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Triple Context-Based knowledge graph embedding

IEEE ACCESS 2018年 6卷 58978-58989页

作者： Gao, Huan Shi, Jun Qi, Guilin Wang, Meng Southeast Univ Comp Sci & Engn Inst Nanjing 211100 Jiangsu Peoples R China

knowledge graph embedding aims to represent entities and relations of a knowledge graph in continuous vector spaces. It has increasingly drawn attention for its ability to encode semantics in low dimensional vectors as well as its outstanding performance on many applications, such as question answering systems and information retrieval tasks. Existing methods often handle each triple independently, without considering context information of a triple in the knowledge graph, such an information can be useful for inference of new knowledge. Moreover, the relations and paths between an entity pair also provide information for inference. In this paper, we define a novel context-dependent knowledge graph representation model named triple-context-based knowledge embedding, which is based on the notion of triple context used for embedding entities and relations. For each triple, the triple context is composed of two kinds of graph structured information: one is a set of neighboring entities along with their outgoing relations, the other is a set of relation paths which contain a pair of target entities. Our embedding method is designed to utilize the triple context of each triple while learning embeddings of entities and relations. The method is evaluated on multiple tasks in the paper. Experimental results reveal that our method achieves significant improvements over the state-of-the-art methods.

关键词： knowledge graph embedding semantic Web link prediction

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Modeling the Correlations of Relations for knowledge graph embedding

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Journal of Computer Science & Technology 2018年第2期33卷 323-334页

作者： Ji-Zhao Zhu Yan-Tao Jia Jun Xu Jian-Zhong Qiao Xue-Qi Cheng College of Computer Science and Engineering Northeastern University Shenyang 110169 China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing 110190 China

knowledge graph embedding, which maps the entities and relations into low-dimensional vector spaces, has demonstrated its effectiveness in many tasks such as link prediction and relation extraction. Typical methods include TransE, TransH, and TransR. All these methods map different relations into the vector space separately and the intrinsic correlations of these relations are ignored. It is obvious that there exist some correlations among relations because different relations may connect to a common entity. For example, the triples （Steve Jobs, PlaceOfBrith, California） and （Apple Inc., Location, California） share the same entity California as their tail entity. We analyze the embedded relation matrices learned by TransE/TransH/TransR, and find that the correlations of relations do exist and they are showed as low-rank structure over the embedded relation matrix. It is natural to ask whether we can leverage these correlations to learn better embeddings for the entities and relations in a knowledge graph. In this paper, we propose to learn the embedded relation matrix by decomposing it as a product of two low-dimensional matrices, for characterizing the low-rank structure. The proposed method, called TransCoRe （Translation-Based Method via Modeling the Correlations of Relations）, learns the embeddings of entities and relations with translation-based framework. Experimental results based on the benchmark datasets of WordNet and Freebase demonstrate that our method outperforms the typical baselines on link prediction and triple classification tasks.

关键词： knowledge graph embedding low-rank matrix decomposition

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GTrans: Generic knowledge graph embedding via Multi-State Entities and Dynamic Relation Spaces

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IEEE ACCESS 2018年 6卷 8232-8244页

作者： Tan, Zhen Zhao, Xiang Fang, Yang Xiao, Weidong Natl Univ Def Technol Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Dept Informat Syst Engn Changsha 410073 Hunan Peoples R China

knowledge graph embedding aims to construct a low-dimensional and continuous space, which is able to describe the semantics of high-dimensional and sparse knowledge graphs. Among existing solutions, translation models have drawn much attention lately, which use a relation vector to translate the head entity vector, the result of which is close to the tail entity vector. Compared with classical embedding methods, translation models achieve the state-of-the-art performance;nonetheless, the rationale and mechanism behind them still aspire after understanding and investigation. In this connection, we quest into the essence of translation models, and present a generic model, namely, GTrans, to entail all the existing translation models. In GTrans, each entity is interpreted by a combination of two states-eigenstate and mimesis. Eigenstate represents the features that an entity intrinsically owns, and mimesis expresses the features that are affected by associated relations. The weighting of the two states can be tuned, and hence, dynamic and static weighting strategies are put forward to best describe entities in the problem domain. Besides, GTrans incorporates a dynamic relation space for each relation, which not only enables the flexibility of our model but also reduces the noise from other relation spaces. In experiments, we evaluate our proposed model with two benchmark tasks triplets classification and link prediction. Experiment results witness significant and consistent performance gain that is offered by GTrans over existing alternatives.

