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knowledge graph embedding via reasoning over entities, relations, and text

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 91卷 426-433页

作者： Nie, Binling Sun, Shouqian Zhejiang Univ Zheda Rd 38 Hangzhou Zhejiang Peoples R China

knowledge graph embedding has attracted significant research interest in the field of intelligent web, which aims to embed both entities and relations into a low-dimensional space. In particular, there are two fundamentally different kinds of models, latent feature models and graph feature models, to infer new predictions in the graph. Latent feature models are expert at using latent features of entities to explain triples and infer these features automatically from the data, while graph feature models are do well in extracting features from the observable graph patterns. Combining the strengths of these two fundamental models is a promising approach to increase the predictive performance of graph models. Thus, we propose a new combined model, named as Text-enhanced knowledge graph embedding (TKGE), to perform inference over entities, relations, and text. The model is not only well-suited for modeling interactions of their latent features, but also well-suited for modeling paths between entities in the graph. Experimental results show that TKGE has significant improvements compared to baselines on two tasks: knowledge graph completion and triple classification. (C) 2018 Elsevier B.V. All rights reserved.

关键词： knowledge graph embedding Text-enhanced knowledge inference

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JointE: Jointly utilizing 1D and 2D convolution for knowledge graph embedding

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knowledge-BASED SYSTEMS 2022年 240卷 108100-108100页

作者： Zhou, Zhehui Wang, Can Feng, Yan Chen, Defang Zhejiang Univ Hangzhou 310027 Peoples R China

knowledge graph embedding is a popular method to predict missing links for knowledge graphs by projecting entities and relations into continuous low-dimension embeddings. Some recent embedding models employ translation-based operations to learn the representations of entities and relations with shallow and linear structures, and others leverage neural networks, especially convolution neural networks, to embed the entities and relations with deep and non-linear structures. However, shallow and linear models limit the extraction capacity of the latent knowledge while deep and non-linear models lead to the overabundance of parameters and the loss of surface and explicit knowledge. In this paper, we propose JointE, which utilizes 1D and 2D convolution operations jointly to alleviate these issues effectively. More specifically, we utilize 2D convolution operations to facilitate the interactions between entities and relations, thereby capturing the latent knowledge sufficiently. To reduce the number of parameters significantly, we innovatively construct 2D convolution filters from internal embeddings rather than using external filters which costs plenty of redundant parameters. Furthermore, we appropriately employ 1D convolution filters over input embeddings to extract the surface and explicit knowledge and preserve it by element-wise addition. Experimental evaluation on five benchmark datasets demonstrates that our model outperforms all other state-of-the-art convolution-based models and simultaneously enhances the parameter efficiency. (c) 2022 Elsevier B.V. All rights reserved.

关键词： knowledge graph embedding knowledge graph Convolution networks

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Timespan-Aware Dynamic knowledge graph embedding by Incorporating Temporal Evolution

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IEEE ACCESS 2020年 8卷 6849-6860页

作者： Tang, Xiaoli Yuan, Rui Li, Qianyu Wang, Tengyun Yang, Haizhi Cai, Yundong Song, Hengjie South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China Chinese Univ Hong Kong Sch Journalism & Commun Hong Kong Peoples R China Nanyang Technol Univ Joint NTU UBC Res Ctr Excellence Act Living Elder Singapore Singapore

Recently, knowledge graph embedding (KGE) has attracted considerable research efforts, since it simplifies the manipulation while preserving the inherent structure of the KG. However to some extent, most existing KGE approaches ignore the historical changes of structural information involved in dynamic knowledge graphs (DKGs). To deal with this problem, this paper presents a Timespan-aware Dynamic knowledge graph embedding Evolution (TDG2E) method that considers temporal evolving process of DKGs. The major innovations of our paper are two-fold. Firstly, a Gated Recurrent Units (GRU) based model is utilized in TDG2E to deal with the dependency among sub-KGs that is inevitably involved in the learning process of the dynamic knowledge graph embedding. Furthermore, we incorporate an auxiliary loss to supervise the learning process of the next sub-KG by utilizing previous structural information (i.e., the hidden state of GRU). In contrast with existing approaches in the literature (e.g., HyTE and t-TransE), TDG2E preserves structural information of current sub-KG and the temporal evolving process of the DKG simultaneously. Secondly, to further deal with the time unbalance issue underlying the DKGs, a Timespan Gate is designed in GRU. It makes TDG2E possible to model the temporal evolving process of DKGs more effectively by incorporating the timespan between adjacent sub-KGs. Extensive experiments on two large temporal datasets (i.e., YAGO11k and Wikidata12k) extracted from real-world KGs validate that the proposed TDG2E significantly outperforms traditional KGE methods in terms of Mean Rank and Hit Rate.

