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Urban Flow Pattern Mining Based on Multi-Source Heterogeneous Data Fusion and knowledge graph embedding

IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2023年第2期35卷 2133-2146页

作者： Liu, Jia Li, Tianrui Ji, Shenggong Xie, Peng Du, Shengdong Teng, Fei Zhang, Junbo Southwest Jiaotong Univ Inst Artificial Intelligence Sch Comp & Artificial Intelligence Chengdu 611756 Sichuan Peoples R China Tencent Inc Shenzhen 518054 Guangdong Peoples R China JD Finance Urban Comp Business Unit Shenzhen 518054 Guangdong Peoples R China

Urban flow analysis is an essential research for smart city construction, in which urban flow pattern analysis focuses on the continuous state of urban flow. How to mine, store and reuse traffic patterns from urban multi-source heterogeneous big data is challenging. Therefore, this paper proposes a knowledge mining network for regional flow pattern to mine and store the urban flow pattern. The proposed model consists of two modules. In the first module, the features of the region and its flow pattern are extracted as the entity and relation, respectively. In the second module, POI features are modeled to enhance the embedding representation of relation and entity. Based on the translation distance method, the knowledge triplets of regional flow patterns are mined. Finally, the proposed model is compared with some benchmark methods using Chengdu Didi order and POI datasets. Experimental results show that the proposed model is effective. In addition, the knowledge triplets are visualized and some application examples are introduced.

关键词： Data mining Urban areas knowledge engineering Feature extraction Data models Smart cities Big Data urban flow pattern knowledge graph embedding multi-source heterogeneous data fusion urban computing

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FedCKE: Cross-Domain knowledge graph embedding in Federated Learning

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IEEE TRANSACTIONS ON BIG DATA 2023年第3期9卷 792-804页

作者： Huang, Wei Liu, Jia Li, Tianrui Ji, Shenggong Wang, Dexian Huang, Tianqiang Southwest Jiaotong Univ Inst Artificial Intelligence Sch Comp & Artificial Intelligence Natl Engn Lab Integrated Transportat Big Data Appl Chengdu Peoples R China Tencent Inc Shenzhen 518052 Peoples R China Fujian Normal Univ Coll Math & Informat Fuzhou 350117 Peoples R China

Representing the structural relations between entities, i.e., knowledge graph embedding, which is a method to learn low-dimensional representations of knowledge, has become an increasingly prevalent research orientation in cognitive and human intelligence. It is significant to study how to interrelate, fuse and embed the knowledge graph data from different domains while considering data not shared. In this paper, we propose a model of cross-domain knowledge graph embedding in federated learning (FedCKE), in which entity/relation embedding between different domains can interact securely in the case that data is not shared. In advance of client model training, we present an inter-domain encrypted entity/relation alignment method using the encrypted sample alignment method in vertical federated learning, which can obtain entity/relation intersections between different domains without revealing any triples structure and additional entities/relations in the respective datasets. On the server, we aggregate the same entity/relation embeddings by the association in conjunction with the parameter-secure aggregation method in horizontal federated learning. Experimental results on three real datasets show that the proposed FedCKE model is able to enhance the embedding of different clients (domains).

关键词： Cryptography Collaborative work Data models Training Data privacy Protocols Computational modeling Federated learning knowledge graph embedding encrypted entity relation alignment private set intersection cross-domain

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A quaternion-group knowledge graph embedding model

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第1期41卷 2459-2468页

作者： Chen, Heng Li, Guanyu Sun, Yunhao Jiang, Wei Dalian Maritime Univ Fac Informat Sci & Technol Dalian Peoples R China Dalian Univ Foreign Languages Res Ctr Language Intelligence Dalian Peoples R China

Capturing the composite embedding representation of a multi-hop relation path is an extremely vital task in knowledge graph completion. Recently, rotation-based relation embedding models have been widely studied to embed composite relations into complex vector space. However, these models make some over-simplified assumptions on the composite relations, resulting the relations to be commutative. To tackle this problem, this paper proposes a novel knowledge graph embedding model, named QuatGE, which can provide sufficient modeling capabilities for complex composite relations. In particular, our method models each relation as a rotation operator in quaternion group-based space. The advantages of our model are twofold: (1) Since the quaternion group is a non-commutative group (i.e., non-Abelian group), the corresponding rotation matrices of composite relations can be non-commutative;(2) The model has a more expressive setting with stronger modeling capabilities, which is flexible to model and infer the complete relation patterns, including: symmetry/anti-symmetry, inversion and commutative/non-commutative composition. Experimental results on four benchmark datasets show that the proposed method outperforms the existing state-of-the-art models for link prediction, especially on composite relations.

