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knowledge-Enhanced Attributed Multi-Task Learning for Medicine Recommendation

ACM TRANSACTIONS ON INFORMATION SYSTEMS 2023年第1期41卷 1-24页

作者： Zhang, Yingying Wu, Xian Fang, Quan Qian, Shengsheng Xu, Changsheng Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit 95 Zhongguancun Rd Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence 80 Zhongguancun Rd Beijing Peoples R China Tencent BeijingBldg8 Xibei Wang East Rd Beijing 100080 Peoples R China Peng Cheng Lab Shenzhen Peoples R China

Medicine recommendation systems target to recommend a set of medicines given a set of symptoms which play a crucial role in assisting doctors in their daily clinics. Existing approaches are either rule-based or supervised. However, the former heavily relies on expert labeling, which is time-consuming and costly to collect, and the latter suffers from the data sparse problem. To automate medicine recommendation on sparse data, we propose MedRec, which introduces two graphs in modeling: (1) a knowledge graph connecting diseases, medicines, symptoms, and examinations;(2) an attribute graph connecting medicines via shared attributes and molecular structures. These two graphs enhance the connectivity between symptoms and medicines, which thus alleviate the data sparse problem. By learning the interrelationship between diseases, medicines, symptoms and examinations and the inner relationship within medicine, we can acquire unified embedding representations of symptoms and medicines which can be used in medicine recommendation. The experimental results show that the proposed model outperforms state-of-the-art methods. In addition, we find that these two tasks: learning graph representation and medical recommendation can benefit each other.

关键词： Medicine recommendation knowledge graph embedding multi-task graph neural network

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TDN: Triplet Distributor Network for knowledge graph Completion

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2023年第12期35卷 13002-13014页

作者： Wang, Jiapu Wang, Boyue Gao, Junbin Li, Xiaoyan Hu, Yongli Yin, Baocai Beijing Univ Technol Beijing Artificial Intelligence Inst Fac Informat Technol Beijing Municipal Key Lab Multimedia & Intelligent Beijing 100124 Peoples R China Univ Sydney Business Sch Discipline Business Analyt Camperdown NSW 42006 Australia

Conventional knowledge graph Completion (KGC) methods typically map entities and relations to a unified space through the shared mapping matrix, and then interact with entities and relations to infer the missing items in the knowledge graph. Although this shared mapping matrix considers the suitability of all triplets, it neglects the specificity of each triplet. To solve this problem, we dynamically learn one information distributor for each triplet to exchange its specific information. In this paper, we propose a novel Triplet Distributor Network (TDN) for the knowledge graph completion task. Specifically, we adaptively learn one Triplet Distributor (TD) for each triplet to assist the interaction between the entity and relation. Furthermore, on the basis of TD, we creatively design the information exchange layer to dynamically propagate the information of the entity and relation, thus mutually enhancing entity and relation representations. Except for several commonly-used knowledge graph datasets, we still implement the link prediction task on the social-relational and medical datasets to test the proposed method. Experimental results demonstrate that the proposed method performs better than existing state-of-the-art KGC methods. The source codes of this paper are available at https://***/TDN for knowledge graph ***.

关键词： Triplet distributor network knowledge graph completion knowledge graph embedding attention mechanism

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Trust-SIoT: Toward Trustworthy Object Classification in the Social Internet of Things

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第2期20卷 1210-1223页

作者： Sagar, Subhash Mahmood, Adnan Wang, Kai Sheng, Quan Z. Pabani, Jitander Kumar Zhang, Wei Emma Macquarie Univ Sch Comp Sydney NSW 2109 Australia Deakin Univ Sch Informat Technol Melbourne Vic 3125 Australia Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ Malaga Sch Telecommun Engn Malaga 29016 Spain Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia

The recent emergence of the promising paradigm of the Social Internet of Things (SIoT) is a result of an intelligent amalgamation of the social networking concepts with the Internet of Things (IoT) objects (also referred to as "things") in an attempt to unravel the challenges of network discovery, navigability, and service composition. This is realized by facilitating the IoT objects to socialize with one another, i.e., similar to the social interactions amongst human beings. A fundamental issue that mandates careful attention is to thus establish, and over time, maintain trustworthy relationships amongst these IoT objects. Therefore, a trust framework for SIoT must include object-object interactions, the aspects of social relationships, credible recommendations, etc., however, the existing literature has only focused on some aspects of trust by primarily relying on the conventional approaches that govern linear relationships between input and output. In this paper, an artificial neural network-based trust framework, Trust-SIoT, has been envisaged for identifying the complex non-linear relationships between input and output in a bid to classify trustworthy objects. Moreover, Trust-SIoT has been designed for capturing a number of key trust metrics as input, i.e., direct trust by integrating both current and past interactions, reliability and benevolence of an object, credible recommendations, and the degree of relationship by employing knowledge graph embedding. Finally, we have performed extensive experiments to evaluate the performance of Trust-SIoT vis-a-vis state-of-the-art heuristics on two real-world datasets. The results demonstrate that Trust-SIoT achieves a higher F1-score and lower MAE and MSE scores.

关键词： Trust management social Internet of Things knowledge graph embedding social relationships reliability benevolence

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GOGCN: graph Convolutional Network on Gene Ontology for Functional Similarity Analysis of Genes

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第2期20卷 1053-1064页

作者： Tian, Zhen Fang, Haichuan Teng, Zhixia Ye, Yangdong Zhengzhou Univ Dept Sch Informat Engn Zhengzhou 450001 Henan Peoples R China Northeast Forestry Univ Dept Coll Informat & Comp Engn Harbin 150040 Heilongjiang Peoples R China

The measurement of gene functional similarity plays a critical role in numerous biological applications, such as gene clustering, the construction of gene similarity networks. However, most existing approaches still rely heavily on traditional computational strategies, which are not guaranteed to achieve satisfactory performance. In this study, we propose a novel computational approach called GOGCN to measure gene functional similarity by modeling the Gene Ontology (GO) through graph Convolutional Network (GCN). GOGCN is a graph-based approach that performs sufficient representation learning for terms and relations in the GO graph. First, GOGCN employs the GCN-based knowledge graph embedding (KGE) model to learn vector representations (i.e., embeddings) for all entities (i.e., terms). Second, GOGCN calculates the semantic similarity between two terms based on their corresponding vector representations. Finally, GOGCN estimates gene functional similarity by making use of the pair-wise strategy. During the representation learning period, GOGCN promotes semantic interaction between terms through GCN, thereby capturing the rich structural information of the GO graph. Further experimental results on various datasets suggest that GOGCN is superior to the other state-of-the-art approaches, which shows its reliability and effectiveness.

关键词： Semantics Annotations Representation learning Predictive models Integrated circuit modeling Computational modeling Proteins graph convolutional network knowledge graph embedding Gene Ontology gene functional similarity

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Multi-task feature and structure learning for user-preference based knowledge-aware recommendation

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NEUROCOMPUTING 2023年第1期532卷 43-55页

作者： Shu, Hang Huang, Jun Chinese Acad Sci Shanghai Adv Res Inst 99 Haike RdZhangjiang Hitech Pk Shanghai 201210 Peoples R China Univ Chinese Acad Sci 1 Yanqihu East Rd Beijing 101408 Peoples R China

Data sparsity and long-tail items recommendation, two typical problems of recommender systems, can be effectively alleviated by leveraging user-side information and item-side information. knowledge graph (KG) is a source of side information. Due to the specificity of data distribution, the current KG-enhanced recommendation algorithms have the problem of weak generalization and robustness (the performance varies greatly in multiple data sets). Moreover, the previous knowledge-aware recommendation models are insufficient to distill the collaborative signal and personal features from the collective behaviors of users while simultaneously distilling the connectivity and exclusive entity features from the knowledge graph on the item-side. In this paper, we propose a multi-task framework Multi-Rec with high flexibility and adaptability based on alternating learning, which consists of a recommender system (RS) task and a knowledge graph embedding (KGE) task. The RS task consists of user feature learning and user structure learning modules. Correspondingly, the KGE task consists of knowledge graph feature learning and knowledge graph structure learning modules. The correlation between each sub-learning task is carried out by designed cross units and exchange units. Through the end-to-end framework, our model generates high-efficiency node embeddings for downstream recommender tasks and outperforms several state-of-the-art baselines on four real-world datasets through extensive experiments.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Recommender system knowledge graph embedding Multi-task learning

