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检索条件"主题词=Knowledge Graph embedding"

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Exploring Scholarly Data by Semantic Query on knowledge graph embedding Space 23rd

Exploring Scholarly Data by Semantic Query on Knowledge Grap...

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23rd International Conference on Theory and Practice of Digital Libraries (TPDL)

作者： Hung Nghiep Tran Takasu, Atsuhiro SOKENDAI Grad Univ Adv Studies Tokyo Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9783030307608;9783030307592

The trends of open science have enabled several open scholarly datasets which include millions of papers and authors. Managing, exploring, and utilizing such large and complicated datasets effectively are challenging. In recent years, the knowledge graph has emerged as a universal data format for representing knowledge about heterogeneous entities and their relationships. The knowledge graph can be modeled by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between these embedding vectors. However, the semantic structures in the knowledge graph embedding space are not well-studied, thus knowledge graph embedding methods are usually only used for knowledge graph completion but not data representation and analysis. In this paper, we propose to analyze these semantic structures based on the well-studied word embedding space and use them to support data exploration. We also define the semantic queries, which are algebraic operations between the embedding vectors in the knowledge graph embedding space, to solve queries such as similarity and analogy between the entities on the original datasets. We then design a general framework for data exploration by semantic queries and discuss the solution to some traditional scholarly data exploration tasks. We also propose some new interesting tasks that can be solved based on the uncanny semantic structures of the embedding space.

关键词： Scholarly data Data exploration Semantic query knowledge graph knowledge graph embedding embedding space

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Top-N Collaborative Filtering Recommendation Algorithm Based on knowledge graph embedding 14th

Top-N Collaborative Filtering Recommendation Algorithm Based...

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14th International Conference on knowledge Management in Organizations (KMO) - Synergistic Role of knowledge Management in Organization

作者： Zhu, Ming Zhen, De-sheng Tao, Ran Shi, You-qun Feng, Xiang-yang Wang, Qian Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9783030214517;9783030214500

The traditional collaborative filtering recommendation algorithm only uses the item-user rating matrix without considering the semantic information of the item itself, resulting in a problem that the recommendation accuracy is not high. This paper proposes a Top-N collaborative filtering recommendation algorithm based on knowledge graph embedding. The knowledge graph embedding is used to learn a low-dimensional vector for each entity and relationship in the knowledge graph, while maintaining the structure and semantic information of the original graph in the vector. By calculating the semantic similarity between items, the semantic information of the item itself is incorporated into the collaborative filtering recommendation. The algorithm makes up for the defect that the collaborative filtering recommendation algorithm does not consider the knowledge information of the item itself, and enhances the effect of collaborative filtering recommendation on the semantic level. The experimental results on the MovieLens dataset show that the algorithm can get higher values on precision, recall and F1 measure.

关键词： Collaborative filtering knowledge graph embedding Recommendation algorithm Semantic similarity

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Enhance knowledge graph embedding via fake triples

Enhance knowledge graph embedding via fake triples

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International Joint Conference on Neural Networks (IJCNN)

作者： Yan, Zhihuan Peng, Rong Wang, Yaqian Li, Weidong Wuhan Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781728119854

embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Although previous works achieve great success, most of them are based on the closed-world assumption, where they only utilize knowledge explicitly exist in KGs but ignore implicit knowledge. This paper tries to improve the performance of KG embedding models by adding implicit knowledge to it. We consider that when an entity occurs only in head or tail of all triples in one KG, its connectivity with other entities is single and the inverse triples of it can provide as implicit knowledge to enrich its connectivity, and with this implicit knowledge KG embedding models can learn more accurate semantic of entities and relations in the KG. For this purpose, we introduce 'fake triples' (triples that theoretically should exist but not appear in knowledge graph explicitly) via dummy relations for zero in-degree and zero out-degree entities to enrich their connectivity and further improve the embedding models' performance. Extensive experiments on entity alignment task and linking predication task show that our approach achieves good results. On entity alignment task, the entity alignment model with fake triples (EA+F) obtains better results than a number of state-of-the-art entity alignment models. On linking predication task, our method achieves better Mean Rank on FB15K. Our work can prove that the performance of knowledge graph embedding may be promoted by elaborate analysis on dataset rather than designing complex models.

