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Enhancing knowledge graph embedding with relational constraints

NEUROCOMPUTING 2021年 429卷 77-88页

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

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, translational 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 translational 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 can possess certain robust guarantee. We evaluate our method on the tasks of knowledge graph completion and entity classification. Extensive experiments show that KRC achieves a better, or comparable performance against state-of-the-art methods. Besides, KRC makes a great improvement when dealing with long-tail entities, which have few instances in the knowledge graph. (C) 2020 Elsevier B.V. All rights reserved.

关键词： knowledge graph embedding Translational model Relational constraints knowledge graph completion

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Structured query construction via knowledge graph embedding

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knowledge AND INFORMATION SYSTEMS 2020年第5期62卷 1819-1846页

作者： Wang, Ruijie Wang, Meng Liu, Jun Cochez, Michael Decker, Stefan Xi An Jiao Tong Univ Natl Engn Lab Big Data Analyt Xian Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China Guang Dong Xian Jiaotong Univ Acad Shunde Peoples R China Vrije Univ Amsterdam Amsterdam Netherlands Fraunhofer FIT St Augustin Germany Rhein Westfal TH Aachen Informat 5 Aachen Germany Univ Jyvaskyla Fac Informat Technol Jyvaskyla Finland

In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency.

关键词： knowledge graph Query construction knowledge graph embedding Natural language question answering

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HPRE: Leveraging hierarchy-aware paired relation vectors for knowledge graph embedding

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 5907-5926页

作者： Zhang, Dong Liu, Jinzhu Liu, Duo Li, Guanyu Dalian Maritime Univ Informat Sci & Technol Coll Dalian Peoples R China

knowledge graphs exhibit a typical hierarchical structure and find extensive applications in various artificial intelligence domains. However, large-scale knowledge graphs need to be completed, which limits the performance of knowledge graphs in downstream tasks. knowledge graph embedding methods have emerged as a primary solution to enhance knowledge graph completeness. These methods aim to represent entities and relations as low-dimensional vectors, focusing on handling relation patterns and multi-relation types. Researchers need to pay more attention to the crucial feature of hierarchical relationships in real-world knowledge graphs. We propose a novel knowledge graph embedding model called Hierarchy-Aware Paired Relation Vectors knowledge graph embedding (HPRE) to bridge this gap. By leveraging the power of 2D coordinates, HPRE adeptly model relation patterns, multi-relation types, and hierarchical features in the knowledge graph. Specifically, HPRE employs paired relation vectors to capture the distinct characteristics of head and tail entities, facilitating a better fit for relational patterns and multi-relation scenarios. Additionally, HPRE employs angular coordinates to differentiate entities at various levels of the hierarchy, effectively representing the hierarchical nature of the knowledge graph. The experimental results show that the HPRE model can effectively learn the hierarchical features of the knowledge graph and achieve state-of-the-art experimental results on multiple real-world datasets for the link prediction task.

关键词： knowledge graph completion link prediction knowledge graph embedding knowledge graph representation

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Relation-based multi-type aware knowledge graph embedding q

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NEUROCOMPUTING 2021年 456卷 11-22页

作者： Xue, Yingying Jin, Jiahui Song, Aibo Zhang, Yingxue Liu, Yangyang Wang, Kaixuan Southeast Univ Dept Comp Sci & Engn Nanjing Jiangsu Peoples R China Southeast Univ Dept Cyber Sci & Engn Nanjing Jiangsu Peoples R China Southeast Univ Minist Educ Key Lab Comp Network & Informat Integrat Nanjing Jiangsu Peoples R China

knowledge graph (KG) embedding projects the graph into a low-dimensional space and preserves the graph information. An essential part of a KG is the ontology, which always is organized as a taxonomy tree, depicting the type (or multiple types) of each entity and the hierarchical relationships among these types. The importance of considering the ontology during KG embedding lies in its ability to provide side information, improving the downstream applications' accuracy (e.g., link prediction, entity alignment or recommendation). However, the ontology has yet to receive adequate attention during the KG embedding, especially for instances where each entity may belong to multiple types. This ontology-enhanced KG embedding's main challenges are twofold: determining how to discover the relationships among these types and how to integrate them with the entities' relationship network. Although it is common to see attention-based models used in KG embedding, they cannot settle the issues raised simultaneously. Only a single type is assigned to each entity and the correlation among types are ignored in those models, leading to information loss and encumbered downstream tasks. To overcome these challenges, we propose a composite multi-type aware KG embedding model, whose main components are a multi-type layer and entity embedding layer. We model it as a natural language processing task at the multi-type layer to discover each entity's multi-type feature and automatically capture their correlations. Additionally, a relation-based attention mechanism is conducted at the entity embedding layer, which aggregates neighborhoods' information and integrates the multi-type layer's information through common entities of these two layers. Through extensive experiments on two real KGs, we demonstrate that, compared to several state-of-the-art baselines, our Multi-Type aware embedding (MTE) model achieves substantial gain in both Mean Rank and Hit@N for the link prediction task and accurac