关键词： knowledge graph embedding multi-state entities dynamic relation spaces triplets classification link prediction

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Path-specific knowledge graph embedding

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knowledge-BASED SYSTEMS 2018年 151卷 37-44页

作者： Jia, Yantao Wang, Yuanzhuo Jin, Xiaolong Cheng, Xueqi Chinese Acad Sci Key Lab Network Data Sci & Technol Inst Comp Technol Beijing Peoples R China

knowledge graph embedding aims to represent entities, relations and multi-step relation paths of a knowledge graph as vectors in low-dimensional vector spaces, and supports many applications, such as entity prediction, relation prediction, etc. Existing embedding methods learn the representations of entities, relations, and multi-step relation paths by minimizing a general margin-based loss function shared by all relation paths. This setting fails to consider the differences among different relation paths. In this paper, we propose an embedding method by minimizing a path-specific margin-based loss function for knowledge graph embedding, called PaSKoGE. For each path, it adaptively determines its margin-based loss function by encoding the correlation between relations and multi-step relation paths for any given pair of entities. PaSKoGE outperforms the-state-of-the-art methods. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Path-specific knowledge graph embedding Relation path

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knowledge graph embedding: A Locally and Temporally Adaptive Translation-Based Approach

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ACM TRANSACTIONS ON THE WEB 2018年第2期12卷 8-8页

作者： Jia, Yantao Wang, Yuanzhuo Jin, Xiaolong Lin, Hailun Cheng, Xueqi Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing 100190 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing 100193 Peoples R China

A knowledge graph is a graph with entities of different types as nodes and various relations among them as edges. The construction of knowledge graphs in the past decades facilitates many applications, such as link prediction, web search analysis, question answering, and so on. knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, for example, TransE, TransH, and TransR, learn the embedding representation by defining a global margin-based loss function over the data. However, the loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidates, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this article, a locally adaptive translation method for knowledge graph embedding, called TransA, is proposed to find the loss function by adaptively determining its margin over different knowledge graphs. Then the convergence of TransA is verified from the aspect of its uniform stability. To make the embedding methods up-to-date when new vertices and edges are added into the knowledge graph, the incremental algorithm for TransA, called iTransA, is proposed by adaptively adjusting the optimal margin over time. Experiments on four benchmark data sets demonstrate the superiority of the proposed method, as compared to the state-of-the-art ones.

关键词： Locally and temporally adaptive translation knowledge graph embedding optimal margin convergence

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knowledge graph embedding: A Survey of Approaches and Applications

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2017年第12期29卷 2724-2743页

作者： Wang, Quan Mao, Zhendong Wang, Bin Guo, Li Chinese Acad Sci Inst Informat Engn Beijing 100049 Peoples R China Univ CAS Sch Cyber Secur Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Informat Secur Beijing 100093 Peoples R China

knowledge graph (KG) embedding is to embed components of a KG including entities and relations into continuous vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. It can benefit a variety of downstream tasks such as KG completion and relation extraction, and hence has quickly gained massive attention. In this article, we provide a systematic review of existing techniques, including not only the state-of-the-arts but also those with latest trends. Particularly, we make the review based on the type of information used in the embedding task. Techniques that conduct embedding using only facts observed in the KG are first introduced. We describe the overall framework, specific model design, typical training procedures, as well as pros and cons of such techniques. After that, we discuss techniques that further incorporate additional information besides facts. We focus specifically on the use of entity types, relation paths, textual descriptions, and logical rules. Finally, we briefly introduce how KG embedding can be applied to and benefit a wide variety of downstream tasks such as KG completion, relation extraction, question answering, and so forth.