关键词： knowledge graph embedding fake triple dummy relation entity alignment linking predication open world assumption

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Spectrum Quality Cognition Driven by GCN-Based with knowledge graph embedding and knowledge Transfer

Spectrum Quality Cognition Driven by GCN-Based with Knowledg...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Guo, Yibo Huang, Yang Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing Peoples R China

ISBN: (纸本)9798350378412

Spectrum is an indispensable scarce resource for 5G and beyond mobile networks. Aiming at improving the spectrum utilization, spectrum quality cognition is expected to be a key enabler, which features predicting the transmission performance values and recommending frequency-domain channel for utilization. Nevertheless, the learning of feature representation for entities and relations in the knowledge graph (KG) for the electromagnetic spectrum remains largely unexplored. In order to capture the feature of spectrum quality, we construct a KG with transmission performance as node labels in graph model for the communication network and develop a knowledge graph embedding (KGE) model based on convolutional neural network (CNN). In order to address the problem of predicting transmission performance values in the presence of scarce spectrum data, we convert this issue into a node regression problem using a GCN-based graph model. In order to address the problem of sparse feature vector with respect to entities and relationships within the KG, we exploit KGE vectors generated by a CNN-based KGE model to replace the one-hot encoding method in graph model. Moreover, knowledge transfer (KT) can be exploited to extract knowledge from the historical complete transmission performance data of the source domain to the incomplete data of the target domain, so as to make up scarce spectrum data. Simulation results reveal that the algorithm based on GCN-KGE-KT significantly improves the accuracy of predicting transmission performance values compared to the algorithms without KGE.

关键词： Spectrum quality cognition knowledge graph knowledge graph embedding graph convolutional network knowledge transfer

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TRHyTE: Temporal knowledge graph embedding Based on Temporal-Relational Hyperplanes 27th

TRHyTE: Temporal Knowledge Graph Embedding Based on Temporal...

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27th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Yuan, Lin Li, Zhixu Qu, Jianfeng Zhang, Tingyi Liu, An Zhao, Lei Chen, Zhigang Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Shanghai Peoples R China iFLYTEK State Key Lab Cognit Intelligence Hefei Peoples R China iFLYTEK Res Suzhou Peoples R China

ISBN: (纸本)9783031001239;9783031001222

Temporal knowledge graph embedding (TKGE) aims at encoding evolving facts with high-dimensional vectorial representations. Although a representative hyperplane-based TKGE approach, namely HyTE, has achieved remarkable performance, it still suffers from several problems including (i) ignorance of latent temporal properties and diversity of relations;(ii) neglect of temporal dependency between adjacent hyperplanes;(iii) inefficient static random negative sampling method;(iv) incomplete testing on partial time information. To address these issues, we propose TRHyTE, a novel Temporal-Relational Hyperplane based TKGE model, which defines three typical properties, including interval, open-interval, and instantaneousness temporal, for relations and correspondingly constructs three relational sub-KGs, supporting distinguishing learning for facts. Within each sub-KG, TRHyTE transforms entities into relation space first, and then explicitly projects transformed entities and relations into temporal-relational hyperplanes to learn timerelation-aware embeddings. Moreover, Gate Recurrent Unit is leveraged to simulate TKG evolution so as to capture temporal dependency between adjacent hyperplanes. Additionally, we develop a dynamic negative samples mechanism for robust training In testing phase, an expandand-best-merge strategy is crafted to realize a complete testing on all valid time intervals. Extensive experiments on two well-known benchmarks verify the effectiveness of our proposals.

关键词： Temporal knowledge graph knowledge graph embedding Temporal property Hyperplane projection

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knowledge graph question answering based on TE-BiLTM and knowledge graph embedding 2021

Knowledge graph question answering based on TE-BiLTM and kno...

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5th International Conference on Innovation in Artificial Intelligence (ICIAI)

作者： Li, Jianbin Qu, Ketong Li, Kunchang Chen, Zhiqiang Fang, Suwan Yan, Jingchen North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China

ISBN: (纸本)9781450388634

A Abstract: knowledge graph question answering (KGQA) aims to use facts in the knowledge graph to answer natural language questions. Relation extraction, as one of the sub-tasks of the KGQA, is an important and difficult problem in the KGQA. To improve the accuracy of relation extraction in KGQA, in this paper, we propose a new deep neural network model called Transformer Encoder-BiLSTM (TE-BiLSTM). We give the detailed design of our method and our experimental results demonstrate that our approach can not only achieve better results in relation extraction, but can also outperform the state-of-the-art approaches in the KGQA.

关键词： knowledge graph question answering knowledge graph embedding Deep learning Self-attention mechanism TE-BiLSTMJianbin Li Ketong Qu KunChang Li ZhiQiang Chen SuWan Fang Jingchen Yan. 2021

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A knowledge graph embedding Based Approach for Learning Path Recommendation for Career Goals 13th

A Knowledge Graph Embedding Based Approach for Learning Path...

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13th International Conference on Computational Collective Intelligence (ICCCI)

作者： Nguyen, Thu Tran Minh Tran, Thinh Pham Quoc Univ Sci Fac Informat Technol Ho Chi Minh City Vietnam Viet Nam Natl Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9783030880811;9783030880804

Nowadays, many online courses in the Information Technology (IT) field provided by different organizations make it difficult for learners to screen the courses that are best suitable for their career development. Many approaches have been raised to suggest a personalized learning path based on the learner's career goals;however, most of them use traditional techniques and face the problems of sparse data, cold start, and lack of advisory results' interpretation. In this paper, we address these problems by using knowledge graph embedding (KGE) which is known as one of approaches of graph-based models. This approach has emerged as a phenomenon and has not been widely applied in the field of learning path recommendation. We propose a new knowledge graph (KG) architecture for representing entities and their semantic relationships. The main entities identified in the KG are courses, occupations, the units of knowledge, and the relationships are the semantic connections between these entities. In existing KG architectures only exploit the hierarchy of intermediate knowledge units. Whereas, our KG architecture provides a specific classification of these learning objects, and this helps the semantic relationship of the subjects become more unambiguous and connected. Then, we explore and build experiments for our proposed KG architecture by using KGE techniques to prove the effectiveness. The experimental results show that our solution is worth considering and promises to bring a high degree of efficiency in the learning path recommendation system.

