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Hyperbolic hierarchical graph attention network for knowledge graph completion

High Technology Letters 2024年第3期30卷 271-279页

作者： XU Hao CHEN Shudong QI Donglin TONG Da YU Yong CHEN Shuai Institute of Microelectronics of Chinese Academy of Sciences Beijing 100029P.R.China University of Chinese Academy of Sciences Beijing 100049P.R.China

Utilizing graph neural networks for knowledge embedding to accomplish the task of knowledge graph completion(KGC)has become an important research area in knowledge graph ***,the number of nodes in the knowledge graph increases exponentially with the depth of the tree,whereas the distances of nodes in Euclidean space are second-order polynomial distances,whereby knowledge embedding using graph neural networks in Euclidean space will not represent the distances between nodes *** paper introduces a novel approach called hyperbolic hierarchical graph attention network(H2GAT)to rectify this ***,the paper conducts knowledge representation in the hyperbolic space,effectively mitigating the issue of exponential growth of nodes with tree depth and consequent information ***,it introduces a hierarchical graph atten-tion mechanism specifically designed for the hyperbolic space,allowing for enhanced capture of the network structure inherent in the knowledge ***,the efficacy of the proposed H2GAT model is evaluated on benchmark datasets,namely WN18RR and FB15K-237,thereby validating its *** H2GAT model achieved 0.445,0.515,and 0.586 in the Hits@1,Hits@3 and Hits@10 metrics respectively on the WN18RR dataset and 0.243,0.367 and 0.518 on the FB15K-237 *** incorporating hyperbolic space embedding and hierarchical graph attention,the H2GAT model successfully addresses the limitations of existing hyperbolic knowledge embedding models,exhibiting its competence in knowledge graph completion tasks.

关键词： hyperbolic space link prediction knowledge graph embedding knowledge graph completion(KGC)

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Neural axiom network for knowledge graph reasoning

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SEMANTIC WEB 2024年第3期15卷 777-792页

作者： Li, Juan Chen, Xiangnan Yu, Hongtao Chen, Jiaoyan Zhang, Wen Zhejiang Univ Coll Comp Sci & Technol 38 Zheda Rd Hangzhou Peoples R China Univ Oxford Dept Comp Sci 15 Parks Rd Oxford OX1 3QD England Zhejiang Univ Sch Software Technol 1689 Jiangnan Rd Ningbo Peoples R China

knowledge graph reasoning (KGR) aims to infer new knowledge or detect noises, which is essential for improving the quality of knowledge graphs. Recently, various KGR techniques, such as symbolic- and embedding-based methods, have been proposed and shown strong reasoning ability. Symbolic-based reasoning methods infer missing triples according to predefined rules or ontologies. Although rules and axioms have proven effective, it is difficult to obtain them. embedding-based reasoning methods represent entities and relations as vectors, and complete KGs via vector computation. However, they mainly rely on structural information and ignore implicit axiom information not predefined in KGs but can be reflected in data. That is, each correct triple is also a logically consistent triple and satisfies all axioms. In this paper, we propose a novel NeuRal Axiom Network (NeuRAN) framework that combines explicit structural and implicit axiom information without introducing additional ontologies. Specifically, the framework consists of a KG embedding module that preserves the semantics of triples and five axiom modules that encode five kinds of implicit axioms. These axioms correspond to five typical object property expression axioms defined in OWL2, including ObjectPropertyDomain, ObjectPropertyRange, DisjointObjectProperties, IrreflexiveObjectProperty and AsymmetricObjectProperty. The KG embedding module and axiom modules compute the scores that the triple conforms to the semantics and the corresponding axioms, respectively. Compared with KG embedding models and CKRL, our method achieves comparable performance on noise detection and triple classification and achieves significant performance on link prediction. Compared with TransE and TransH, our method improves the link prediction performance on the Hits@1 metric by 22.0% and 20.8% on WN18RR-10% dataset, respectively.