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Relational data embeddings for feature enrichment with background information

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MACHINE LEARNING 2023年第2期112卷 687-720页

作者： Cvetkov-Iliev, Alexis Allauzen, Alexandre Varoquaux, Gael INRIA Saclay Soda 1 Rue Honore Estienne Orves F-91120 Palaiseau France ESPCI Paris 10 Rue Vauquelin F-75005 Paris France

For many machine-learning tasks, augmenting the data table at hand with features built from external sources is key to improving performance. For instance, estimating housing prices benefits from background information on the location, such as the population density or the average income. However, this information must often be assembled across many tables, requiring time and expertise from the data scientist. Instead, we propose to replace human-crafted features by vectorial representations of entities (e.g. cities) that capture the corresponding information. We represent the relational data on the entities as a graph and adapt graph-embedding methods to create feature vectors for each entity. We show that two technical ingredients are crucial: modeling well the different relationships between entities, and capturing numerical attributes. We adapt knowledge graph embedding methods that were primarily designed for graph completion. Yet, they model only discrete entities, while creating good feature vectors from relational data also requires capturing numerical attributes. For this, we introduce KEN: knowledge embedding with Numbers. We thoroughly evaluate approaches to enrich features with background information on 7 prediction tasks. We show that a good embedding model coupled with KEN can perform better than manually handcrafted features, while requiring much less human effort. It is also competitive with combinatorial feature engineering methods, but much more scalable. Our approach can be applied to huge databases, creating general-purpose feature vectors reusable in various downstream tasks.

关键词： Feature engineering Feature enrichment knowledge graph embedding

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DRGI: Deep Relational graph Infomax for knowledge graph Completion

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2023年第3期35卷 2486-2499页

作者： Liang, Shuang Shao, Jie Zhang, Dongyang Zhang, Jiasheng Cui, Bin Univ Elect Sci & Technol China Ctr Future Media Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Ctr Future Media Sch Comp Sci & Engn Chengdu 611731 Peoples R China Sichuan Artificial Intelligence Res Inst Yibin 644000 Peoples R China Peking Univ Dept Comp Sci Key Lab High Confidence Software Technol MOE Beijing 100871 Peoples R China

Recently, many knowledge graph embedding models for knowledge graph completion have been proposed, ranging from the initial translation-based models such as TransE to recent convolutional neural network (CNN) models such as ConvE. However, these models only focus on semantic information of knowledge graph and neglect the natural graph structure information. Although graph convolutional network (GCN)-based models for knowledge graph embedding have been introduced to address this issue, they still suffer from fact incompleteness, resulting in the unconnectedness of knowledge graph. To solve this problem, we propose a novel model called deep relational graph infomax (DRGI) with mutual information (MI) maximization which takes the benefit of complete structure information and semantic information together. Specifically, the proposed DRGI consists of two encoders which are two identical adaptive relational graph attention networks (ARGATs), corresponding to catching semantic information and complete structure information respectively. Our method establishes new state-of-the-art on the standard datasets for knowledge graph completion. In addition, by exploring the complete structure information, DRGI embraces the merits of faster convergence speed over existing methods and better predictive performance for entities with small indegree.

关键词： knowledge engineering Semantics Tensors Convolutional neural networks Predictive models Convolution Feature extraction Mutual information maximization graph attention networks knowledge graph embedding knowledge graph completion

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knowledge graph Completion by Jointly Learning Structural Features and Soft Logical Rules

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2023年第3期35卷 2724-2735页

作者： Li, Weidong Peng, Rong Li, Zhi Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China Guangxi Normal Univ Coll Comp Sci & Informat Technol Guilin 541004 Peoples R China

With the rapid development and widespread application of knowledge graphs (KGs) in many artificial intelligence tasks, a large number of efforts have been made to refine them and increase their quality. Knowedge graph embedding (KGE) has become one of the main refinement tasks, which aims to predict missing facts based on existing ones in KGs. However, there are still mainly two difficult unresolved challenges: (i) how to leverage the local structural features of entities and the potential soft logical rules to learn more expressive embedding of entites and relations;and (ii) how to combine these two learning processes into one unified model. To conquer these problems, we propose a novel KGE model named JSSKGE, which can Jointly learn the local Structural features of entities and Soft logical rules. First, we employ graph attention networks which are specially designed for graph-structured data to aggregate the local structural information of nodes. Then, we utilize soft logical rules implicated in KGs as an expert to further rectify the embeddings of entities and relations. By jointly learning, we can obtain more informative embeddings to predict new facts. With experiments on four commonly used datasets, the JSSKGE obtains better performance than state-of-the-art approaches.