关键词： knowledge graph embedding knowledge graph structure Learning path recommendation for career goals graph database

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Enhancing knowledge graph embedding with Probabilistic Negative Sampling 26

Enhancing Knowledge Graph Embedding with Probabilistic Negat...

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26th International Conference on World Wide Web (WWW)

作者： Kanojia, Vibhor Maeda, Hideyuki Togashi, Riku Fujita, Sumio Yahoo Japan Corp Tokyo Japan

ISBN: (纸本)9781450349147

Link Prediction using knowledge graph embedding projects symbolic entities and relations into low dimensional vector space, thereby learning the semantic relations between entities. Among various embedding models, there is a series of translation-based models such as TransE[1], TransH[2], and TransR[3]. This paper proposes modifications in the TransR model to address the issue of skewed data which is common in real-world knowledge graphs. The enhancements enable the model to smartly generate corrupted triplets during negative sampling, which significantly improves the training time and performance of TransR. The proposed approach can be applied to other translation-based models.

关键词： knowledge graph embedding TransR Probabilistic Negative Sampling

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What Affects the Performance of Models? Sensitivity Analysis of knowledge graph embedding 27th

What Affects the Performance of Models? Sensitivity Analysis...

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27th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Yang, Han Zhang, Leilei Su, Fenglong Pang, Jinhui Peking Univ Beijing Peoples R China Natl Univ Def Technol Changsha Hunan Peoples R China Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9783031001239;9783031001222

knowledge graph (KG) embedding aims to embed entities and relations into a low-dimensional vector space, which has been an active research topic for knowledge base completion (KGC). Recent researchers improve existing models in terms of knowledge representation space, scoring function, encoding method, etc., have achieved progressive improvements. However, the theoretical mechanism behind them has always been ignored. There are few works on sensitivity analysis of embedded models, which is extremely challenging. The diversity of KGE models makes it difficult to consider them uniformly and compare them fairly. In this paper, we first study the internal connections and mutual transformation methods of different KGE models from the generic group perspective, and further propose a unified KGE learning framework. Then, we conduct an in-depth sensitivity analysis on the factors that affect the objective of embedding learning. Specifically, in addition to the impact of the embedding algorithm itself, this article also considers the structural features of the dataset and the strategies of the training method. After a comprehensive experiment and analysis, we can conclude that the Head-to-Tail rate of datasets, the definition of model metric function, the number of negative samples and the selection of regularization methods have a greater impact on the final performance.

关键词： knowledge graph embedding Group theory Sensitivity analysis

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Stock Price Movement Prediction from Financial News with Deep Learning and knowledge graph embedding 1

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15th Pacific Rim International Conference on Artificial Intelligence (PRICAI) / 15th Pacific Rim knowledge Acquisition Workshop (PKAW)

作者： Liu, Yang Zeng, Qingguo Yang, Huanrui Carrio, Adrian Univ Politecn Madrid Dept Ind Engn Business Adm & Stat E-28006 Madrid Spain South China Normal Univ Guangzhou Guangdong Peoples R China Duke Univ Elect & Comp Engn Dept Durham NC 27708 USA Univ Politecn Madrid Ctr Automat & Robot E-28006 Madrid Spain

ISBN: (数字)9783319972893

ISBN: (纸本)9783319972886;9783319972893

As the technology applied to economy develops, more and more investors are paying attention to stock prediction. Therefore, research on stock prediction is becoming a hot area. In this paper, we propose to incorporate a joint model using the TransE model for representation learning and a Convolutional Neural Network (CNN), which extracts features from financial news articles. This joint learning can improve the accuracy of text feature extraction while reducing the sparseness of news headlines. On the other hand, we present a joint feature extraction method which extracts feature vectors from both daily trading data and technical indicators. The approach is evaluated using Support Vector Machines (SVM) as a traditional machine learning method and Long Short-term Memory (LSTM) model as a deep learning method. The proposed model is used to predict Apple's stock price movement using the Standard & Poor's 500 index (S&P 500). The experiments show that the accuracy of news sentiment classification for feature selection achieved 97.66% by model of joint learning, the performance of joint learning is better than feature extraction by CNN, the accuracy of stock price movement prediction through deep learning achieved 55.44%, this result is higher than traditional machine learning. This model can give the investors greater decision support.

关键词： Stock market Deep learning Event tuple Financial news knowledge graph embedding

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