Biomedical Knowledge Graph Refinement with Embedding and Logic Rules

• URL: http://arxiv.org/abs/2012.01031v1
• Date: Wed, 2 Dec 2020 08:55:07 GMT
• Title: Biomedical Knowledge Graph Refinement with Embedding and Logic Rules
• Authors: Sendong Zhao, Bing Qin, Ting Liu, Fei Wang
• Abstract summary: Many studies have demonstrated that reasoning upon the knowledge graph is effective in eliminating such conflicts and noises. This paper proposes a method BioGRER to improve the BioKG's quality. We employ the variational EM algorithm to optimize knowledge graph embedding and logic rule inference.
• Score: 29.933066247708638
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Currently, there is a rapidly increasing need for high-quality biomedical knowledge graphs (BioKG) that provide direct and precise biomedical knowledge. In the context of COVID-19, this issue is even more necessary to be highlighted. However, most BioKG construction inevitably includes numerous conflicts and noises deriving from incorrect knowledge descriptions in literature and defective information extraction techniques. Many studies have demonstrated that reasoning upon the knowledge graph is effective in eliminating such conflicts and noises. This paper proposes a method BioGRER to improve the BioKG's quality, which comprehensively combines the knowledge graph embedding and logic rules that support and negate triplets in the BioKG. In the proposed model, the BioKG refinement problem is formulated as the probability estimation for triplets in the BioKG. We employ the variational EM algorithm to optimize knowledge graph embedding and logic rule inference alternately. In this way, our model could combine efforts from both the knowledge graph embedding and logic rules, leading to better results than using them alone. We evaluate our model over a COVID-19 knowledge graph and obtain competitive results.

Related papers

• 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)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• Tertiary Lymphoid Structures Generation through Graph-based Diffusion [54.37503714313661]
  In this work, we leverage state-of-the-art graph-based diffusion models to generate biologically meaningful cell-graphs. We show that the adopted graph diffusion model is able to accurately learn the distribution of cells in terms of their tertiary lymphoid structures (TLS) content.
  arXiv  Detail & Related papers  (2023-10-10T14:37:17Z)
• Applying BioBERT to Extract Germline Gene-Disease Associations for Building a Knowledge Graph from the Biomedical Literature [0.0]
  This paper presents SimpleGermKG, an automatic knowledge graph construction approach that connects germline genes and diseases. For the extraction of genes and diseases, we employ BioBERT, a pre-trained BERT model on biomedical corpora. For semantic relationships between articles, genes, and diseases, we implemented a part-whole relation approach. Our knowledge graph contains 297 genes, 130 diseases, and 46,747 triples.
  arXiv  Detail & Related papers  (2023-09-11T18:05:12Z)
• Multimodal brain age estimation using interpretable adaptive population-graph learning [58.99653132076496]
  We propose a framework that learns a population graph structure optimized for the downstream task. An attention mechanism assigns weights to a set of imaging and non-imaging features. By visualizing the attention weights that were the most important for the graph construction, we increase the interpretability of the graph.
  arXiv  Detail & Related papers  (2023-07-10T15:35:31Z)
• Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks [12.896135204106423]
  This study aims to apply state-of-the-art knowledge graph embedding models in the context of a recent biomedical knowledge graph, BioKG. We achieve a three-fold improvement in terms of performance based on the HITS@10 score over previous work on the same biomedical knowledge graph. Results suggest that knowledge learnt from large biomedical knowledge graphs can be transferred to such downstream use cases.
  arXiv  Detail & Related papers  (2023-05-31T16:04:25Z)
• BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis [0.4893345190925178]
  We propose an end-to-end approach for knowledge extraction and analysis from biomedical clinical notes. The proposed framework can successfully extract relevant structured information with high accuracy.
  arXiv  Detail & Related papers  (2023-04-21T14:45:33Z)
• Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study [62.376800537374024]
  We study scientific LMs for KG completion, exploring whether we can tap into their latent knowledge to enhance biomedical link prediction. We integrate the LM-based models with KG embedding models, using a router method that learns to assign each input example to either type of model and provides a substantial boost in performance.
  arXiv  Detail & Related papers  (2021-06-17T17:55:33Z)
• Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs [10.244651735862627]
  We conduct an empirical study based on the real-world task of drug repurposing. We formulate this task as a link prediction problem where both compounds and diseases correspond to entities in a knowledge graph. We propose a new method, PoLo, that combines policy-guided walks based on reinforcement learning with logical rules.
  arXiv  Detail & Related papers  (2021-03-18T16:46:11Z)
• Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings [8.835844347471626]
  We train several state-of-the-art knowledge graph embedding models on the SNOMED-CT knowledge graph. We make a case for the importance of leveraging the multi-relational nature of knowledge graphs for learning biomedical knowledge representation.
  arXiv  Detail & Related papers  (2020-06-24T14:47:33Z)
• A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts [81.4757750425247]
  We study question answering over a dynamic textual environment. We develop a graph neural network over the constructed graph, and train the model in an end-to-end manner.
  arXiv  Detail & Related papers  (2020-04-25T04:53:54Z)

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.