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A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks

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BRIEFINGS IN BIOINFORMATICS 2022年第5期23卷 bbac363-bbac363页

作者： Ren, Zhong-Hao You, Zhu-Hong Yu, Chang-Qing Li, Li-Ping Guan, Yong-Jian Guo, Lu-Xiang Pan, Jie Northwestern Polytech Univ Sch Comp Sci Xian 710129 Peoples R China Xijing Univ Sch Elect Informat Xian 710100 Peoples R China Longdong Univ Coll Agr & Forestry Qingyang 745000 Peoples R China Xijing Univ Xian Peoples R China

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

关键词： drug-drug interactions biomedical knowledge graph graph neural network deep learning multi-feature aggregation

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Aspect-Level Sentiment Analysis via a Syntax-based Neural Network

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND language processing 2022年 30卷 2568-2583页

作者： Zhang, Richong Chen, Qianben Zheng, Yaowei Mensah, Samuel Mao, Yongyi Beihang Univ Sch Comp Sci SKLSDE Engn Beijing 100190 Peoples R China Univ Sheffield Dept Comp Sci Sheffield S10 2TN S Yorkshire England Univ Ottawa Sch Elect Engn Comp Sci Ottawa ON K1N 6N5 Canada

Aspect-level sentiment classification (ALSC);a fine-grained task of sentiment analysis holds the promise of machines communicating knowledge of different aspects of a product/service with humans. Specifically, ALSC aims at inferring the sentiment expressed toward a specific aspect term in a user-generated textual content. It is a long-standing problem in sentiment analysis, and its usefulness cannot be underestimated. Recent studies employ the dependency tree and restructure it to model the syntactic relationship among words in text. However, the restructuring process destructs the structural information of the original dependency tree. As a solution, we first construct a syntax graph that preserves the structural information of the dependency tree. We then propose a reliable syntax-based neural network model that performs a thorough search on the syntax graph to effectively find the relevant contextual information with respect to the aspect term for the sentence encoding. Noting that dependency trees parsed from existing dependency parsers may contain incorrect syntactic dependencies due to grammatical errors in a sentence, we adopt a convolutional layer that takes into account the relations among features of words in a local neighborhood to mitigate the issues brought by incorrect syntactic dependencies. Our results on benchmark datasets demonstrate that our model outperforms the previous methods and achieves state-of-the-art results for the ALSC task.

关键词： Syntactics Task analysis Context modeling Neural networks Sentiment analysis Feature extraction Linguistics Sentiment analysis natural language processing

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O-Dang at HODI and HaSpeeDe3: A Knowledge-Enhanced Approach to Homotransphobia and Hate Speech Detection in Italian 8

O-Dang at HODI and HaSpeeDe3: A Knowledge-Enhanced Approach ...

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8th Evaluation Campaign of natural language processing and Speech Tools for Italian. Final workshop, EVALITA 2023

作者： Di Bonaventura, Chiara Muti, Arianna Stranisci, Marco Antonio King's College London United Kingdom University of Bologna Italy University of Turin Italy

This paper describes our methods implemented during the EVALITA 2023 campaign for homotransphobia (HODI task) and hate speech detection (HaSpeeDe3 task) in Italian. We present three knowledge-enhanced approaches, namely via triple verbalisation, via prompting and via a majority vote, and we compare them to the AlBERTo baseline. These systems leverage the knowledge graph O-Dang, which contains information about named entities in Italian dangerous speech. Our knowledge-enhanced systems outperformed all the competition's baselines. Our best submissions achieved the macro-F1 score of 0.912 for HaSpeeDe3 and 0.795 for HODI, reaching the 1st and 3rd place, respectively. These results were achieved by using our baseline for HODI, and a majority voting approach for HaSpeeDe3. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Knowledge graph

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A survey: knowledge graph entity alignment research based on graph embedding

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第9期57卷 229-229页

作者： Zhu, Beibei Wang, Ruolin Wang, Junyi Shao, Fei Wang, Kerun Liaoning Normal Univ Sch Comp Sci & Artificial Intelligence Liushu South St Dalian 116081 Liaoning Peoples R China Jilin Univ Sch Comp Sci & Technol Qianjin St Changchun 130012 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Ed Qianjin St Changchun 130012 Jilin Peoples R China

