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11th IEEE International Conference on knowledge graph (IEEE ICKG)

作者： Hong, Yan Bu, Chenyang Jiang, Tingting Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei 230009 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China Hefei Univ Technol Inst Big Knowledge Sci Hefei 23009 Peoples R China

ISBN: (纸本)9781728181561

knowledge graphs (KGs) have been widely applied in many fields such as recommendation systems and knowledge reasoning. embedding KGs into a continuous vector space has quickly gained significant attention. However, most traditional KG embedding models assume that all the facts in the existing KGs are completely correct, ignoring that KG construction usually involves automatic mechanisms. These automatic construction processes inevitably generate a lot of noises and conflicts, including low-quality errors (e.g., entity type errors). Moreover, these low-quality noises could greatly influence the quality of rule extraction, which may reduce the efficiency of Rule-Guided embedding model (RUGE). To address this problem, an efficient method to eliminate those entity type errors in triples is proposed and applied to RUGE. Experimental results demonstrate that the filtering of low-quality noises can greatly improve the accuracy of knowledge representation learning as well as the quality of rules, further illustrating the effectiveness of our method.

关键词： knowledge graph embedding noise detection entity type

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Rotate3D: Representing Relations as Rotations in Three-Dimensional Space for knowledge graph embedding 20

Rotate3D: Representing Relations as Rotations in Three-Dimen...

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29th ACM International Conference on Information and knowledge Management (CIKM)

作者： Gao, Chang Sun, Chengjie Shan, Lili Lin, Lei Wang, Mingjiang Harbin Inst Technol Harbin Heilongjiang Peoples R China Harbin Inst Technol SZ Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781450368599

knowledge graph embedding, which aims to learn low-dimensional embeddings of entities and relations, plays a vital role in a wide range of applications. It is crucial for knowledge graph embedding models to model and infer various relation patterns, such as symmetry/antisymmetry, inversion, and composition. However, most existing methods fail to model the non-commutative composition pattern, which is essential, especially for multi-hop reasoning. To address this issue, we propose a new model called Rotate3D, which maps entities to the three-dimensional space and defines relations as rotations from head entities to tail entities. By using the non-commutative composition property of rotations in the three-dimensional space, Rotate3D can naturally preserve the order of the composition of relations. Experiments show that Rotate3D outperforms existing state-of-the-art models for link prediction and path query answering. Further case studies demonstrate that Rotate3D can effectively capture various relation patterns with a marked improvement in modeling the composition pattern.

关键词： knowledge graph embedding Link Prediction Path Query Answering Relation Patterns

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Enhancing knowledge graph embedding with Relational Constraints 11

Enhancing Knowledge Graph Embedding with Relational Constrai...

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11th IEEE International Conference on knowledge graph (IEEE ICKG)

作者： Li, Mingda Sun, Zhengya Zhang, Siheng Zhang, Wensheng Chinese Acad Sci Res Ctr Precis Sensing & Control Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728181561

knowledge graph embedding is studied to embed entities and relations of a knowledge graph into continuous vector spaces, which benefits a variety of real-world applications. Among existing solutions, translation-based models, which employ geometric translation to design score function, have drawn much attention. However, these models primarily concentrate on evidence from observing whether the triplets are plausible, and ignore the fact that the relation also implies certain semantic constraints on its subject or object entity. In this paper, we present a general framework for enhancing knowledge graph embedding with relational constraints (KRC). Specifically, we elaborately design the score function by encoding regularities between a relation and its arguments into the translation-based embedding space. Additionally, we propose a soft margin-based ranking loss for effectively training the KRC model, which characterizes different semantic distances between negative and positive triplets. Furthermore, we combine regularities with distributional representations to predict the missing triplets, which possesses certain robust guarantee. We evaluate our method on the task of knowledge graph completion. Extensive experiments show that KRC achieves substantial improvements against baselines.

关键词： knowledge graph embedding relational constraints knowledge graph completion

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Compressing knowledge graph embedding with Relational graph Auto-encoder 10

Compressing Knowledge Graph Embedding with Relational Graph ...

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10th IEEE International Conference on Electronics Information and Emergency Communication (IEEE ICEIEC)

作者： Zhang, Shiyu Zhang, Zhao Zhuang, Fuzhen Shi, Zhiping Han, Xu Capital Normal Univ Coll Informat Engn Beijing 100048 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100049 Peoples R China

ISBN: (纸本)9781728163130;9781728163123

knowledge graphs (KGs) are extremely useful resources for varieties of applications. However, with the large and steadily growing sizes of modern KGs, knowledge graph embeddings (KGE), which represent entities and relations in KGs into 32-bit floating-point vectors, become more and more expensive in terms of memory. To this end, in this paper, we propose a general framework to compress the embeddings from real-valued vectors to binary ones while preserving the inherent information of KGs. Specifically, the proposed framework utilizes relational graph auto-encoders as well as the Gumbel-Softmax trick to obtain the compressed representations. Our framework can be applied to a number of existing KGE models. Particularly, we extend state-of-the-art models TransE, DistMult, and ConvE in this paper. Finally, extensive experiments show that the proposed method successfully reduces the memory size of the embeddings by 92% while only leading to a loss of no more than 5% in the knowledge graph completion task.

