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检索条件"主题词=Knowledge Graph embedding"

共 569 条记录，以下是481-490 订阅

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Relation-Aware Neighborhood Aggregation for Cross-lingual Entity Alignment 24

Relation-Aware Neighborhood Aggregation for Cross-lingual En...

引用

24th IEEE International Conference on Information Fusion (FUSION)

作者： Liu, Yuanna Geng, Jie Deng, Xinyang Jiang, Wen Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (纸本)9781737749714

Cross-lingual entity alignment refers to linking entities in different language knowledge graphs if they are of identical meaning. Recent works focus on learning structure information of knowledge graphs and calculate the distance of entity embeddings for entity alignment. However, the GCN-based methods may bring noise from neighbors due to the heterogeneity of knowledge graphs. Besides, relations, as inherent attribute of knowledge graph, should be merged into the structure learning. In this paper, a relation-aware neighborhood aggregation model RANA is proposed to solve cross-lingual entity alignment task. The specific relation semantics are modeled to modify the aggregation weights of neighbors. CSLS and knowledge graph completion are introduced to enhance the alignment metric and structural information respectively. Experiments on real-world datasets demonstrate that RANA significantly outperforms other baselines in alignment accuracy and robustness.

关键词： knowledge graph embedding Entity alignment Relation-aware graph attention network

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knowledge-enhanced Spherical Representation Learning for Text Classification

Knowledge-enhanced Spherical Representation Learning for Tex...

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SIAM International Conference on Data Mining (SDM)

作者： Ennajari, Hafsa Bouguila, Nizar Bentahar, Jamal Concordia Univ Concordia Inst Informat Syst Engn CIISE Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9781611977172

We introduce knowledge-enhanced Spherical Representation Learning (K-SRL), a generative probabilistic model of text documents that combines word embeddings and knowledge graph embeddings to effectively encode the semantic information of text and the related background knowledge into a low-dimensional representation. More specifically, the proposed model represents each text document as a combination of both words and entities linked to an external large knowledge graph and models them as points on the unit hypersphere using the von Mises-Fisher distribution. Furthermore, we develop an efficient variational Bayesian inference algorithm to learn unsupervised text embeddings in the spherical space. Experimental results on multiple benchmark datasets demonstrate that our model outperforms existing probabilistic models on common text classification tasks, including text categorization and sentiment analysis.

关键词： Representation learning von Mises-Fisher knowledge graph embedding Bayesian inference

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AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal Explanations 22

AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Cla...

引用

31st ACM Web Conference (WWW)

作者： Islam, Sk Mainul Bhattacharya, Sourangshu IIT Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9781450390965

Aspect level sentiment classification (ALSC) is a difficult problem with state-of-the-art models showing less than 80% macro-F1 score on benchmark datasets. Existing models do not incorporate information on aspect-aspect relations in knowledge graphs (KGs), e.g. DBpedia. Two main challenges stem from inaccurate disambiguation of aspects to KG entities, and the inability to learn aspect representations from the large KGs in joint training with ALSC models. We propose AR-BERT, a novel two-level global-local entity embedding scheme that allows efficient joint training of KG-based aspect embeddings and ALSC models. A novel incorrect disambiguation detection technique addresses the problem of inaccuracy in aspect disambiguation. We also introduce the problem of determining mode significance in multi-modal explanation generation, and propose a two step solution. The proposed methods show a consistent improvement of 2.5 - 4.1 percentage points, over the recent BERT-based baselines on benchmark datasets.

关键词： Sentiment Analysis knowledge graph embedding Explainable Deep Learning

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Loan Default Risk Prediction Using knowledge graph 14

Loan Default Risk Prediction Using Knowledge Graph

引用

14th International Conference on knowledge and Smart Technology (KST)

作者： Alam, Md Nurul Ali, Muhammad Masroor Bangladesh Univ Engn & Technol Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781665400145

Credit risk, also known as loan default risk, is one of the significant financial challenges in banking and financial institutions since it involves the uncertainty of the borrowers' ability to perform their contractual obligation. Banks and financial institutions rely on statistical and machine learning methods in predicting loan default to reduce the potential losses of issued loans. These machine learning applications may never achieve their full potential without the semantic context in the data. A knowledge graph is a collection of linked entities and objects that include semantic information to contextualize them. knowledge graphs allow machines to incorporate human expertise into their decision-making and provide context to machine learning applications. Therefore, we proposed a loan default prediction model based on knowledge graph technology to improve the prediction model's accuracy and interpretability. The experimental results demonstrated that incorporating knowledge graph embedding as features can boost the performance of the conventional machine learning classifiers in predicting loan default risk.