Entity alignment (EA) aims to automatically match entities in different knowledge graphs, which is beneficial to the development of knowledge-driven applications. Representation learning has powerful feature capture capability and it is widely used in the field of natural language processing. Compared with traditional EA methods, EA methods based on representation learning have better performance and efficiency. Hence, we summarize and analyze the representative EA approaches based on representation learning in this paper. We present the problem description and data preprocessing for EA and other related fundamental knowledge. We propose a new EA framework for the latest models, which includes information aggregation module, entity alignment module, and post-alignment module. based on these three modules, the various technologies are described in detail. In the experimental part, we first explore the effect of EA direction on model performance. Then, we classify the models into different categories in terms of alignment inference strategy, noise filtering strategy, and whether additional information is utilized. To ensure fairness, we perform the comparative analysis of the performance of the models within the categories separately on different datasets. We investigate both unimodal and multimodal EA. Finally, we present future research perspectives based on the shortcomings of existing EA methods.

关键词： Entity alignment Knowledge graph natural language processing Representation learning

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Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale graph 22

Embedding Compression with Hashing for Efficient Representat...

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28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KKD)

作者： Yeh, Chin-Chia Michael Gu, Mengting Zheng, Yan Chen, Huiyuan Ebrahimi, Javid Zhuang, Zhongfang Wang, Junpeng Wang, Liang Zhang, Wei Visa Res Austin TX 78759 USA

ISBN: (纸本)9781450393850

graph neural networks (GNNs) are deep learning models designed specifically for graph data, and they typically rely on node features as the input to the first layer. When applying such a type of network on the graph without node features, one can extract simple graph-based node features (e.g., number of degrees) or learn the input node representations (i.e., embeddings) when training the network. While the latter approach, which trains node embeddings, more likely leads to better performance, the number of parameters associated with the embeddings grows linearly with the number of nodes. It is therefore impractical to train the input node embeddings together with GNNs within graphics processing unit (GPU) memory in an end-to-end fashion when dealing with industrial-scale graph data. Inspired by the embedding compression methods developed for natural language processing (NLP) tasks, we develop a node embedding compression method where each node is compactly represented with a bit vector instead of a floating-point vector. The parameters utilized in the compression method can be trained together with GNNs. We show that the proposed node embedding compression method achieves superior performance compared to the alternatives.

关键词： graph neural network compression low-bit embeddings

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Team Cadence at MEDIQA-Chat 2023: Generating, augmenting and summarizing clinical dialogue with large language models 5

Team Cadence at MEDIQA-Chat 2023: Generating, augmenting and...

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5th workshop on Clinical natural language processing, ClinicalNLP 2023. held at ACL 2023

作者： Sharma, Ashwyn Feldman, David I. Jain, Aneesh Cadence Solutions United States Massachusetts General Hospital Harvard University United States Virginia Polytechnic Institute and State University United States

ISBN: (纸本)9781959429883

This paper describes Team Cadence's winning submission to Task C of the MEDIQA-Chat 2023 shared tasks. We also present the set of methods, including a novel N-pass strategy to summarize a mix of clinical dialogue and an incomplete summarized note, used to complete Task A and Task B, ranking highly on the leaderboard amongst stable and reproducible code submissions. The shared tasks invited participants to summarize, classify and generate patient-doctor conversations. Considering the small volume of training data available, we took a data-augmentation-first approach to the three tasks by focusing on the dialogue generation task, i.e., Task C. It proved effective in improving our models' performance on Task A and Task B. We also found the BART architecture to be highly versatile, as it formed the base for all our submissions. Finally, based on the results shared by the organizers, we note that Team Cadence was the only team to submit stable and reproducible runs to all three tasks. © 2023 Association for Computational Linguistics.

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Scientific Paper Extractive Summarization Enhanced by Citation graphs

Scientific Paper Extractive Summarization Enhanced by Citati...

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2022 Conference on Empirical methods in natural language processing, EMNLP 2022

作者： Chen, Xiuying Li, Mingzhe Gao, Shen Yan, Rui Gao, Xin Zhang, Xiangliang Computational Bioscience Reseach Center KAUST Saudi Arabia Ant Group School of Computer Science and Technology Shandong University China Gaoling School of Artificial Intelligence Renmin University of China China University of Notre Dame United States