关键词： Task analysis Predictive models Training knowledge engineering Deep learning Neural networks Convolutional neural networks knowledge graph completion knowledge graph embedding graph attention neural networks link prediction soft logical rules

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graph-Based Non-Sampling for knowledge graph Enhanced Recommendation

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2023年第9期35卷 9462-9475页

作者： Liang, Shuang Shao, Jie Zhang, Jiasheng Cui, Bin Univ Elect Sci & Technol China Ctr Future Media Sch Comp Sci & Engn Chengdu 611731 Peoples R China Sichuan Artificial Intelligence Res Inst Yibin 644000 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China Peking Univ Dept Comp Sci Key Lab High Confidence Software Technol MOE Beijing 100871 Peoples R China

knowledge graph (KG) enhanced recommendation, which aims to solve the cold start and explainability in recommender systems, has attracted considerable research interest recently. Existing recommender systems usually focus on implicit feedback such as purchase history without negative feedback. Most of them apply the negative sampling strategy to deal with the implicit feedback data, which may ignore the latent positive user-item interaction. Some other works adopt the non-sampling strategy that treats all non-observed interactions as negative samples and assigns a weight for each negative sample to represent the probability that this sample is a positive sample. However, they use a simple and intuitive weight assignment strategy and cannot catch the latent relationship from all interaction data. To address these problems, we consider graph structure information of both user-item interaction and knowledge graph, and propose a graph-based Non-Sampling strategy to achieve efficient performance in knowledge graph enhanced Recommendation (GNSKR). GNSKR utilizes node centrality to significantly improve recommendation performance with low computation cost. Meanwhile, we combine knowledge graph embedding and recommendation task with a local aggregation block, which efficiently catches the high-order connection information in KG enhanced recommendation. Experiments on three datasets show that GNSKR embraces the state-of-the-art with competitive efficiency.

关键词： graph neural network knowledge graph enhanced recommendation knowledge graph embedding non-sampling strategy

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Interactive optimization of relation extraction via knowledge graph representation learning

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JOURNAL OF VISUALIZATION 2024年第2期27卷 197-213页

作者： Liu, Yuhua Ma, Yuming Zhang, Yong Yu, Rongdong Zhang, Zhenwei Meng, Yuwei Zhou, Zhiguang Hangzhou Dianzi Univ Intelligent Big Data Visualizat Lab Hangzhou 310000 Peoples R China Zhejiang Prov Energy Grp Co Ltd Sci & Informatizat Dept Hangzhou 310000 Peoples R China

Relation extraction is a vital task in constructing large-scale knowledge graphs, aiming to identify factual relations between entities from plain texts and generate triples. However, it is inevitable that a large amount of noise will be generated and should be given special attention;otherwise, they will seriously downgrade the performance of knowledge reasoning. In this paper, we propose a visual analytics system that facilitates automatic extraction and interactive optimization of relations between entities, enabling users to refine these extraction results with low confidence. First, a triple-based embedding method is designed to provide an overview of the triples by capturing the semantic similarity between entities and relations. Then, the contextual information in the embedding space is utilized to evaluate the correctness of triples and infer more probable relations for correction. Finally, a visual analysis system integrating the above method and multiple coordinated views is developed, enabling the higher-quality data corrected by users to assist in achieving iterative optimization of the relation extraction model in an interpretable way. Case studies based on real-world datasets and expert interviews further demonstrate the effectiveness of the system for effective analysis and exploration of the knowledge graph relation extraction.

关键词： Relation extraction knowledge graph knowledge graph embedding Interactive optimization

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