关键词： knowledge graph embedding graph attention network Ontology Taxonomy tree Multi-type

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Relation correlations-aware graph convolutional network with text-enhanced for knowledge graph embedding

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第10期15卷 4659-4668页

作者： Yu, Hong Tang, Jinxuan Peng, Zhihan Wang, Ye Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

Long-tail distribution is a difficult challenge for knowledge graph embedding. We expect to solve the problem by complementing the information through the neighbor aggregation mechanism of GCN. However, the GCN method and its derivations are unable to learn the representation of edges. To address this problem, we propose RCGCN-TE, Relation Correlations-aware graph Convolutional Network with Text-Enhanced for knowledge graph embedding, which is the first effort to enable GCN to learn the representation of relations directly. First, the pre-trained language model is used to extract semantic information. Then, the relation correlation graph is constructed by defining the relation relevance function based on the co-occurrence pattern and semantic similarity of relations. Finally, two GCNS are designed to learn entities and relations respectively. Experimental results on tasks such as triple classification and link prediction are better than the baseline. For example, Hits@10, Hits@3, and Hits@1 improved by 8.23%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, 37.49%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, and 46.94%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, respectively, on the entity prediction task.

关键词： Representation learning GCN knowledge graph embedding Text-enhanced

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CONHyperKGE: Using Contrastive Learning in Hyperbolic Space for knowledge graph embedding

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2024年第4期38卷 2451005-2451005页

作者： Gao, Mandeng Tian, Shengwei Yu, Long Xinjiang Univ Sch Software Urumqi Xinjiang Uygur Peoples R China

The embedding of knowledge graphs (KGs) in hyperbolic space has recently received great attention in the field of deep learning because it can provide more accurate and concise representations of hierarchical structures compared to Euclidean spaces and complex spaces. Although hyperbolic space embeddings have shown significant improvements over Euclidean spaces and complex space embeddings in handling the task of KG embedding, they still face challenges related to the uneven distribution and insufficient alignment of high-dimensional sparse data. To address this issue, we propose the CONHyperKGE model, which leverages contrastive learning to optimize the embedding distribution in hyperbolic space. This approach enables better capture of hierarchical structures, improved handling of symmetry, and enhanced treatment of sparse matrices. Our proposed method is evaluated on four standard KG embedding (KGE) datasets: WN18RR, FB15k-237, Kinship, and UMLS. After extensive experimental verification, our method has improved its performance on all four datasets. Notably, on the low-dimensional Kinship dataset, our method achieves an average Mean Reciprocal Rank (MRR) improvement of 2% over the original method, while on the high-dimensional WN18RR dataset, an average MRR improvement of 1% is observed compared to the original method.

关键词： knowledge graph hyperbolic space embedding knowledge graph embedding contrastive learning

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SSKGE: a time-saving knowledge graph embedding framework based on structure enhancement and semantic guidance

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APPLIED INTELLIGENCE 2023年第21期53卷 25171-25183页

作者： Wang, Tao Shen, Bo Zhong, Yu Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Municipal Commiss Educ Key Lab Commun & Informat Syst Beijing 100044 Peoples R China