关键词： knowledge graph embedding gated recurrent units time unbalance

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Enhancing knowledge graph embedding by composite neighbors for link prediction

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COMPUTING 2020年第12期102卷 2587-2606页

作者： Wang, Kai Liu, Yu Xu, Xiujuan Sheng, Quan Z. Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116023 Peoples R China Macquarie Univ Dept Comp Sydney NSW 2109 Australia

knowledge graph embedding (KGE) aims to represent entities and relations in a low-dimensional continuous vector space. Recent KGE works focus on incorporating additional information, such as local neighbors and textual descriptions, to learn valuable representations. However, the non-uniformity and redundancy hinder the effectiveness of entity features from those information sources. In this paper, we propose a novel end-to-end framework, called composite neighborhood embedding (CoNE), utilizing composite neighbors to enhance the existing KGE methods. To ease past problems, the new composite neighbors are gathered from both entity descriptions and local neighbors. We design a novel graph Memory Networks to extract entity features from composite neighbors, and fulfill the entity representation in the target KGE method. The experimental results show that CoNE effectively enhances three different KGE methods, TransE, ConvE, and RotatE, and achieves the state-of-the-art results on four real-world large datasets. Furthermore, our approach outperforms the recent text-enhanced models with fewer parameters and calculation. The source code of our work can be obtained from.

关键词： knowledge graph embedding Link prediction graph memory networks knowledge graphs

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Modelling Multi-relations for Convolutional-based knowledge graph embedding

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Procedia Computer Science 2022年 207卷 624-633页

作者： Sirui Li Kok Wai Wong Dengya Zhu Chun Che Fung Discipline of Information Technology Murdoch University South St Murdoch Western Australia

Representation learning of knowledge graphs aims to embed entities and relations into low-dimensional vectors. Most existing works only consider the direct relations or paths between an entity pair. It is considered that such approaches disconnect the semantic connection of multi-relations between an entity pair, and we propose a convolutional and multi-relational representation learning model, ConvMR. The proposed ConvMR model addresses the multi-relation issue in two aspects: (1) Encoding the multi-relations between an entity pair into a unified vector that maintains the semantic connection. (2) Since not all relations are necessary while joining multi-relations, we propose an attention-based relation encoder to automatically assign weights to different relations based on semantic hierarchy. Experimental results on two popular datasets, FB15k-237 and WN18RR, achieved consistent improvements on the mean rank. We also found that ConvMR is efficient to deal with less frequent entities.

关键词： knowledge graph embedding Multi-relation Convolution Representation learning

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TransModE: Translational knowledge graph embedding Using Modular Arithmetic

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Procedia Computer Science 2022年 207卷 1154-1163页

作者： Hussein Baalbaki Hussein Hazimeh Hassan Harb Rafael Angarita Institut Supérieur d'Electronique de Paris LISITE 75006 Paris France Data Science department Faculty of Information Lebanese University Beirut Lebanon Computer Science department Faculty of Sciences Lebanese University Beirut Lebanon

In order to improve link prediction process in a knowledge graph, we tackle the problem of learning representations of entities and relations. The strength of the existing models in this domain mainly relies on its ability of modeling and inferring the patterns of the relations. In this paper, we propose a new knowledge graph embedding approach called TransModE that has the ability to represent all simple and complex relation patterns. Inspired by TransE, TransModE represents the relations between the entities of a knowledge graph as a transition in the modulus space. Experimenting our model on multiple benchmark knowledge graphs shows the simplicity and the scalability of TransModE. It also shows that TransModE outperforms existing state-of-the-art models by being able to infer and model all types of relations.

关键词： knowledge graph embedding knowledge graphs Link Prediction Reasoning

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Improve the translational distance models for knowledge graph embedding

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2020年第3期55卷 445-467页

作者： Zhang, Siheng Sun, Zhengya Zhang, Wensheng Chinese Acad Sci Res Ctr Precis Sensing & Control Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Foshan Univ Sch Math & Big Data Foshan Peoples R China

knowledge graph embedding techniques can be roughly divided into two mainstream, translational distance models and semantic matching models. Though intuitive, translational distance models fail to deal with the circle structure and hierarchical structure in knowledge graphs. In this paper, we propose a general learning framework named TransX-pa, which takes various models (TransE, TransR, TransH and TransD) into consideration. From this unified viewpoint, we analyse the learning bottlenecks are: (i) the common assumption that the inverse of a relation r is modelled as its opposite - r;and (ii) the failure to capture the rich interactions between entities and relations. Correspondingly, we introduce position-aware embeddings and self-attention blocks, and show that they can be adapted to various translational distance models. Experiments are conducted on different datasets extracted from real-world knowledge graphs Freebase and WordNet in the tasks of both triplet classification and link prediction. The results show that our approach makes a great improvement, showing a better, or comparable, performance with state-of-the-art methods.

