检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

304 篇 期刊文献
261 篇 会议
1 篇 学位论文

馆藏范围

566 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

531 篇 工学
- 510 篇 计算机科学与技术...
- 103 篇 电气工程
- 66 篇 软件工程
- 39 篇 信息与通信工程
- 27 篇 控制科学与工程
- 4 篇 机械工程
- 4 篇 测绘科学与技术
- 3 篇 电子科学与技术（可...
- 3 篇 生物医学工程（可授...
- 3 篇 网络空间安全
- 2 篇 仪器科学与技术
- 2 篇 交通运输工程
51 篇 管理学
- 38 篇 管理科学与工程(可...
- 11 篇 图书情报与档案管...
- 3 篇 公共管理
45 篇 理学
- 18 篇 数学
- 13 篇 生物学
- 8 篇 物理学
- 6 篇 系统科学
- 4 篇 地理学
- 3 篇 化学
- 2 篇 地球物理学
24 篇 医学
- 14 篇 基础医学(可授医学...
- 4 篇 临床医学
- 4 篇 药学(可授医学、理...
- 2 篇 公共卫生与预防医...
6 篇 文学
- 5 篇 外国语言文学
3 篇 法学
- 2 篇 社会学
- 1 篇 法学
3 篇 农学
2 篇 教育学
- 2 篇 教育学

主题

566 篇 knowledge graph ...
142 篇 link prediction
90 篇 knowledge graph
63 篇 knowledge graph ...
34 篇 representation l...
29 篇 knowledge graphs
23 篇 semantics
20 篇 entity alignment
17 篇 task analysis
17 篇 attention mechan...
15 篇 graph neural net...
14 篇 deep learning
14 篇 predictive model...
12 篇 negative samplin...
12 篇 graph neural net...
12 篇 machine learning
12 篇 convolutional ne...
11 篇 computational mo...
10 篇 knowledge engine...
10 篇 recommender syst...

机构

17 篇 chinese acad sci...
12 篇 univ chinese aca...
12 篇 univ chinese aca...
10 篇 univ sci fac inf...
9 篇 wuhan univ sch c...
9 篇 vietnam natl uni...
7 篇 hefei univ techn...
7 篇 southeast univ s...
7 篇 univ mannheim da...
6 篇 zhejiang univ co...
5 篇 zhejiang univ pe...
5 篇 alibaba grp peop...
5 篇 peng cheng lab p...
5 篇 shanghai univ sc...
4 篇 univ queensland ...
4 篇 soochow univ sch...
4 篇 bosch ctr ai ren...
4 篇 beihang univ sch...
4 篇 harbin inst tech...
4 篇 huawei technol c...

作者

11 篇 zhang wen
9 篇 chen huajun
9 篇 jia yantao
8 篇 paulheim heiko
7 篇 cheng xueqi
7 篇 thanh le
7 篇 bac le
7 篇 wang yuanzhuo
7 篇 wang meng
6 篇 li zhifei
6 篇 wang ronggui
6 篇 li guanyu
6 篇 yang juan
6 篇 xue lixia
5 篇 liu jun
5 篇 wang tao
5 篇 zhang wensheng
5 篇 guo xiaobo
5 篇 sun zhengya
5 篇 wang bin

语言

559 篇 英文
7 篇 其他

检索条件"主题词=Knowledge Graph embedding"

共 566 条记录，以下是291-300 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

GEval: A Modular and Extensible Evaluation Framework for graph embedding Techniques 1

引用

17th Extended Semantic Web Conference (ESWC)

作者： Pellegrino, Maria Angela Altabba, Abdulrahman Garofalo, Martina Ristoski, Petar Cochez, Michael Univ Salerno Dept Comp Sci Fisciano Italy Rhein Westfal TH Aachen Informat Syst & Databases Aachen Germany IBM Res Almaden San Jose CA USA Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (数字)9783030494612