关键词： Loan default prediction knowledge graph embedding machine learning semantic data model

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Next-Generation Security Entity Linkage: Harnessing the Power of knowledge graphs and Large Language Models 23

Next-Generation Security Entity Linkage: Harnessing the Powe...

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16th ACM International Systems and Storage Conference (SYSTOR)

作者： Alfasi, Daniel Shapira, Tal Bremler-Barr, Anat Reichman Univ Dept Comp Sci Herzliyya Israel Tel Aviv Univ Dept Comp Sci Tel Aviv Israel

ISBN: (纸本)9781450399623

With the continuous increase in reported Common Vulnerabilities and Exposures (CVEs), security teams are overwhelmed by vast amounts of data, which are often analyzed manually, leading to a slow and inefficient process. To address cybersecurity threats effectively, it is essential to establish connections across multiple security entity databases, including CVEs, Common Weakness Enumeration (CWEs), and Common Attack Pattern Enumeration and Classification (CAPECs). In this study, we introduce a new approach that leverages the RotatE [4] knowledge graph embedding model, initialized with embeddings from Ada language model developed by OpenAI [3]. Additionally, we extend this approach by initializing the embeddings for the relations.

关键词： CVE CWE CAPEC knowledge graph embedding

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An End-to-End knowledge graph Based Question Answering Approach for COVID-19 8th

An End-to-End Knowledge Graph Based Question Answering Appro...

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8th Annual China Conference on Health Information Processing (CHIP)

作者： Qiao, Yinbo Yang, Zhihao Lin, Hongfei Wang, Jian Dalian Univ Technol Dalian 116024 Peoples R China

ISBN: (纸本)9789811998645;9789811998652

Question Answering based on knowledge graph (KG) has emerged as a popular research area in general domain. However, few works focus on the COVID-19 kg-based question answering, which is very valuable for biomedical domain. In addition, existing question answering methods rely on knowledge embedding models to represent knowledge (i.e., entities and questions), but the relations between entities are neglected. In this paper, we construct a COVID-19 knowledge graph and propose an end-to-end knowledge graph question answering approach that can utilize relation information to improve the performance. Experimental result shows that the effectiveness of our approach on the COVID-19 knowledge graph question answering. Our code and data are available at https:// ***/CHNcreater/COVID-19-KGQA.

关键词： COVID-19 knowledge graph knowledge graph embedding

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Self-learning and embedding Based Entity Alignment

Self-learning and Embedding Based Entity Alignment

引用

IEEE International Conference on Big knowledge (IEEE ICBK)

作者： Guan, Saiping Jin, Xiaolong Jia, Yantao Wang, Yuanzhuo Shen, Huawei Cheng, Xueqi Chinese Acad Sci Univ Chinese Acad Sci Sch Comp & Control Engn Inst Comp TechnolCAS Key Lab Network Data Sci & Beijing Peoples R China

ISBN: (纸本)9781538631201

Entity alignment aims to identify semantical matchings between entities from different groups. Traditional methods (e. g., attribute comparison based methods, clustering based methods, and active learning methods) are usually supervised by labelled data as prior knowledge. Since it is not trivial to label data for training, researchers have recently turned to unsupervised methods, and have thus developed similarity based methods, probabilistic methods, hierarchical graph model based methods, etc. As an important part of a knowledge graph, entities contain rich semantical information that can be well learned by knowledge graph embedding in a low-dimensional vector space. However, existing methods for entity alignment have paid little attention to knowledge graph embedding. In this paper, we propose a Self-learning and embedding based method for Entity Alignment, thus called SEEA, to iteratively find semantically matched entity pairs, which makes full use of semantical information contained in the attributes of entities. Experiments on two realistic datasets and comparison with the baselines validate the effectiveness and merits of the proposed method.

