but also take into account the semantic implications of negation.
• Score: 1.7778609937758327
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A knowledge graph is a powerful representation of real-world entities and their relations. The vast majority of these relations are defined as positive statements, but the importance of negative statements is increasingly recognized, especially under an Open World Assumption. Explicitly considering negative statements has been shown to improve performance on tasks such as entity summarization and question answering or domain-specific tasks such as protein function prediction. However, no attention has been given to the exploration of negative statements by knowledge graph embedding approaches despite the potential of negative statements to produce more accurate representations of entities in a knowledge graph. We propose a novel approach, TrueWalks, to incorporate negative statements into the knowledge graph representation learning process. In particular, we present a novel walk-generation method that is able to not only differentiate between positive and negative statements but also take into account the semantic implications of negation in ontology-rich knowledge graphs. This is of particular importance for applications in the biomedical domain, where the inadequacy of embedding approaches regarding negative statements at the ontology level has been identified as a crucial limitation. We evaluate TrueWalks in ontology-rich biomedical knowledge graphs in two different predictive tasks based on KG embeddings: protein-protein interaction prediction and gene-disease association prediction. We conduct an extensive analysis over established benchmarks and demonstrate that our method is able to improve the performance of knowledge graph embeddings on all tasks.

Related papers

• The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models [3.1666540219908272]
  We conduct a comprehensive investigation into the properties of publicly available biomedical Knowledge Graphs. We establish links to the accuracy observed in real-world applications. We release all model predictions and a new suite of analysis tools.
  arXiv  Detail & Related papers  (2024-09-06T08:09:15Z)
• Learning Latent Graph Structures and their Uncertainty [63.95971478893842]
  Graph Neural Networks (GNNs) use relational information as an inductive bias to enhance the model's accuracy. As task-relevant relations might be unknown, graph structure learning approaches have been proposed to learn them while solving the downstream prediction task.
  arXiv  Detail & Related papers  (2024-05-30T10:49:22Z)
• Graph Relation Distillation for Efficient Biomedical Instance Segmentation [80.51124447333493]
  We propose a graph relation distillation approach for efficient biomedical instance segmentation. We introduce two graph distillation schemes deployed at both the intra-image level and the inter-image level. Experimental results on a number of biomedical datasets validate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-01-12T04:41:23Z)
• GraphCloak: Safeguarding Task-specific Knowledge within Graph-structured Data from Unauthorized Exploitation [61.80017550099027]
  Graph Neural Networks (GNNs) are increasingly prevalent in a variety of fields. Growing concerns have emerged regarding the unauthorized utilization of personal data. Recent studies have shown that imperceptible poisoning attacks are an effective method of protecting image data from such misuse. This paper introduces GraphCloak to safeguard against the unauthorized usage of graph data.
  arXiv  Detail & Related papers  (2023-10-11T00:50:55Z)
• Investigating the Interplay between Features and Structures in Graph Learning [6.436174170552484]
  In the past, it was believed that homophily strongly correlates with better node classification predictions of message-passing methods. More recently, researchers pointed out that such dichotomy is too simplistic as we can construct node classification tasks where graphs are completely heterophilic but the performances remain high. Our work investigates what happens when this strong assumption does not hold, by formalising two generative processes for node classification tasks.
  arXiv  Detail & Related papers  (2023-08-18T14:02:56Z)
• Benchmark datasets for biomedical knowledge graphs with negative statements [0.0]
  We present a collection of datasets for three relation prediction tasks that aim at circumventing the difficulties in building benchmarks for knowledge graphs with negative statements. We also generate knowledge graph embeddings each dataset with two popular path-based methods and evaluate the performance in each task. The results show that the negative statements can improve the performance of knowledge graph embeddings.
  arXiv  Detail & Related papers  (2023-07-21T17:25:21Z)
• Explainable Representations for Relation Prediction in Knowledge Graphs [0.0]
  We propose SEEK, a novel approach for explainable representations to support relation prediction in knowledge graphs. It is based on identifying relevant shared semantic aspects between entities and learning representations for each subgraph. We evaluate SEEK on two real-world relation prediction tasks: protein-protein interaction prediction and gene-disease association prediction.
  arXiv  Detail & Related papers  (2023-06-22T06:18:40Z)
• Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications [52.65389473899139]
  Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Recent works have shown that considering relationships between input data samples have a positive regularizing effect for the downstream task. We propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications.
  arXiv  Detail & Related papers  (2022-04-01T10:01:37Z)
• Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs [16.566710222582618]
  We show how knowledge graph embedding models can be affected by structural imbalance. We show how the graph topology can be perturbed to artificially alter the rank of a gene via random, biologically meaningless information.
  arXiv  Detail & Related papers  (2021-12-13T11:20:36Z)
• Unbiased Graph Embedding with Biased Graph Observations [52.82841737832561]
  We propose a principled new way for obtaining unbiased representations by learning from an underlying bias-free graph. Based on this new perspective, we propose two complementary methods for uncovering such an underlying graph.
  arXiv  Detail & Related papers  (2021-10-26T18:44:37Z)
• A Survey of Adversarial Learning on Graphs [59.21341359399431]
  We investigate and summarize the existing works on graph adversarial learning tasks. Specifically, we survey and unify the existing works w.r.t. attack and defense in graph analysis tasks. We emphasize the importance of related evaluation metrics, investigate and summarize them comprehensively.
  arXiv  Detail & Related papers  (2020-03-10T12:48:00Z)

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.