ISBN: (纸本)9783030494612;9783030494605

While RDF data are graph shaped by nature, most traditional Machine Learning (ML) algorithms expect data in a vector form. To transform graph elements to vectors, several graph embedding approaches have been proposed. Comparing these approaches is interesting for 1) developers of new embedding techniques to verify in which cases their proposal outperforms the state-of-art and 2) consumers of these techniques in choosing the best approach according to the task(s) the vectors will be used for. The comparison could be delayed (and made difficult) by the choice of tasks, the design of the evaluation, the selection of models, parameters, and needed datasets. We propose GEval, an evaluation framework to simplify the evaluation and the comparison of graph embedding techniques. The covered tasks range from ML tasks (Classification, Regression, Clustering), semantic tasks (entity relatedness, document similarity) to semantic analogies. However, GEval is designed to be (easily) extensible. In this article, we will describe the design and development of the proposed framework by detailing its overall structure, the already implemented tasks, and how to extend it. In conclusion, to demonstrate its operating approach, we consider the parameter tuning of the KGloVe algorithm as a use case.

关键词： Evaluation framework knowledge graph embedding Machine Learning Semantic tasks

来源：评论

学校读者我要写书评

暂无评论

A Re-ranking Model for Dependency Parsing with knowledge graph embeddings

A Re-ranking Model for Dependency Parsing with Knowledge Gra...

引用

Proceedings of International Conference on Asian Language Processing

作者： Kim, A-Yeong Song, Hyun-Je Park, Seong-Bae Lee, Sang-Jo Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea

ISBN: (纸本)9781467395960

Re-ranking models of parse trees have been focused on re-ordering parse trees with a syntactic view. However, also a semantic view should be considered in re-ranking parse trees, because the fact that a word pair has a dependency implies that the pair has both syntactic and semantic relations. This paper proposes a re-ranking model for dependency parsing based on a combination of syntactic and semantic plausibilities of dependencies. The syntactic probability is used as a syntactic plausibility of a parse tree, and a knowledge graph embedding is adopted to represent its semantic plausibility. The knowledge graph embedding allows the semantic plausibility of parse trees to be expressed effectively with ease. The experiments on the standard Penn Treebank corpus prove that the proposed model improves the base parser regardless of the number of candidate parse trees.

关键词： dependency parsing re-ranking knowledge graph embedding

来源：评论

学校读者我要写书评

暂无评论

Untargeted Adversarial Attack on knowledge graph embeddings 47

Untargeted Adversarial Attack on Knowledge Graph Embeddings

引用

47th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Zhao, Tianzhe Chen, Jiaoyan Ru, Yanchi Lin, Qika Geng, Yuxia Liu, Jun Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Univ Manchester Dept Comp Sci Manchester Lancs England Natl Univ Singapore Singapore Singapore Hangzhou Dianzi Univ Sch Comp Sci Hangzhou Peoples R China Xi An Jiao Tong Univ Natl Engn Lab Big Data Analyt Xian Peoples R China

ISBN: (纸本)9798400704314

knowledge graph embedding (KGE) methods have achieved great success in handling various knowledge graph (KG) downstream tasks. However, KGE methods may learn biased representations on low-quality KGs that are prevalent in the real world. Some recent studies propose adversarial attacks to investigate the vulnerabilities of KGE methods, but their attackers are target-oriented with the KGE method and the target triples to predict are given in advance, which lacks practicability. In this work, we explore untargeted attacks with the aim of reducing the global performances of KGE methods over a set of unknown test triples and conducting systematic analyses on KGE robustness. Considering logic rules can effectively summarize the global structure of a KG, we develop rulebased attack strategies to enhance the attack efficiency. In particular, we consider adversarial deletion which learns rules, applying the rules to score triple importance and delete important triples, and adversarial addition which corrupts the learned rules and applies them for negative triples as perturbations. Extensive experiments on two datasets over three representative classes of KGE methods demonstrate the effectiveness of our proposed untargeted attacks in diminishing the link prediction results. And we also find that different KGE methods exhibit different robustness to untargeted attacks. For example, the robustness of methods engaged with graph neural networks and logic rules depends on the density of the graph. But rule-based methods like NCRL are easily affected by adversarial addition attacks to capture negative rules.

