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Zero-shot text classification with knowledge resources under label-fully-unseen setting

NEUROCOMPUTING 2024年 610卷

作者： Wang, Yuqi Wang, Wei Chen, Qi Huang, Kaizhu Nguyen, Anh De, Suparna Xian Jiaotong Liverpool Univ Sch Adv Technol 111 Renai Rd Suzhou 215123 Jiangsu Peoples R China Xian Jiaotong Liverpool Univ Sch AI & Adv Comp 111 Renai Rd Suzhou 215123 Jiangsu Peoples R China Duke Kunshan Univ Data Sci Res Ctr 8 Duke Ave Kunshan 215316 Jiangsu Peoples R China Univ Liverpool Dept Comp Sci Liverpool L69 3BX England Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

Classification techniques are at the heart of many real-world applications, e.g. sentiment analysis, recommender systems and automatic text annotation, to process and analyse large-scale textual data in multiple fields. However, the effectiveness of natural language processing models can only be confirmed when a large amount of up-to-date training data is available. An unprecedented amount of data is continuously created, and new topics are introduced, making it less likely or even infeasible to collect labelled samples covering all topics for training models. We attempt to study the extreme case: there is no labelled data for model training, and the model, without being adapted to any specific dataset, will be directly applied to the testing samples. We propose a transformer-based framework to encode sentences in a contextualised way and leverage the existing knowledge resources, i.e. ConceptNet and WordNet, to integrate both descriptive and structural knowledge for better performance. To enhance the robustness of the model, we design an adversarial example generator based on relations from external knowledge bases. The framework is evaluated on both general and specific domain text classification datasets. Results show that the proposed framework can outperform the existing competitive state-of-the-art baselines, delivering new benchmark results.

关键词： Zero-shot learning knowledge graph embedding Natural language processing Textual analysis Multi-class classification

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A Novel Integrating Approach Between graph Neural Network and Complex Representation for Link Prediction in knowledge graph 14th

A Novel Integrating Approach Between Graph Neural Network an...

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14th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Thanh Le Loc Tran Bac Le Univ Sci Fac Informat Technol Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9789811982330;9789811982347

Deep learning brings high results in many problems, including Link Prediction on knowledge graphs (KGs). Although there are many techniques to implement deep learning into KGs, graph Neural Networks (GNNs) have recently emerged as a promising direction for representing the structure of KGs as input for a decoder. With this structural information, GNNs can help to retain more information from the original graph than conventional embeddings like TransE, TransH, RESCAL. As a result, the learning model achieves higher accuracy in predicting missing links between entities in the KG. Meanwhile, several studies have successfully demonstrated the intrinsic properties of the embedding process in complex space while keeping many binary relations (symmetric and asymmetric). Thus, this paper proposes deploying GNNs into complex space to increase the model's predictive capability. Another issue with GNNs is that they are susceptible to over-squashing when a large amount of information propagating between nodes is compressed down to a fixed representation space. As a result, we utilize a dynamic attention mechanism to minimize the adverse effects of these factors, and experiments on benchmark datasets have indicated that our proposal achieves a significant improvement compared to baseline models on almost all standard metrics.

关键词： knowledge graph embedding Link prediction graph convolutional networks Dynamic graph attention networks

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EaT-PIM: Substituting Entities in Procedural Instructions Using Flow graphs and embeddings 21st

EaT-PIM: Substituting Entities in Procedural Instructions Us...

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21st International Semantic Web Conference (ISWC)

作者： Shirai, Sola S. Kim, HyeongSik Rensselaer Polytech Inst Troy NY 12180 USA Robert Bosch LLC Sunnyvale CA USA

ISBN: (纸本)9783031194320;9783031194337

When cooking, it can sometimes be desirable to substitute ingredients for purposes such as avoiding allergens, replacing a missing ingredient, or exploring new flavors. More generally, the problem of substituting entities used in procedural instructions is challenging as it requires an understanding of how entities and actions in the instructions interact to produce the final result. To support the task of automatically identifying viable substitutions, we introduce a methodology to (1) parse instructions, using NLP tools and domain-specific ontologies, to generate flow graph representations, (2) train a novel embedding model which captures flow and interaction of entities in each step of the instructions, and (3) utilize the embeddings to identify plausible substitutions. Our embedding strategy aggregates nodes and dynamically computes intermediate results within the flow graphs, which requires learning embeddings for fewer nodes than typical graph embedding models. Our rule-based flow graph generation method shows comparable performance to machine learning-based work, while our embedding model outperforms baselines on a link-prediction task for ingredients in recipes.

