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检索条件"主题词=Knowledge Graph embedding"

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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...

引用

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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LiterallyWikidata - A Benchmark for knowledge graph Completion Using Literals 20th

LiterallyWikidata - A Benchmark for Knowledge Graph Completi...

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20th International Semantic Web Conference (ISWC)

作者： Gesese, Genet Asefa Alam, Mehwish Sack, Harald FIZ Karlsruhe Leibniz Inst Informat Infrastruct Eggenstein Leopoldshafen Germany Karlsruhe Inst Technol Inst AIFB Karlsruhe Germany

ISBN: (纸本)9783030883614;9783030883607

In order to transform a knowledge graph (KG) into a low dimensional vector space, it is beneficial to preserve as much semantics as possible from the different components of the KG. Hence, some link prediction approaches have been proposed so far which leverage literals in addition to the commonly used links between entities. However, the procedures followed to create the existing datasets do not pay attention to literals. Therefore, this study presents a set of KG completion benchmark datasets extracted from Wikidata and Wikipedia, named LiterallyWikidata. It has been prepared with the main focus on providing benchmark datasets for multimodal KG embedding (KGE) models, specifically for models using numeric and/or text literals. Hence, the benchmark is novel as compared to the existing datasets in terms of properly handling literals for those multimodal KGE models. LiterallyWikidata contains three datasets which vary both in size and structure. Benchmarking experiments on the task of link prediction have been conducted on LiterallyWikidata with extensively tuned unimodal/multimodal KGE models. The datasets are available at https://***/10.5281/zenodo.4701190.

关键词： knowledge graph completion knowledge graph embedding Link prediction Literals Benchmark dataset

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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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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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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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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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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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Efficient Parallel Translating embedding For knowledge graphs 17

Efficient Parallel Translating Embedding For Knowledge Graph...

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IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Zhang, Denghui Li, Manling Jia, Yantao Wang, Yuanzhuo Cheng, Xueqi Chinese Acad Sci Inst Comp Technol 6 Kexueyuan South Rd Beijing 100190 Peoples R China

ISBN: (纸本)9781450349512

knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great difficulty in practical applications. In this paper, we propose an efficient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [19], and a more efficient variant TransE- AdaGrad [11] validate that ParTrans-X can speed up the training process by more than an order of magnitude.

关键词： knowledge graph embedding Translation-based Parallel

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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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Relation embedding for Personalised Translation-Based POI Recommendation 24th

Relation Embedding for Personalised Translation-Based POI Re...

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24th Pacific-Asia Conference on knowledge Discovery and Data Mining (PAKDD)

作者： Wang, Xianjing Salim, Flora D. Ren, Yongli Koniusz, Piotr RMIT Univ Melbourne Vic Australia CSIRO Data61 Canberra ACT Australia Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9783030474263;9783030474256

Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI systems, which affects the quality of POI recommendations. To this end, we propose a translation-based relation embedding for POI recommendation. Our approach encodes the temporal and geographic information, as well as semantic contents effectively in a low-dimensional relation space by using knowledge graph embedding techniques. To further alleviate the issue of user-POI matrix sparsity, a combined matrix factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests by exploiting the side-information. Experiments on two real-world datasets demonstrate the effectiveness of our proposed model.

关键词： knowledge graph embedding Collaborative filtering Matrix factorization Recommender System POI recommendation

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