关键词： knowledge graph embedding knowledge graph Completion Adversarial Attack Robustness Rule Learning

来源：评论

学校读者我要写书评

暂无评论

In Silico Drug Repurposing using knowledge graph embeddings for Alzheimer's Disease 9

In Silico Drug Repurposing using Knowledge Graph Embeddings ...

引用

9th International Conference on Bioinformatics Research and Applications (ICBRA)

作者： Daluwatumulle, Geesa Wijesinghe, Rupika Weerasinghe, Ruvan Univ Colombo Sch Comp Colombo Sri Lanka

ISBN: (纸本)9781450396868

Drug repurposing (DR), also known as drug repositioning, is a method that identifies novel therapeutic uses from existing drugs. This strategy is highly effective, saves time, cost, and has a minimum risk factor when compared with the traditional in vitro and in vivo drug development methodologies. DR is used for difficult to treat, neglected, or incredibly rare diseases. Alzheimer's disease (AD) is categorized as a difficult to treat disease since, no medication is available that can slow down the disease progression without causing severe risk factors. In this context, novel methodologies are vital to find DR candidates for AD. Natural Language Processing (NLP) is garnering popularity due its ability to discover unseen knowledge and complex associations and this study too utilized a NLP approach. The proposed methodology included four main steps. First, the text data that is needed for the study were retrieved, then the information within the unstructured text data were extracted, next, the DR candidates were predicted using a novel hybrid method which included cosine similarity (CS) and link prediction (LP). Finally, the predicted drug candidates were fed into a trained machine learning (ML) model with a specificity of 0.894% and a f1 score of 0.819% and further validated. The analysis showed that some of the candidates which were not approved nor had ongoing clinical trials have enormous potential in treating AD.

关键词： drug repurposing alzheimer's disease knowledge graph knowledge graph embedding machine learning

来源：评论

学校读者我要写书评

暂无评论

New Strategies for Learning knowledge graph embeddings: The Recommendation Case 23rd

New Strategies for Learning Knowledge Graph Embeddings: The ...

引用

23rd International Conference on knowledge Engineering and knowledge Management (EKAW)

作者： Hubert, Nicolas Monnin, Pierre Brun, Armelle Monticolo, Davy Univ Lorraine LORIA CNRS Nancy France Univ Lorraine ERPI Nancy France Orange Belfort France

ISBN: (纸本)9783031171055;9783031171048

knowledge graph embedding models encode elements of a graph into a low-dimensional space that supports several downstream tasks. This work is concerned with the recommendation task, which we approach as a link prediction task on a single target relation performed in the embedding space. Training an embedding model requires negative sampling, which consists in corrupting the head or the tail of positive triples to generate negative ones. Although knowledge graph embedding models and negative sampling have extensively been investigated for link prediction, their combined use for performing recommendations over knowledge graphs remains largely unexplored in the literature. In this work, we propose two specialization strategies for training embedding models and performing knowledge graph-based recommendations. Both strategies first train an embedding model on the whole knowledge graph. Then, during a specialization phase, a dedicated negative sampling scheme is applied to refine the pre-trained model. Experimental results on two public datasets demonstrate that a simple strategy which refines a pre-trained model by sampling random negative tails for the target relation proves to be very effective. This strategy significantly improves performance with respect to traditional rank-based evaluation metrics as well as a newly introduced metric that reflects the semantic validity of the top-ranked candidate entities.

关键词： Negative Sampling knowledge graph embedding Recommendation Link Prediction Ontology

来源：评论

学校读者我要写书评

暂无评论

ResConvE: Deeper Convolution-Based knowledge graph embeddings 16th

ResConvE: Deeper Convolution-Based Knowledge Graph Embedding...

