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检索条件"主题词=Knowledge Graph embedding"

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Decoupled knowledge Propagation Network and Sampling Strategies for Recommendation System 27

Decoupled Knowledge Propagation Network and Sampling Strateg...

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27th International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Kuo, Zhen-Yuan Lin, Bor-Shen Natl Taiwan Univ Sci & Technol Dept Informat Management Taipei Taiwan

ISBN: (纸本)9798350399509

State-of-the-art knowledge-graph-based models, such as RippleNet and CKAN, have been successfully applied to recommendation systems. These models make use of knowledge graph to expand the entity information, which is similar to ripples, so as to strengthen the correlation between the user's preference and the candidate item. In RippleNet and CKAN, however, the networks for learning the features of user preference and candidate item are mutually coupled, which might limit the recommendation performance, especially for multi-levels propagation. On the other hand, when entities are expanded over the knowledge graph, the amount of entities increases exponentially with the number of diffusion levels. In earlier research, random sampling was adopted to restrict the number of triples, which however may limit the coverage of the expanded knowledge for model learning as well as the prediction performance. To tackle these problems, we propose a decoupled knowledge propagation network that separates the user's preference and the candidate item to distinguish the candidate items better. Experiments conducted on the MovieLens 1M recommendation dataset and the Microsoft Satori knowledge graph show the proposed mode outperforms RippleNet and CKAN. In addition, three sampling strategies, including balanced sampling, non-duplication, and dynamic sampling were proposed to deal with the sampling issue, and experiments show such sampling strategies are effective and superimposed for the prediction performance.

关键词： Recommendation System RippleNet knowledge graph embedding Decoupled Network Sampling Strategy

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Effective Use of BERT in graph embeddings for Sparse knowledge graph Completion 22

Effective Use of BERT in Graph Embeddings for Sparse Knowled...

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37th Annual ACM Symposium on Applied Computing

作者： Liu, Xinglan Hussain, Hussain Razouk, Houssam Kern, Roman Know Ctr GmbH Graz Austria Graz Univ Technol Graz Austria

ISBN: (纸本)9781450387132

graph embedding methods have emerged as effective solutions for knowledge graph completion. However, such methods are typically tested on benchmark datasets such as Freebase, but show limited performance when applied on sparse knowledge graphs with orders of magnitude lower density. To compensate for the lack of structure in a sparse graph, low dimensional representations of textual information such as word2vec or BERT embeddings have been used. This paper proposes a BERT-based method (BERT-ConvE), to exploit transfer learning of BERT in combination with a convolutional network model ConvE. Comparing to existing text-aware approaches, we effectively make use of the context dependency of BERT embeddings through optimizing the features extraction strategies. Experiments on ConceptNet show that the proposed method outperforms strong baselines by 50% on knowledge graph completion tasks. The proposed method is suitable for sparse graphs as also demonstrated by empirical studies on ATOMIC and sparsified-FB15k-237 datasets. Its effectiveness and simplicity make it appealing for industrial applications.

关键词： knowledge graph embedding sparse knowledge graph language model context aware embedding BERT

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NeuralKG: An Open Source Library for Diverse Representation Learning of knowledge graphs 22

NeuralKG: An Open Source Library for Diverse Representation ...

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45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Zhang, Wen Chen, Xiangnan Yao, Zhen Chen, Mingyang Zhu, Yushan Yu, Hongtao Huang, Yufeng Xu, Yajing Zhang, Ningyu Xu, Zezhong Yuan, Zonggang Xiong, Feiyu Chen, Huajun Zhejiang Univ Sch Software Technol Hangzhou Zhejiang Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Huawei Technol Co Ltd Shenzhen Guangdong Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China Alibaba Zhejiang Univ Joint Inst Frontier Technol ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450387323

NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs. It implements three kinds of knowledge graph embedding (KGE) methods, including conventional KGEs, GNN-based KGEs, and Rule-based KGEs. With a unified framework, NeuralKG successfully reproduces link prediction results of these methods on benchmarks, freeing users from the laborious task of reimplementing them, especially for some methods originally written in non-python programming languages. Besides, NeuralKG is highly configurable and extensible. It provides various decoupled modules that can be mixed and adapted to each other. Thus with NeuralKG, developers and researchers can quickly implement their own designed models and obtain the optimal training methods to achieve the best performance efficiently. We built a website(1) to organize an open and shared KG representation learning community. The library, experimental methodologies,and model re-implement results of NeuralKG are all publicly released at https://***/zjukg/NeuralKG.