关键词： entity alignment self-learning knowledge graph embedding

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knowledge graph Representation Learning via Generated Descriptions 28th

Knowledge Graph Representation Learning via Generated Descri...

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28th International Conference on Applications of Natural Language to Information Systems (NLDB)

作者： Hu, Miao Lin, Zhiwei Marshall, Adele Queens Univ Belfast Sch Math & Phys Belfast Antrim North Ireland

ISBN: (纸本)9783031353192;9783031353208

knowledge graph representation learning (KGRL) aims to project the entities and relations into a continuous low-dimensional knowledge graph space to be used for knowledge graph completion and detecting new triples. Using textual descriptions for entity representation learning has been a key topic. However, the current work has two major constraints: (1) some entities do not have any associated descriptions;(2) the associated descriptions are usually phrases, and they do not contain enough information. This paper presents a novel KGRL method for learning effective embeddings by generating meaningful descriptive sentences from entities' connections. The experiments using four public datasets and a new proposed dataset show that the New Description-Embodied knowledge graph embedding (NDKGE for short) approach introduced in this paper outperforms most of the existing work in the task of link prediction. The code and datasets of this paper can be obtained from GitHub (https://***/MiaoHu-Pro/NDKGE.)

关键词： knowledge graph embedding Entity description Constructing new descriptions Link prediction

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Distributed representation learning of knowledge graph with diverse information 9

Distributed representation learning of knowledge graph with ...

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9th International Conference on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Guo, Wenzhong Dai, Yuanfei Chen, Yiyan Chen, Xing Xiong, Neal N. Fuzhou Univ Coll Math & Comp Sci Fuzhou Fujian Peoples R China Fuzhou Univ Key Lab Network Comp & Intelligent Informat Proc Fuzhou Fujian Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK USA

ISBN: (纸本)9781538694039

knowledge graph is a type of network structure in which nodes represent entities and edges indicate relations. However, as the network size explosively increases, the issues of data sparsity and computation inefficiency on large-scale knowledge graph become more difficult to manipulate and manage. knowledge graph embedding, which is a representation technique of embedding entities and relations in the knowledge graph into continuous, dense, and low-dimensional semantics vector spaces to tackle these challenges and endow the model with the abilities of knowledge fusion and inference, has recently attracted much attention. In this paper, we firstly introduce the overall framework and specific idea of embedding models. We then introduce two applications that apply KG embedding, compare the performance of the methods in these applications. Finally, we summarize several challenges to overcome, and provide some prospective future research directions such as deep learning network for approaches and applications.

关键词： knowledge graph embedding knowledge representation Deep learning Statistical relational learning

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Relational Learning with Hierarchical Attention Encoder and Recoding Validator for Few-Shot knowledge graph Completion 22

Relational Learning with Hierarchical Attention Encoder and ...

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37th Annual ACM Symposium on Applied Computing

作者： Yuan, Xu Xu, Chengchuan Li, Peng Chen, Zhikui Dalian Univ Technol Dalian Peoples R China

ISBN: (纸本)9781450387132

Few-shot knowledge graph complementation (FKGC) has gained broad interest, where each task aims to complete missing facts of the long tail relations by few-shot support instances in the knowledge graph (KG). Most previous FKGC models encode few-shot relations by aggregating the neighbor information of each entity but do not make fine-grained distinctions between neighbor relations and entities. Moreover, many of those models neglect the valuable information interaction between relations, which may reduce the representational performance of the model. In this paper, we propose HARV, a novel FKGC framework based on hierarchical attention encoder and relation recoding validator. Specifically, through the hierarchical attention encoder, we can aggregate fine-grained information at the relation-level and entity-level in the neighbors respectively, thus obtaining more accurate representations. Besides, through the relation recoding validator, we are able to capture the interaction between different relations, which can decrease the over-dependence on specific entities in the few-shot relation encoding stage. Experimental results on two datasets, NELL and Wiki, demonstrate that our model performs better than the state-of-theart baselines in different few-shot sizes. We also demonstrate the interpretability of our approach through a case study of different levels of attention for heterogeneous neighbors when encoding the central entity in the experiment.

关键词： Relational Learning Few-Shot knowledge graph Completion knowledge graph embedding Representation Learning Attention Mechanism

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