引用

16th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW)

作者： Long, Yongxu Qiu, Zihan Zheng, Dongyang Wu, Zhengyang Li, Jianguo Tang, Yong South China Normal Univ Sch Comp Sci Guangzhou 510631 Guangdong Peoples R China

ISBN: (纸本)9789811945496;9789811945489

Link prediction on knowledge graphs (KGs) is an effective way to address their incompleteness. ConvE and InteractE have introduced CNN to this task and achieved excellent performance, but their model uses only a single 2D convolutional layer. Instead, we think that the network should go deeper. In this case, we propose the ResConvE model, which takes reference from the application of residual networks in computer vision, and deepens the neural network, and applies a skip connection to alleviate the gradient explosion and gradient disappearance caused by the deepening of the network layers. We also introduce the SKG-course dataset from Scholat for experiments. Through extensive experiments, we find that ResConvE performs well on some datasets, which proves that the idea of this method has better performance than baselines. Moreover, we also design controlled experiments setting different depths of ResConvE on FB15k and SKG-course to demonstrate that deepening the number of network layers within a certain range does help in performance improvement on different datasets.

关键词： knowledge graph embedding Residual network knowledge graph SCHOLAT Link prediction

来源：评论

学校读者我要写书评

暂无评论

STDE: A Single-Senior-Teacher knowledge Distillation Model for High-Dimensional knowledge graph embeddings 2

STDE: A Single-Senior-Teacher Knowledge Distillation Model f...

引用

2nd IEEE International Conference on Information Communication and Software Engineering (ICICSE) / 7th International Conference on Mathematics and Artificial Intelligence (ICMAI)

作者： Guo, Xiaobo Wang, Peipei Gao, Neng Wang, Xin Feng, Wenying Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China

ISBN: (纸本)9781665482202

An important role of knowledge graph embedding (KGE) is to automatically complete the missing fact in a knowledge base. It is well-known that human society is constantly developing and the knowledge generated by human society will always being increasing. The increasing scale of the knowledge base is a great challenge to the storage and computing resources of downstream applications. At present, the dimensions of most mainstream knowledge graph embedding models are between 200-1000. For a large-scale knowledge base with millions of entities, these embeddings with hundreds of dimensional values are not conducive to rapid and frequent deployment on many kinds of artificial intelligent applications with limited storage and computing resources. To solve this problem, we propose a single-senior-teacher knowledge distillation model for high-dimensional knowledge graph embeddings named STDE, which constructs a low-dimensional student from a trained high-dimensional teacher. In STDE, the senior teacher can help the student learn key knowledge from correct knowledge and indistinguishable wrong knowledge with use of high-quality negative samples of triplets. We apply STDE to four typical KGE models on two famous data sets. Experimental results show that STDE can compress the embedding parameters of high-dimensional KGE models to 1/8 or 1/16 of their original scales. We further verify the effectiveness of "senior teacher" through ablation experiments.

关键词： knowledge graph embedding knowledge distillation link prediction

来源：评论

学校读者我要写书评

暂无评论

RETRA: Recurrent Transformers for Learning Temporally Contextualized knowledge graph embeddings 18th

RETRA: Recurrent Transformers for Learning Temporally Contex...

引用

18th Extended Semantic Web Conference (ESWC)

作者： Werner, Simon Rettinger, Achim Halilaj, Lavdim Luttin, Jurgen Trier Univ Trier Germany Bosch Res Renningen Germany

ISBN: (纸本)9783030773854;9783030773847

knowledge graph embeddings (KGE) are vector representations that capture the global distributional semantics of each entity instance and relation type in a static knowledge graph (KG). While KGEs have the capability to embed information related to an entity into a single representation, they are not customizable to a specific context. This is fundamentally limiting for many applications, since the latent state of an entity can change depending on the current situation and the entity's history of related observations. Such context-specific roles an entity might play cannot be captured in global KGEs, since it requires to generate an embedding unique for each situation. This paper proposes a KG modeling template for temporally contextualized observations and introduces the Recurrent Transformer (RETRA), a neural encoder stack with a feedback loop and constrained multi-headed self-attention layers. RETRA enables to transform global KGEs into custom embeddings, given the situation-specific factors of the relation and the subjective history of the entity. This way, entity embeddings for down-stream knowledge graph Tasks (KGT) can be contextualized, like link prediction for location recommendation, event prediction, or driving-scene classification. Our experimental results demonstrate the performance gains standard KGEs can obtain, if they are customized according to the situational context.