关键词： knowledge graph embedding graph autoencoders Compression knowledge graph completion

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Prediction of adverse drug reactions based on knowledge graph embedding

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2021年第1期21卷 38-38页

作者： Zhang, Fei Sun, Bo Diao, Xiaolin Zhao, Wei Shu, Ting Chinese Acad Med Sci & Peking Union Med Coll Natl Ctr Cardiovasc Dis Fuwai Hosp Dept Informat Ctr 167 North Lishi Rd Beijing 100037 Peoples R China Natl Hlth Commiss Natl Inst Hosp Adm Bldg 3Yard 6Shouti South Rd Beijing 100044 Peoples R China

BackgroundAdverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug *** on the Word2Vec model in Nature Language Processing, we propose a new knowledge graph embedding method that embeds drugs and ADRs into their respective vectors and builds a logistic regression classification model to predict whether a given drug will have ***, a new knowledge graph embedding method was proposed, and comparison with similar studies showed that our model not only had high prediction accuracy but also was simpler in model structure. In our experiments, the AUC of the classification model reached a maximum of 0.87, and the mean AUC was *** this paper, we introduce a new method to embed knowledge graph to vectorize drugs and ADRs, then use a logistic regression classification model to predict whether there is a causal relationship between them. The experiment showed that the use of knowledge graph embedding can effectively encode drugs and ADRs. And the proposed ADRs prediction system is also very effective.

关键词： Adverse Drug Reactions knowledge graph embedding Word2Vec DrugBank

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Improving knowledge graph embedding Using Locally and Globally Attentive Relation Paths 42nd

Improving Knowledge Graph Embedding Using Locally and Global...

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42nd European Conference on IR Research

作者： Jia, Ningning Cheng, Xiang Su, Sen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9783030454388;9783030454395

knowledge graphs' incompleteness has motivated many researchers to propose methods to automatically infer missing facts in knowledge graphs. knowledge graph embedding has been an active research area for knowledge graph completion, with great improvement from the early TransE to the current state-of-the-art ConvKB. ConvKB considers a knowledge graph as a set of triples, and employs a convolutional neural network to capture global relationships and transitional characteristics between entities and relations in the knowledge graph. However, it only utilizes the triple information, and ignores the rich information contained in relation paths. In fact, a path of one relation describes the relation from some aspect in a fine-grained way. Therefore, it is beneficial to take relation paths into consideration for knowledge graph embedding. In this paper, we present a novel convolutional neural network-based embedding model PConvKB, which improves knowledge graph embedding by incorporating relation paths locally and globally. Specifically, we introduce attention mechanism to measure the local importance of relation paths. Moreover, we propose a simple yet effective measure DIPF to compute the global importance of relation paths. Experimental results show that our model achieves substantial improvements against state-of-the-art methods.

关键词： knowledge graph embedding Link prediction Triple classification Convolutional neural network Attention mechanism

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A knowledge graph embedding Model Based on Node-Relation Fusion Perception 2020

A Knowledge Graph Embedding Model Based on Node-Relation Fus...

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2020 The 9th International Conference on Networks, Communication and Computing

作者： Zuowei Tang Lixia DU Lanzhou Jiaotong University China School of Electronic & Information Engineering Lanzhou Jiaotong University China

ISBN: (纸本)9781450388566

Although modeling graph structure data with graph convolution network has become a hot research topic in recent years, the research subject in most papers are simple undirected graphs while the multi-relational ones, for example, knowledge graphs, of which each edge has labels and directions associated with entity nodes, are more commonly used and more consistent with the development of the modern Internet. However, the existing methods of dealing with multi-relational graphs all have the problem of over parameterization and are limited to the representation of learning nodes,This leads to unsatisfactory knowledge graph embedding, poor reasoning perception of the model, and inadequate automatic completion of the knowledge *** this paper, a new knowledge graph model for graph convolution network modeling is proposed, in which, a combination operator is used to embed nodes and relationships into the graph, enriching the information of the neighborhood of the central node and enhancing the perceptibility of the knowledge graph embedding model. In the experimental part of the paper, the results of node classification and link prediction tasks both prove the effectiveness of the model.

关键词： node classification graph Convolutional Networks knowledge graph embedding link prediction

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knowledge graph embedding With Interactive Guidance From Entity Descriptions

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IEEE ACCESS 2019年 7卷 156686-156693页

作者： Zhou, Wen'an Wang, Shirui Jiang, Chao Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

knowledge graph (KG) embedding aims to represent both entities and relations into a continuous low-dimensional vector space. Most previous attempts perform the embedding task using only knowledge triples to indicate relations between entities. Entity descriptions, although containing rich background information, have not been well utilized in these methods. In this paper, we propose Entity Descriptions-Guided embedding (EDGE), a novel method for learning the knowledge graph representations with semantic guidance from entity descriptions. EDGE enables an embedding model to learn simultaneously from 1) knowledge triples that have been directly observed in a given KG, and 2) entity descriptions which have rich semantic information about these entities. In the learning process, EDGE encodes the semantics of entity descriptions to enhance the learning of knowledge graph embedding, and integrates such learned KG embedding to constraint their corresponding word embeddings in entity descriptions. Through this interactive procedure, semantics of entity descriptions may be better transferred into the learned KG embedding. We evaluate EDGE in link prediction and entity classification on Freebase and WordNet. Experimental results show that: 1) with entity descriptions injected, EDGE achieves significant and consistent improvements over state-of-the-art baselines;and 2) compared to those one-time injection schemes studied before, the interactive guidance strategy maximizes the utility of entity descriptions for KG embedding, and indeed achieves substantially better performance.

关键词： Semantics Task analysis knowledge acquisition Licenses Convolutional neural networks Sparse matrices knowledge graph embedding entity descriptions interactive guidance

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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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