关键词： knowledge graph knowledge graph embedding Diverse Representation Learning Open Source Link Prediction

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Leveraging the Power of Echo State Network for Enhanced Temporal knowledge graph Reasoning

Leveraging the Power of Echo State Network for Enhanced Temp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Sun, Yi Wang, Zhe Wang, Kewen Griffith Univ Sch Informat & Commun Technol Brisbane Qld Australia

ISBN: (纸本)9798350359329;9798350359312

Temporal knowledge graphs (TKG) reasoning has emerged as a powerful methodology for prediction in various domains. Unlike traditional knowledge graphs(KG), TKGs introduce a critical time dimension to the graph structure. Existing TKG reasoning methods expose two important limitations. The first is they are primarily designed to predict the immediate next time step. When applied to multi-step predictions, this leads to an iterative process that compromises prediction efficiency. The second limitation is their performance often depends on short-term memory and overlook long-term global information. To overcome these limitations, we introduce the Echo State Temporal knowledge graph Network (ESTN), an innovative approach leveraging the Echo State Networks (ESN) for TKG reasoning. Specifically, the ESTN contains a graph Embedder (GE) which integrates both short-term and long-term information within the TKG, enhancing the depth and accuracy of the data representation in graph latent space (embeddings). A Time Module (TM) is designed to facilitate direct prediction over multiple time steps, significantly improved the prediction efficiency. Extensive experiments demonstrate that our method not only show robust performance, but also offers an alternative approach to TKG reasoning, emphasizing how the distinct features of ESN can be effectively harnessed in TKG reasoning tasks.

关键词： Temporal knowledge graph Echo State Network knowledge graph embedding

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knowledge Augmented Inference Network for Natural Language Inference 3rd

Knowledge Augmented Inference Network for Natural Language I...

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3rd China Conference on knowledge graph and Semantic Computing (CCKS)

作者： Jiang, Shan Li, Bohan Liu, Chunhua Yu, Dong Beijing Adv Innovat Language Resources BLCU Beijing Peoples R China Beijing Language & Culture Univ Beijing Peoples R China

ISBN: (纸本)9789811331466;9789811331459

This paper proposes a knowledge Augmented Inference Network (K- AIN) that can effectively incorporate external knowledge into existing neural network models on Natural Language Inference (NLI) task. Different from previous works that use one-hot representations to describe external knowledge, we employ the TransE model to encode various semantic relations extracted from the external knowledge Base (KB) as distributed relation features. We utilize these distributed relation features to construct knowledge augmented word embeddings and integrate them into the current neural network models. Experimental results show that our model achieves a better performance than the strong baseline on the SNLI dataset and we also surpass the current state-of-the-art models on the SciTail dataset.

关键词： Natural language inference External knowledge knowledge graph embedding

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Multi-task knowledge graph Representations via Residual Functions 1

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26th Pacific-Asia Conference on knowledge Discovery and Data Mining (PAKDD)

作者： Krishnan, Adit Das, Mahashweta Bendre, Mangesh Wang, Fei Yang, Hao Sundaram, Hari Univ Illinois Champaign IL 61820 USA Visa Res Palo Alto CA USA

ISBN: (数字)9783031059339

ISBN: (纸本)9783031059339;9783031059322

In this paper, we propose MuTATE, a Multi-Task Augmented approach to learn Transferable embeddings of knowledge graphs. Previous knowledge graph representation techniques either employ task-agnostic geometric hypotheses to learn informative node embeddings or integrate task-specific learning objectives like attribute prediction. In contrast, our framework unifies multiple co-dependent learning objectives with knowledge graph enrichment. We define co-dependence as multiple tasks that extract covariant distributions of entities and their relationships for prediction or regression objectives. We facilitate knowledge transfer in this setting: tasks -> graph, graph -> tasks, and task-1 -> task-2 via task-specific residual functions to specialize the node embeddings for each task, motivated by domain-shift theory. We show 5% relative gains over state-of-the-art knowledge graph embedding baselines on two public multi-task datasets and show significant potential for cross-task learning.

关键词： knowledge graphs knowledge graph embedding graph neural networks Multi-task learning Residual learning

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Link Prediction Using Multi Part embeddings 16th

Link Prediction Using Multi Part Embeddings

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16th International Extended Semantic Web Conference (ESWC)

作者： Mohamed, Sameh K. Novacek, Vit Data Sci Inst Galway Ireland Insight Ctr Data Analyt Galway Ireland Natl Univ Ireland Galway Galway Ireland

ISBN: (纸本)9783030213480;9783030213473

knowledge graph embeddings models are widely used to provide scalable and efficient link prediction for knowledge graphs. They use different techniques to model embeddings interactions, where their tensor factorisation based versions are known to provide state-of-the-art results. In recent works, developments on factorisation based knowledge graph embedding models were mostly limited to enhancing the ComplEx and the DistMult models, as they can efficiently provide predictions within linear time and space complexity. In this work, we aim to extend the works of the ComplEx and the DistMult models by proposing a new factorisation model, TriModel, which uses three part embeddings to model a combination of symmetric and asymmetric interactions between embeddings. We perform an empirical evaluation for the TriModel model compared to other tensor factorisation models on different training configurations (loss functions and regularisation terms), and we show that the TriModel model provides the state-of-the-art results in all configurations. In our experiments, we use standard benchmarking datasets (WN18, WN18RR, FB15k, FB15k-237, YAGO10) along with a new NELL based benchmarking dataset (NELL239) that we have developed.