关键词： knowledge graph embedding Contextualized embeddings Modeling temporal context

来源：评论

学校读者我要写书评

暂无评论

Integrating knowledge graph embeddings and Pre-trained Language Models in Hypercomplex Spaces 22nd

Integrating Knowledge Graph Embeddings and Pre-trained Langu...

引用

22nd International Semantic Web Conference (ISWC)

作者： Nayyeri, Mojtaba Wang, Zihao Akter, Mst. Mahfuja Alam, Mirza Mohtashim Rony, Md Rashad Al Hasan Lehmann, Jens Staab, Steffen Univ Stuttgart Stuttgart Germany Univ Bonn Bonn Germany Karlsruhe Inst Technol Karlsruhe Germany Tech Univ Dresden Amazon Dresden Germany Univ Southampton Southampton England

ISBN: (纸本)9783031472398;9783031472404

knowledge graphs comprise structural and textual information to represent knowledge. To predict new structural knowledge, current approaches learn representations using both types of information through knowledge graph embeddings and language models. These approaches commit to a single pre-trained language model. We hypothesize that heterogeneous language models may provide complementary information not exploited by current approaches. To investigate this hypothesis, we propose a unified framework that integrates multiple representations of structural knowledge and textual information. Our approach leverages hypercomplex algebra to model the interactions between (i) graph structural information and (ii) multiple text representations. Specifically, we utilize Dihedron models with 4*D dimensional hypercomplex numbers to integrate four different representations: structural knowledge graph embeddings, word-level representations (e.g., Word2vec and FastText), sentence-level representations (using a sentence transformer), and document-level representations (using FastText or Doc2vec). Our unified framework score the plausibility of labeled edges via Dihedron products, thus modeling pairwise interactions between the four representations. Extensive experimental evaluations on standard benchmark datasets confirm our hypothesis showing the superiority of our two new frameworks for link prediction tasks.

关键词： knowledge graph embedding Pre-trained Language Model Textual Information

来源：评论

学校读者我要写书评

暂无评论

Fantastic knowledge graph embeddings and How to Find the Right Space for Them 19th

Fantastic Knowledge Graph Embeddings and How to Find the Rig...

引用

19th International Semantic Web Conference (ISWC)

作者： Nayyeri, Mojtaba Xu, Chengjin Vahdati, Sahar Vassilyeva, Nadezhda Sallinger, Emanuel Yazdi, Hamed Shariat Lehmann, Jens Univ Bonn Smart Data Analyt Grp SDA Bonn Germany InfAI Dresden Lab Dresden Germany Univ Oxford Oxford England TU Wien Vienna Austria Fraunhofer IAIS Dresden Lab Dresden Germany

ISBN: (纸本)9783030624194;9783030624187

During the last few years, several knowledge graph embedding models have been devised in order to handle machine learning problems for knowledge graphs. Some of the models which were proven to be capable of inferring relational patterns, such as symmetry or transitivity, show lower performance in practice than those not allowing to infer those patterns. It is often unknown what factors contribute to such performance differences among KGE models in the inference of particular patterns. We develop the concept of a solution space as a factor that has a direct influence on the practical performance of knowledge graph embedding models as well as their capability to infer relational patterns. We showcase the effect of solution space on a newly proposed model dubbed SpacEss. We describe the theoretical considerations behind the solution space and evaluate our model against state-of-the-art models on a set of standard benchmarks namely WordNet and FreeBase.

关键词： Link prediction knowledge graph embedding Relation pattern Solution space knowledge completion

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共57页 << < 26 27 28 29 30 31 32 33 34 35 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：