关键词： knowledge graph reasoning knowledge graph embedding noise detection triple classification link prediction

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Multiple disease diagnoses using heterogeneous EHR curated knowledge graph and machine learning models

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APPLIED INTELLIGENCE 2025年第7期55卷 1-17页

作者： Dhiman, Shivani Thukral, Anjali Bedi, Punam Univ Delhi Dept Comp Sci Delhi India Keshav Mahavidyalaya Univ Delhi Delhi India

Artificial Intelligence (AI) can play a significant role by assisting healthcare professionals in disease diagnosis, which is a critical step towards a patient's treatment. Most of the research work in disease diagnosis systems predicts the presence or absence of a given single disease in a patient. However, there are only a few studies on multiple disease diagnoses, i.e., on detecting the presence of more than one disease at the same time. In this paper, we propose a framework for diagnosing multiple diseases using knowledge graph (KG), knowledge embeddings and Machine Learning (ML). KG is created to semantically organize heterogeneous clinical details extracted from Electronic Health Records (EHRs). Additionally, we present a detailed comparison and analysis of three disease diagnosis systems, Single Disease Single Diagnosis (SDSD), Multiple Disease Single Diagnosis (MDSD), and Multiple Disease Multiple Diagnosis (MDMD) using the MIMIC-III dataset on Chronic Heart Failure (CHF), Acute Respiratory Failure (ARF) and Acute Kidney Failure (AKF) diseases. The above disease diagnosis systems have been implemented and analysed with different ML algorithms, such as Logistic Regression (LR), Random Forest (RF), Na & iuml;ve Bayes (NB), and Support Vector Machine (SVM). Besides, detecting the probability of having multiple diseases at a time, the MDMD shows comparable results in comparison to SDSD and MDSD. This is being evaluated by using the Area Under Receiver Operating Characteristic (AUROC) and the Area Under Precision-Recall Curve (AUPRC) metrics. The MDMD system based on the proposed framework for multiple disease diagnosis predicts CHF, ARF and AKF in 91%, 74% and 79% of positive cases, respectively.

关键词： Electronic health record knowledge graph Machine learning knowledge graph embedding Disease diagnosis Multiple disease problem

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knowledge graph Representation Learning With Multi-Scale Capsule-Based embedding Model Incorporating Entity Descriptions

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IEEE ACCESS 2020年 8卷 203028-203038页

作者： Cheng, Jingwei Zhang, Fu Yang, Zhi Northeastern Univ Coll Comp Sci & Engn Shenyang 110169 Peoples R China

A knowledge graph (KG) is a directed graph with nodes as entities and edges as relations. KG representation learning (KGRL) aims to embed entities and relations in a KG into continuous low-dimensional vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. In this paper, we propose a KG embedding framework, namely MCapsEED (Multi-Scale Capsule-based embedding Model Incorporating Entity Descriptions). MCapsEED employs a Transformer in combination with a relation attention mechanism to identify the relation-specific part of an entity description and obtain the description representation of an entity. The structured and description representations of an entity are integrated into a synthetic representation. A 3-column matrix with each column a synthetic representation of an element of a triple is fed into a Multi-Scale Capsule-based embedding model to produce final representations of the head entity, the tail entity and the relation. Experiments show that MCapsEED achieves better performance than state-of-the-art embedding models for the task of link prediction on four benchmark datasets. Our code can be found at https://***/1780041410/McapsEED.