关键词： knowledge graph embedding Link prediction

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KIDNet: A knowledge-Aware Neural Network Model for Academic Performance Prediction

KIDNet: A Knowledge-Aware Neural Network Model for Academic ...

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IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Tang, Tao Hou, Jie Guo, Teng Bai, Xiaomei Tian, Xue Hoshyar, Azadeh Noori Federat Univ Australia Sch Engn IT & Phys Sci Ballarat Vic Australia Dalian Univ Technol Sch Software Dalian Liaoning Peoples R China Anshan Normal Univ Comp Ctr Anshan Liaoning Peoples R China Univ Tasmania Sch Arts Law & Educ Launceston Tas Australia Federat Univ Australia Sch Engn IT & Phys Sci Brisbane Qld Australia

ISBN: (纸本)9781450391870

Academic performance prediction and analysis in educational data mining is meaningful for instructors to know the student's ongoing learning status, and also provide appropriate help to students as early as possible if academic difficulties appear. In this paper, we first collect the basic information of students and courses as features. Then, we propose a novel knowledge extraction framework to obtain course knowledge features to reinforce feature groups. The comparative analyses of the knowledge similarity and average grades of the courses in all terms demonstrate a strong correlation between them. Furthermore, we build the knowledge Interaction Discovery Network (KIDNet) model, based on factorization machine (FM) and deep neural network (DNN) algorithms. This model uses FM to model lower-order interactions of sparse features and employs DNN to model higher-order interactions of both dense and sparse features. The effectiveness of KIDNet has been validated by conducting experiments based on a real-world dataset.

关键词： Educational Data Academic Prediction knowledge graph embedding knowledge Interaction

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Entity Alignment Between knowledge graphs Using Entity Type Matching 14th

Entity Alignment Between Knowledge Graphs Using Entity Type ...

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14th International Conference on knowledge Science, Engineering, and Management (KSEM)

作者： Song, Xiuting Zhang, Han Bai, Luyi Northeastern Univ Qinhuangdao Sch Comp & Commun Engn Qinhuangdao 066004 Hebei Peoples R China

ISBN: (纸本)9783030821364;9783030821357

The task of entity alignment between knowledge graphs (KGs) aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based entity alignment methods get extended attention. Most of them firstly embed the entities in low dimensional vectors space via relation structure, and then align entities via these learned embeddings combined with some entity similarity function. Even achieved promising performances, these methods are inadequate in utilizing entity type information. In this paper, we propose a novel entity alignment framework, which integrates entity embeddings and entity type information to achieve entity alignment. This framework uses encoding functions to extract the type features of entities for type matching, and combines the similarity of entity embeddings to improve the accuracy of entity alignment. Our experimental results on several real-world datasets shows that our proposed method achieves improvements on entity alignment compared with most methods, and is close to the state-of-the-art method on several metrics.

关键词： Entity alignment Entity type Iterative alignment knowledge graph embedding knowledge fusion Relation triples

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A Physical embedding Model for knowledge graphs 1

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9th Joint International Semantic Technology Conference (JIST)

作者： Demir, Caglar Ngomo, Axel-Cyrille Ngonga Paderborn Univ DICE Res Grp D-33098 Paderborn Germany

ISBN: (数字)9783030414078

ISBN: (纸本)9783030414078;9783030414061

knowledge graph embedding methods learn continuous vector representations for entities in knowledge graphs and have been used successfully in a large number of applications. We present a novel and scalable paradigm for the computation of knowledge graph embeddings, which we dub PYKE. Our approach combines a physical model based on Hooke's law and its inverse with ideas from simulated annealing to compute embeddings for knowledge graphs efficiently. We prove that PYKE achieves a linear space complexity. While the time complexity for the initialization of our approach is quadratic, the time complexity of each of its iterations is linear in the size of the input knowledge graph. Hence, PYKE'S overall runtime is close to linear. Consequently, our approach easily scales up to knowledge graphs containing millions of triples. We evaluate our approach against six state-of-the-art embedding approaches on the DrugBank and DBpedia datasets in two series of experiments. The first series shows that the cluster purity achieved by PYKE is up to 26% (absolute) better than that of the state of art. In addition, PYKE is more than 22 times faster than existing embedding solutions in the best case. The results of our second series of experiments show that PYKE is up to 23% (absolute) better than the state of art on the task of type prediction while maintaining its superior scalability. Our implementation and results are open-source and are available at http://***/dice-group/PYKE.

关键词： knowledge graph embedding Hooke's law Type prediction

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