关键词： Procedural instructions Cooking recipes Information extraction Ingredient substitution knowledge graph embedding

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ACRM: Integrating Adaptive Convolution with Recalibration Mechanism for Link Prediction 1

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15th International Conference on knowledge Science, Engineering, and Management (KSEM)

作者： Thanh Le Anh-Hao Phan Bac Le Univ Sci Fac Informat Technol Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam

ISBN: (数字)9783031109867

ISBN: (纸本)9783031109867;9783031109850

Link prediction is one of the important tasks of knowledge graphs. Recently, some convolutional models inherited from computer vision have shown many advantages over other methods. The convolution-based method is capable of exploiting the full interactions between entities and relations. However, the feature maps between channels generated from the convolution step are independent, and each channel operating on the local receptacle cannot use context information outside the local receptacle. Therefore, we propose a combined ACRM model based on adaptive convolution and recalibration mechanism. This idea is inherited from ConvR model. We evaluated our proposed model on standard benchmark datasets and achieved improved results compared to baseline models for the link prediction task. Especially those datasets with no inverse relation test leakage. The experimental results show that the recalibration mechanism has improved the limitation of feature maps in convolutional neural models. In addition, we also show the convergence of the loss value during training and demonstrate the model's ability to predict the head and tail entities on common evaluation metrics.

关键词： knowledge graph embedding Link prediction Representation learning Convolutional neural network Recalibration mechanism

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knowledge-Enhanced Learning for KG embedding 28

Knowledge-Enhanced Learning for KG Embedding

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IEEE 28th International Conference on Parallel and Distributed Systems (IEEE ICPADS)

作者： Zhang, Haodi Chen, Zhao Nie, Jinyin Jiang, Di Fan, Lixin Wu, Kaishun Shenzhen Univ Dept Comp Sci Shenzhen Peoples R China Shenzhen Univ WeBank Inst Fintech Webank Co Ltd Shenzhen Peoples R China Shenzhen Univ Shenzhen Audencia Business Sch Shenzhen Peoples R China Webank Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781665473156

knowledge graphs (KGs) have been widely used in recent years and proved to be useful in many applications. However, large scale KGs that are collected by crowdsourcing or constructed by automated algorithms, are usually incomplete. Most existing works focus on how to complete the KG based on embedding learning, but ignore the semantic properties of the relations in KGs. In this paper, we propose a knowledge-enhanced approach for KG embedding learning that leverages the explicit knowledge about the relation properties. The knowledge is used to enhance the training data and guide the model on data selection during each training iteration. We conduct a series of experiments on datasets WN18RR and FB15k-237. The experiment results show that our method outperforms the previous state-of-the-art models.

关键词： knowledge graph embedding data enhancement relation pattern

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Signal embeddings for Complex Logical Reasoning in knowledge graphs 1

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15th International Conference on knowledge Science, Engineering, and Management (KSEM)

作者： Wang, Kai Zhang, Chunhong Yu, Jibin Sun, Qi Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Minist Educ Key Lab Univ Wireless Commun Beijing 100876 Peoples R China

ISBN: (数字)9783031109836

ISBN: (纸本)9783031109836;9783031109829

Complex logical reasoning over knowledge graph is one of the fundamental tasks of Artificial Intelligence. Traditional approaches suffer from the incompleteness and noise of knowledge graph, making complex logical reasoning a challenging task. Recent methods propose to embed entities and first-order logic (FOL) queries in low-dimensional vector spaces. However, most of the current models cannot deal with logical negation. In addition, manymethods utilize neural networks tomodel relation projections, which require a large number of parameters and computational expense. In this work, we proposes SignalE that simplifies relation projection operations while being able to handle logical negation. We represent entities and queries by signal embeddings, which can be represented in both time domain and frequency domain interchangeably. The logical negation operation is handled by inverting the amplitude in each dimension of the frequency-domain form of the signal embedding. Furthermore, relational projection operations are simplified into translation between entities. Experiments demonstrate that SignalE significantly outperforms existing state-of-the-art methods on benchmark datasets.

关键词： Complex logical reasoning Query embedding knowledge graph embedding

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A knowledge graph based Approach for Apps Permission Recommendation

A Knowledge Graph based Approach for Apps Permission Recomme...

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IEEE International Conference on Web Services (IEEE ICWS) / IEEE World Congress on Services (IEEE SERVICES)

作者： Zhang, Huwei Feng, Zhiyong Xiao, Jianmao Ye, Zhixiong Fan, Guodong Chen, Shizhan Xue, Xiao Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Jiangxi Normal Univ Sch Software Nanchang 330022 Jiangxi Peoples R China

ISBN: (数字)9781665481434

ISBN: (纸本)9781665481434

The incompleteness of android documentation causes the lack of permission semantics, which in turn leads to permission misuse and threatens android system security. Previous studies have used API information to supplement permission semantics and recommended permissions, but they still lack sufficient contextual information, e.g. category and user review grades. To solve this problem, a novel knowledge graph based Convolutional Propagation Model (KGCP) is proposed for apps permission recommendation. In KGCP, we construct a knowledge graph (KG) to model the contextual information corresponding to apps and permissions. In order to regularize the representations of items, KGCP utilizes KG embedding technique to preserve its intrinsic structure while embedding entities and relationships into a continuous vector space. Focused on apps permission recommendation, KGCP learns the representation of entity through graph convolutional networks, which recursively aggregates information about neighbors in KG to mine potential preferences for app entities over permission entities. Experimental results show that KGCP improves by 16.7% over state-of-the-art apps permission recommendation methods, thus helps developers find the suitable permissions faster and more accurately.