关键词： knowledge graph representation learning capsule network link prediction knowledge graph embedding

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Fast knowledge graph completion using graphics processing units

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2024年 190卷

作者： Lee, Chun-Hee Kang, Dong-oh Song, Hwa Jeon ETRI Intelligence Informat Res Div Daejeon 34129 South Korea Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea

knowledge graphs can be used in many areas related to data semantics such as question -answering systems, knowledge based systems. However, the currently constructed knowledge graphs need to be complemented for better knowledge in terms of relations. It is called knowledge graph completion. To add new relations to the existing knowledge graph by using knowledge graph embedding models, we have to evaluate N x N x R vector operations, where N is the number of entities and R is the number of relation types. It is very costly. In this paper, we provide an efficient knowledge graph completion framework on GPUs to get new relations using knowledge graph embedding vectors. In the proposed framework, we first define transformable to a metric space and then provide a method to transform the knowledge graph completion problem into the similarity join problem for a model which is transformable to a metric space . After that, to efficiently process the similarity join problem, we derive formulas using the properties of a metric space. Based on the formulas, we develop a fast knowledge graph completion algorithm. Finally, we experimentally show that our framework can efficiently process the knowledge graph completion problem.

关键词： knowledge graph embedding knowledge graph completion TransE Similarity join GPU processing

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Large-scale knowledge graph representation learning

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knowledge AND INFORMATION SYSTEMS 2024年第9期66卷 5479-5499页

作者： Badrouni, Marwa Katar, Chaker Inoubli, Wissem Univ Jendouba Fac Law Econ & Management Sci Jendouba Jendouba 8189 Tunisia Univ Artois Comp Sci Res Inst Lens CRIL CNRS UMR 8188 F-62300 Lens France

The knowledge graph emerges as powerful data structures that provide a deep representation and understanding of the knowledge presented in networks. In the pursuit of representation learning of the knowledge graph, entities and relationships undergo an embedding process, where they are mapped onto a vector space with reduced dimensions. These embeddings are progressively used to extract their information for a multitude of tasks in machine learning. Nevertheless, the increase data in knowledge graph has introduced a challenge, especially as knowledge graph embedding now encompass millions of nodes and billions of edges, surpassing the capacities of existing knowledge representation learning systems. In response to these challenge, this paper presents DistKGE, a distributed learning approach of knowledge graph embedding based on a new partitioning technique. In our experimental evaluation, we illustrate that the proposed approach improves the scalability of distributed knowledge graph learning with respect to graph size compared to existing methods in terms of runtime performances in the link prediction task aimed at identifying new links between entities within the knowledge graph.

关键词： knowledge graph knowledge graph embedding Distributed learning Translating embeddings (TransE)

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embedding-based Two-Stage Entity Alignment for Cross-Lingual knowledge graphs *

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2024年第2期40卷 317-339页

作者： Sun, Yuxiang Lee, Yongju Kyungpook Natl Univ Software Technol Res Ctr Daegu 41566 South Korea Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea

In the knowledge graph alignment process, most researchers focus only on heterogeneous entities, thereby, ignoring the impact of homogeneous entities on matching accuracy and efficiency. In this study, we propose a two-stage strategy that corresponds to homogeneous and heterogeneous entities. In the first stage, an embedding-based semantic clustering algorithm is applied to divide the semantics into multiple clusters, which are paired according to the centroid distance. Additionally, homogeneous entities were matched by combining the Linked Lists and k -dimensional trees. In the second stage, we propose an embedding-based graph convolutional neural network (E-GCN) model that assigns different weights to relations based on the aligned homogeneous entities in the first stage. Compared with other GCN-based models, the entity alignment (EA) accuracy of the E-GCN model was the best, and the training time was reduced by 43.2%. Experimental results reveal that the proposed two-stage method significantly improves EA performance compared with state -of -the -art EA models. Moreover, the Canberra semantic distance is most suitable for representing the similarity between entities and the exponential linear unit (ELU) activation function accelerated the convergence of the E-GCN model.

关键词： knowledge graph embedding entity alignment graph convolutional network heterogeneous entities homogeneous entities semantic distance calculation

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Demographic Aware Probabilistic Medical knowledge graph embeddings of Electronic Medical Records 19th

Demographic Aware Probabilistic Medical Knowledge Graph Embe...