In a citation graph, adjacent paper nodes share related scientific terms and topics. The graph thus conveys unique structure information of document-level relatedness that can be utilized in the paper summarization task, for exploring beyond the intra-document information. In this work, we focus on leveraging citation graphs to improve scientific paper extractive summarization under different settings. We first propose a Multi-granularity Unsupervised Summarization model (MUS) as a simple and low-cost solution to the task. MUS finetunes a pre-trained encoder model on the citation graph by link prediction tasks. Then, the abstract sentences are extracted from the corresponding paper considering multi-granularity information. Preliminary results demonstrate that citation graph is helpful even in a simple unsupervised framework. Motivated by this, we next propose a graph-based Supervised Summarization model (GSS) to achieve more accurate results on the task when large-scale labeled data are available. Apart from employing the link prediction as an auxiliary task, GSS introduces a gated sentence encoder and a graph information fusion module to take advantage of the graph information to polish the sentence representation. Experiments on a public benchmark dataset show that MUS and GSS bring substantial improvements over the prior state-of-the-art model. © 2022 Association for Computational Linguistics.

关键词： graphic methods

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GTN-ED: Event Detection Using graph Transformer Networks 15

GTN-ED: Event Detection Using Graph Transformer Networks

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15th workshop on graph-based methods for natural language processing, Textgraphs 2021

作者： Dutta, Sanghamitra Ma, Liang Saha, Tanay Kumar Lu, Di Tetreault, Joel Jaimes, Alejandro Carnegie Mellon University United States Dataminr United States

ISBN: (纸本)9781954085381

Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection. However, they often only leverage the edges (dependencies) between words, and discard the dependency labels (e.g., nominal-subject), treating the underlying graph edges as homogeneous. In this work, we propose a novel framework for incorporating both dependencies and their labels using a recently proposed technique called graph Transformer Networks (GTN). We integrate GTNs to leverage dependency relations on two existing homogeneousgraph-based models, and demonstrate an improvement in the F1 score on the ACE dataset. © 2021 Association for Computational Linguistics.

关键词： Syntactics

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Deep Learning on graphs: methods and Applications (DLG-KDD2022) 22

Deep Learning on Graphs: Methods and Applications (DLG-KDD20...

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28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KKD)

作者： Wu, Lingfei Pei, Jian Tang, Jiliang Xia, Yinglong Guo, Xiaojie JD COM Silicon Valley Res Ctr Mountain View CA 94043 USA Simon Fraser Univ Burnaby BC Canada Michigan State Univ E Lansing MI USA Meta AI Menlo Pk CA USA

ISBN: (纸本)9781450393850

Deep Learning models are at the core of research in Artificial Intelligence research today. A tide in research for deep learning on graphs or graph neural networks. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including computer vision, natural language processing, program synthesis and analysis, financial security, Drug Discovery and so on. However, there are still many challenges regarding a broad range of the topics in deep learning on graphs, from methodologies to applications, and from foundations to the new frontiers of GNNs. This international workshop on "Deep Learning on graphs: Method and Applications (DLG-KDD'22)" aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to above challenges.

关键词： graph neural network deep learning graph representation learning graph mining

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MoSE: Modality Split and Ensemble for Multimodal Knowledge graph Completion

MoSE: Modality Split and Ensemble for Multimodal Knowledge G...

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2022 Conference on Empirical methods in natural language processing, EMNLP 2022

作者： Zhao, Yu Cai, Xiangrui Wu, Yike Zhang, Haiwei Zhang, Ying Zhao, Guoqing Jiang, Ning College of Cyber Science TKLNDST Nankai University Tianjin China School of Journalism and Communication Nankai University Tianjin China College of Computer Science Nankai University Tianjin China Mashang Consumer Finance Co Ltd

Multimodal knowledge graph completion (MKGC) aims to predict missing entities in MKGs. Previous works usually share relation representation across modalities. This results in mutual interference between modalities during training, since for a pair of entities, the relation from one modality probably contradicts that from another modality. Furthermore, making a unified prediction based on the shared relation representation treats the input in different modalities equally, while their importance to the MKGC task should be different. In this paper, we propose MoSE, a Modality Split representation learning and Ensemble inference framework for MKGC. Specifically, in the training phase, we learn modality-split relation embeddings for each modality instead of a single modality-shared one, which alleviates the modality interference. based on these embeddings, in the inference phase, we first make modality-split predictions and then exploit various ensemble methods to combine the predictions with different weights, which models the modality importance dynamically. Experimental results on three KG datasets show that MoSE outperforms state-of-the-art MKGC methods. Codes are available at https://***/OreOZhao/MoSE4MKGC. © 2022 Association for Computational Linguistics.

关键词： Knowledge graph

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