关键词： knowledge graph Android Apps Permission Recommendation knowledge graph embedding graph Convolutional Network

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Understanding the performance of knowledge graph embeddings in drug discovery

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ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES 2022年 2卷

作者： Bonner, Stephen Barrett, Ian P. Ye, Cheng Swiers, Rowan Engkvist, Ola Hoyt, Charles Tapley Hamilton, William L. AstraZeneca Data Sci & Quantitat Biol Discovery Sci Cambridge England AstraZeneca Mol AI Discovery Sci R&D Gothenburg Sweden Harvard Med Sch Lab Syst Pharmacol Boston MA USA McGill Univ Sch Comp Sci Montreal PQ Canada Mila Quebec AI Inst Montreal PQ Canada

knowledge graphs (KG) and associated knowledge graph embedding (KGE) models have recently begun to be explored in the context of drug discovery and have the potential to assist in key challenges such as target identification. In the drug discovery domain, KGs can be employed as part of a process which can result in lab- based experiments being performed, or impact on other decisions, incurring significant time and financial costs and most importantly, ultimately influencing patient healthcare. For KGE models to have impact in this domain, a better understanding of not only of performance, but also the various factors which determine it, is required. In this study we investigate, over the course of many thousands of experiments, the predictive performance of five KGE models on two public drug discovery-oriented KGs. Our goal is not to focus on the best overall model or configuration, instead we take a deeper look at how performance can be affected by changes in the training setup, choice of hyperparameters, model parameter initialisation seed and different splits of the datasets. Our results highlight that these factors have significant impact on performance and can even affect the ranking of models. Indeed these factors should be reported along with model architectures to ensure complete reproducibility and fair comparisons of future work, and we argue this is critical for the acceptance of use, and impact of KGEs in a biomedical setting.

关键词： Drug discovery knowledge graph embedding knowledge grahps

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Drug-drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings

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BRIEFINGS IN BIOINFORMATICS 2021年第4期22卷 000-000页

作者： Dai, Yuanfei Guo, Chenhao Guo, Wenzhong Eickhoff, Carsten Fuzhou Univ Coll Math & Comp Sci Fuzhou Fujian Peoples R China Brown Univ Brown Ctr Biomed Informat Providence RI 02912 USA Fuzhou Univ Fuzhou Peoples R China

An interaction between pharmacological agents can trigger unexpected adverse events. Capturing richer and more comprehensive information about drug-drug interactions (DDIs) is one of the key tasks in public health and drug development. Recently, several knowledge graph (KG) embedding approaches have received increasing attention in the DDI domain due to their capability of projecting drugs and interactions into a low-dimensional feature space for predicting links and classifying triplets. However, existing methods only apply a uniformly random mode to construct negative samples. As a consequence, these samples are often too simplistic to train an effective model. In this paper, we propose a new KG embedding framework by introducing adversarial autoencoders (AAEs) based on Wasserstein distances and Gumbel-Softmax relaxation for DDI tasks. In our framework, the autoencoder is employed to generate high-quality negative samples and the hidden vector of the autoencoder is regarded as a plausible drug candidate. Afterwards, the discriminator learns the embeddings of drugs and interactions based on both positive and negative triplets. Meanwhile, in order to solve vanishing gradient problems on the discrete representation-an inherent flaw in traditional generative models-we utilize the Gumbel-Softmax relaxation and the Wasserstein distance to train the embedding model steadily. We empirically evaluate our method on two tasks: link prediction and DDI classification. The experimental results show that our framework can attain significant improvements and noticeably outperform competitive baselines. Supplementary information: Supplementary data and code are available at https://***/dyf0631/AAE_FOR_KG.

关键词： drug-drug interaction knowledge graph embedding adversarial learning Wasserstein distance

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TAP with ease: a generic recommendation system for trigger-action programming based on multi-modal representation learning

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APPLIED SOFT COMPUTING 2024年 166卷

作者： Wu, Gang Wang, Ming Wang, Feng Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China

The escalating popularity of smart devices has given rise to an increasing trend wherein users leverage customized trigger-action programming (TAP) rules within the Internet of Things (IoT) to automate various aspects of their lives. This article addresses the challenge of effectively combining functions provided by many smart devices and online services by introducing a novel multi-modal representation learning model called TAP-TAG. This model integrates both textual and graph structures inherent in TAP rules, offering a holistic method to rule recommendation. TAP-TAG comprises two branches: the knowledge graph embedding model, which projects triplets extracted from the TAP dataset into embeddings, and convolution neural networks that extract semantic features from the textual content of TAP rules. Extensive experiments are conducted on real- world TAP datasets to evaluate our model's ability to recommend relevant rules based on user preferences. The experimental results show that TAP-TAG can outperform the state-of-the-art method by 5% in Precision@5, indicating that TAP-TAG is highly effective in providing accurate and diverse recommendations for TAP rules.

关键词： Trigger-action programming knowledge graph embedding Natural language processing Multi-modal representation learning

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