关键词： Statistical relational learning knowledge graph embedding latent factor models tensor/matrix factorization models

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Semi-Supervised Aspect-Based Sentiment Analysis for Case-Related Microblog Reviews Using Case knowledge graph embedding

International Journal of Asian Language Processing

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International Journal of Asian Language Processing 2021年第3期30卷

作者： Peilian Zhao Cunli Mao Zhengtao Yu Faculty of Information Engineering and Automation Kunming University of Science and Technology Jingming South Road No.727 Chenggong Kunming Yunnan 650500 P. R. China Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming Yunnan 650500 P. R. China

Aspect-Based Sentiment Analysis (ABSA), a fine-grained task of opinion mining, which aims to extract sentiment of specific target from text, is an important task in many real-world applications, especially in the legal field. Therefore, in this paper, we study the problem of limitation of labeled training data required and ignorance of in-domain knowledge representation for End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) in legal field. We proposed a new method under deep learning framework, named Semi-ETEKGs, which applied E2E framework using knowledge graph (KG) embedding in legal field after data augmentation (DA). Specifically, we pre-trained the BERT embedding and in-domain KG embedding for unlabeled data and labeled data with case elements after DA, and then we put two embeddings into the E2E framework to classify the polarity of target-entity. Finally, we built a case-related dataset based on a popular benchmark for ABSA to prove the efficiency of Semi-ETEKGs, and experiments on case-related dataset from microblog comments show that our proposed model outperforms the other compared methods significantly.

关键词： Semi-supervised learning sentiment analysis knowledge graph embedding

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Enhanced knowledge graph embedding by Jointly Learning Soft Rules and Facts

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ALGORITHMS 2019年第12期12卷 265页

作者： Zhang, Jindou Li, Jing Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China

Combining first order logic rules with a knowledge graph (KG) embedding model has recently gained increasing attention, as rules introduce rich background information. Among such studies, models equipped with soft rules, which are extracted with certain confidences, achieve state-of-the-art performance. However, the existing methods either cannot support the transitivity and composition rules or take soft rules as regularization terms to constrain derived facts, which is incapable of encoding the logical background knowledge about facts contained in soft rules. In addition, previous works performed one time logical inference over rules to generate valid groundings for modeling rules, ignoring forward chaining inference, which can further generate more valid groundings to better model rules. To these ends, this paper proposes Soft Logical rules enhanced embedding (SoLE), a novel KG embedding model equipped with a joint training algorithm over soft rules and KG facts to inject the logical background knowledge of rules into embeddings, as well as forward chaining inference over rules. Evaluations on Freebase and DBpedia show that SoLE not only achieves improvements of 11.6%/5.9% in Mean Reciprocal Rank (MRR) and 18.4%/15.9% in HITS@1 compared to the model on which SoLE is based, but also significantly and consistently outperforms the state-of-the-art baselines in the link prediction task.

关键词： knowledge graph knowledge graph embedding logical rule link prediction

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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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Confidence-Aware Negative Sampling Method for Noisy knowledge graph embedding 9

Confidence-Aware Negative Sampling Method for Noisy Knowledg...

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9th IEEE International Conference on Big knowledge (ICBK)

作者： Shan, Yingchun Bu, Chenyang Liu, Xiaojian Ji, Shengwei Li, Lei Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Anhui Peoples R China

ISBN: (纸本)9781538691250

knowledge graph embedding (KGE) can benefit a variety of downstream tasks, such as link prediction and relation extraction, and has therefore quickly gained much attention. However, most conventional embedding models assume that all triple facts share the same confidence without any noise, which is inappropriate. In fact, many noises and conflicts can be brought into a knowledge graph (KG) because of both the automatic construction process and data quality problems. Fortunately, the novel confidence-aware knowledge representation learning (CKRL) framework was proposed, to incorporate triple confidence into translation-based models for KGE. Though effective at detecting noises, with uniform negative sampling methods, and a harsh triple quality function, CKRL could easily cause zero loss problems and false detection issues. To address these problems, we introduce the concept of negative triple confidence and propose a confidence-aware negative sampling method to support the training of CKRL in noisy KGs. We evaluate our model on the knowledge graph completion task. Experimental results demonstrate that the idea of introducing negative triple confidence can greatly facilitate performance improvement in this task, which confirms the capability of our model in noisy knowledge representation learning (NKRL).

关键词： knowledge graph embedding representation learning noise detection

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