关键词： knowledge graph embedding Translational distance model Positional encoding Self-attention

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knowledge graph embedding with concepts

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knowledge-BASED SYSTEMS 2019年 164卷 38-44页

作者： Guan, Niannian Song, Dandan Liao, Lejian Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China

knowledge graph embedding aims to embed the entities and relationships of a knowledge graph in low dimensional vector spaces, which can be widely applied to many tasks. Existing models for knowledge graph embedding primarily concentrate on entity-relation-entity triplets, or interact with the text corpus. However, triplets are less informative, and the in-domain text corpus is not always available, making the embedding results deviate from the actual meaning. At the same time, our mental world contains many concepts about worldly facts. For human cognition, compared to knowledge that we learned, common-sense concepts are more basic and general, and they play important roles in human knowledge accumulation. In this paper, based on common-sense concepts information of entities from a concept graph, we propose a knowledge graph embedding with Concepts (KEC) model that embeds entities and concepts of entities jointly into a semantic space. The fact triplets from a knowledge graph are adjusted by the common-sense concept information of entities from a concept graph. Our model not only focuses on the relevance between entities but also focuses on their concepts. Thus, this model offers precise semantic embedding. We evaluate our method on the tasks of knowledge graph completion and entity classification. Experimental results show that our model outperforms other baselines on the two tasks. (C) 2018 Elsevier B.V. All rights reserved.

关键词： knowledge graph embedding Concept space knowledge graph completion

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KEMA plus plus : A Full Representative knowledge-graph embedding Model (036)

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND knowledge ENGINEERING 2022年第11N12期32卷 1619-1641页

作者： Baalbaki, Hussein Hazimeh, Hussein Harb, Hassan Angarita, Rafael Sorbonne Univ ISEP Paris France Lebanese Univ Fac Sci Beirut Lebanon Amer Univ Middle East Coll Engn & Technol Egaila Kuwait

Nowadays, representing entities and relations in a machine understandable way through knowledge graph embedding (KGE) has been proven as an effective approach for predicting missing links in knowledge graphs (KGs). Mainly, the success of such approach depends on the model ability to infer the patterns of the relations. Indeed, most of the existing KG models highly focus on modeling simple relation patterns such as symmetry, anti-symmetry, inversion, and composition. However, there are few models in the literature that take into consideration the modeling of complex relation patterns like 1-N, N-1 and N-N, which are common in real-world applications. To overcome this challenge, this paper presents a new KGE model called KEMA++, i.e. KGE using Modular Arithmetic, that relies on the combination of projection and modular arithmetic. The main idea behind KEMA++ is to project the entities of a relation to represent the relations of a KG, before applying a modular arithmetic over it. Thus, KEMA++ will be able to infer all simple and complex relation patterns for any KGE applications. Through extensive experiments on several datasets, we demonstrated the relevance of KEMA++ in terms of effectively representing all model relations in a KG. Simulations on several tested datasets show that KEMA++ obtains the good scores for Mean Rank (MR) and Hits@1 tests. Moreover, KEMA++ obtains the good Hits@1 score compared to the existing models.

关键词： knowledge graph embedding knowledge graphs link prediction reasoning modular arithmetic (036)

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Explainable knowledge Reasoning Framework Using Multiple knowledge graph embedding 21

Explainable Knowledge Reasoning Framework Using Multiple Kno...

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Proceedings of the 10th International Joint Conference on knowledge graphs

作者： Mori Kurokawa KDDI Research Inc. Japan

ISBN: (纸本)9781450395656

knowledge reasoning using knowledge graphs has attracted much attention. However, there is difficulty in integrating various related works to realize complex reasoning with explanation using multiple knowledge graphs. To do this, I propose a reasoning framework which combines multiple knowledge graph embedding techniques with corresponding explainable AI techniques. Experiments using the third knowledge graph reasoning challenge dataset demonstrate the effectiveness of the framework.

关键词： knowledge reasoning explainable AI knowledge graph embedding knowledge graph

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