In knowledge graph embedding, an attempt is made to embed the objective facts and relationships expressed in the form of triplets into multidimensional vector space, facilitating various applications, such as link prediction and question answering. Structure embedding models focus on the graph structure while the importance of language semantics in inferring similar entities and relations is ignored. Semantic embedding models use pretrained language models to learn entity and relation embeddings based on text information, but they do not fully exploit graph structures that reflect relation patterns and mapping attributes. Structure and semantic information in knowledge graphs represent different hierarchical properties that are indispensable for comprehensive knowledge representation. In this paper, we propose a general knowledge graph embedding framework named SSKGE, which considers both the graph structure and language semantics and learns these two complementary characteristics to integrate entity and relation representations. To compensate for semantic embedding approaches that ignore the graph structure, we first design a structure loss function to explicitly model the graph structure attributes. Second, we leverage a pretrained language model that has been fine-tuned by the structure loss to guide the structure embedding approaches in enhancing the semantic information they lack and obtaining universal knowledge representations. Specifically, guidance is provided by a distance function that makes the spatial distribution of the two types of graph embeddings have a certain similarity. SSKGE significantly reduces the time cost of using a pretrained language model to complete a knowledge graph. Common knowledge graph embedding models such as TransE, DistMult, ComplEx, RotatE, PairRE, and HousE have achieved better results with multiple datasets, including FB15k, FB15k-237, WN18, and WN18RR, using the SSKGE framework. Extensive experiments and analyses have verifi

关键词： knowledge graph embedding knowledge graph completion Structure enhancement Semantic guidance

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Enhancing knowledge graph embedding with structure and semantic features

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APPLIED INTELLIGENCE 2024年第3期54卷 2900-2914页

作者： Wang, Yalin Peng, Yubin Guo, Jingyu Cent South Univ Sch Automat Changsha 410083 Hunan Peoples R China

knowledge graph embedding converts knowledge graphs based on symbolic representations into low-dimensional vectors. Effective knowledge graph embedding methods are key to ensuring downstream tasks. Some studies have shown significant performance differences among various knowledge graph embedding models on different datasets. They attribute this issue to the insufficient representation ability of the models. However, what representation ability knowledge graph embedding models possess is still unknown. Therefore, this paper first selects three representative models for analysis: translation and rotation models in distance models, and the Bert model in neural network models. Based on the analysis results, it can be concluded that the translation model focuses on clustering features, the rotation model focuses on hierarchy features, and the Bert model focuses on word co-occurrence features. This paper categorize clustering and hierarchy as structure features, and word co-occurrence as semantic features. Furthermore, a model that solely focuses on a single feature will lead to a lack of accuracy and generality, making it challenging for the model to be applicable to modern large-scale knowledge graphs. Therefore, this paper proposes an ensemble model with structure and semantic features for knowledge graph embedding. Specifically, the ensemble model includes a structure part and a semantic part. The structure part consists of three models: translation, rotation and cross. Translation and rotation models serve as basic feature extraction, while the cross model enhances the interaction between them. The semantic part is built based on Bert and integrated with the structure part after fine-tuning. In addition, this paper also introduces a frequency model to mitigate the training imbalance caused by differences in entity frequencies. Finally, we verify the effectiveness of the model through link prediction. Experiments show that the ensemble model has achieved improvement

关键词： knowledge graph embedding Ensemble learning Link prediction Distance model Neural network model

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ContE: contextualized knowledge graph embedding for circular relations

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DATA MINING AND knowledge DISCOVERY 2023年第1期37卷 110-135页

作者： Ma, Ting Li, Mingming Lv, Shangwen Zhu, Fuqing Huang, Longtao Hu, Songlin Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100049 Peoples R China JD Com Beijing 100176 Peoples R China Alibaba Grp Beijing 100102 Peoples R China

knowledge graph embedding has been proposed to embed entities and relations into continuous vector spaces, which can benefit various downstream tasks, such as question answering and recommender systems, etc. A common assumption of existing knowledge graph embedding models is that the relation is a translation vector connecting the embedded head entity and tail entity. However, based on this assumption, the same relation connecting multiple entities may form a circle and lead to mistakes during computing process. To solve this so-called circular relation problem that has been ignored previously, we propose a novel method called ContE (Contextualized embedding) for knowledge graphs by exploring collaborative relations. Specifically, each collaborative relation combines an explicit relation and a latent relation, where the explicit one is the original relation between two entities, and the latent one is introduced to capture the implicit interactions obtained via the context information of the two entities. With the collaborative relations, the same relations will be embedded varying across different contexts, and the sharing of similar semantics will be guaranteed as well. We conduct extensive experiments in link prediction and triple classification tasks on five benchmark datasets. The experimental results demonstrate that the proposed ContE achieves improvements over several baselines.

关键词： knowledge graph embedding Circular relations Explicit relations Collaborative relations

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Triple Context-Based knowledge graph embedding

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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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