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19th International Conference on Artificial Intelligence in Medicine (AIME)

作者： Guluzade, Aynur Kacupaj, Endri Maleshkova, Maria Univ Bonn Bonn Germany

ISBN: (纸本)9783030772109;9783030772116

Medical knowledge graphs (KGs) constructed from Electronic Medical Records (EMR) contain abundant information about patients and medical entities. The utilization of KG embedding models on these data has proven to be efficient for different medical tasks. However, existing models do not properly incorporate patient demographics and most of them ignore the probabilistic features of the medical KG. In this paper, we propose DARLING (Demographic Aware pRobabiListic medIcal knowledge embedding), a demographic-aware medical KG embedding framework that explicitly incorporates demographics in the medical entities space by associating patient demographics with a corresponding hyperplane. Our framework leverages the probabilistic features within the medical entities for learning their representations through demographic guidance. We evaluate DARLING through link prediction for treatments and medicines, on a medical KG constructed from EMR data, and illustrate its superior performance compared to existing KG embedding models.

关键词： Demographics Probabilistic medical knowledge graph knowledge graph embedding Electronic medical records

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KGScope: Interactive Visual Exploration of knowledge graphs With embedding-Based Guidance

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER graphICS 2024年第12期30卷 7702-7716页

作者： Yuan, Chao-Wen Hsuan Yu, Tzu-Wei Pan, Jia-Yu Lin, Wen-Chieh Natl Yang Ming Chiao Tung Univ Coll Comp Sci Hsinchu 30010 Taiwan Google Inc Mountain View CA 94043 USA

knowledge graphs have been commonly used to represent relationships between entities and are utilized in the industry to enhance service qualities. As knowledge graphs integrate data from a variety of sources, they can also be useful references for data analysts. However, there is a lack of effective tools to make the most of the rich information in knowledge graphs. Existing knowledge graph exploration systems are ineffective because they did not consider various user needs and characteristics of knowledge graphs. Exploratory approaches specifically designed to uncover and summarize insights in knowledge graphs have not been well studied yet. In this article, we propose KGScope that supports interactive visual explorations and provides embedding-based guidance to derive insights from knowledge graphs. We demonstrate KGScope with usage scenarios and assess its efficacy in supporting the exploration of knowledge graphs with a user study. The results show that KGScope supports knowledge graph exploration effectively by providing useful information and helping explore the entire network.

关键词： knowledge graphs Data visualization Task analysis Semantics Visual analytics Navigation Load modeling Interactive visual exploration knowledge graph knowledge graph embedding

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gMLP-KGE: a simple but efficient MLPs with gating architecture for link prediction

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APPLIED INTELLIGENCE 2024年第20期54卷 9594-9606页

作者： Zhang, Fu Qiu, Pengpeng Shen, Tong Cheng, Jingwei Li, Weijun North Minzu Univ Sch Comp Sci & Engn Yinchuan 750021 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110169 Peoples R China

Most existing knowledge graphs (KGs) suffer from incompleteness, which will be detrimental to a variety of downstream applications. Link prediction is the task of predicting missing links in the KGs and can effectively address the issue of incompleteness by knowledge graph embedding (KGE). ConvE, a relatively popular KGE model based on convolutional neural networks, has shown superiority in link prediction. Some subsequent extension models of ConvE achieve state-of-the-art performance by increasing complexity and training time, which result in a high risk of overfitting and a limited performance due to the large number of parameters concentrated in the fully connected projection layer. To address these challenges, we for the first time innovatively introduce and extend a recently simple network architecture gMLP (based on multi-layer perceptrons MLPs with gating) in vision applications for link prediction. We propose a simple and efficient model called gMLP-KGE, which consists of an embedding layer, an input layer, an extended gMLP layer, and an output layer. Extensive experiments show that the number of parameters of gMLP-KGE is close to that of ConvE and less than other extension models, while gMLP-KGE consistently performs well on seven datasets of different scales under most evaluation metrics.

关键词： knowledge graph embedding Link prediction gMLP

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