关键词： knowledge graph embedding quaternion group link prediction

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Relational Learning Analysis of Social Politics using knowledge graph embedding

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DATA MINING AND knowledge DISCOVERY 2021年第4期35卷 1497-1536页

作者： Abu-Salih, Bilal Al-Tawil, Marwan Aljarah, Ibrahim Faris, Hossam Wongthongtham, Pornpit Chan, Kit Yan Beheshti, Amin Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Al Hussein Tech Univ Sch Comp & Informat Amman Jordan Univ Western Australia Perth WA Australia Curtin Univ Perth WA Australia Macquarie Univ Melbourne Vic Australia

knowledge graphs (KGs) have gained considerable attention recently from both academia and industry. In fact, incorporating graph technology and the copious of various graph datasets have led the research community to build sophisticated graph analytics tools, which has extended the application of KGs to tackle a plethora of real-life problems in dissimilar domains. Despite the abundance of the currently proliferated generic KGs, there is a vital need to construct domain-specific KGs. Further, quality and credibility should be assimilated in the process of constructing and augmenting KGs, particularly those propagated from mixed-quality resources such as social media data. For example, the amount of the political discourses in social media is overwhelming yet can be hijacked and misused by spammers to spread misinformation and false news. This paper presents a novel credibility domain-based KG embedding framework. This framework involves capturing a fusion of data related to politics domain and obtained from heterogeneous resources into a formal KG representation depicted by a politics domain ontology. The proposed approach makes use of various knowledge-based repositories to enrich the semantics of the textual contents, thereby facilitating the interoperability of information. The proposed framework also embodies a domain-based social credibility module to ensure data quality and trustworthiness. The utility of the proposed framework is verified by means of experiments conducted on two constructed KGs. The KGs are then embedded in low-dimensional semantically-continuous space using several embedding techniques. The effectiveness of embedding techniques and social credibility module is further demonstrated and substantiated on link prediction, clustering, and visualisation tasks.

关键词： Domain-specific knowledge graphs Social Politics knowledge graph embedding knowledge graph Completion knowledge graph Construction Semantic Analytics Trustworthy knowledge graphs

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Learning graph attention-aware knowledge graph embedding

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NEUROCOMPUTING 2021年 461卷 516-529页

作者： Li, Chen Peng, Xutan Niu, Yuhang Zhang, Shanghang Peng, Hao Zhou, Chuan Li, Jianxin Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Beihang Univ State Key Lab Software Dev Environm Lab Beijing Peoples R China Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA USA Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England

The knowledge graph, which utilizes graph structure to represent multi-relational data, has been widely used in the reasoning and prediction tasks, attracting considerable research efforts recently. However, most existing works still concentrate on learning knowledge graph embeddings straightforwardly and intuitively without subtly considering the context of knowledge. Specifically, recent models deal with each single triple independently or consider contexts indiscriminately, which is one-sided as each knowledge unit (i.e., triple) can be derived from its partial surrounding triples. In this paper, we propose a graph-attention-based model to encode entities, which formulates a knowledge graph as an irregular graph and explores a number of concrete and interpretable knowledge compositions by integrating the graph-structured information via multiple independent channels. To measure the correlation between entities from different angles (i.e., entity pair, relation, and structure), we respectively develop three attention metrics. By making use of our enhanced entity embeddings, we further introduce several improved factorization functions for updating relation embeddings and evaluating candidate triples. We conduct extensive experiments on downstream tasks including entity classification, entity typing, and link prediction to validate our methods. Empirical results validate the importance of our introduced attention metrics and demonstrate that our proposed method can improve the performance of factorization models on large-scale knowledge graphs. (c) 2021 Elsevier B.V. All rights reserved.

关键词： knowledge graph embedding graph attention mechanism Entity typing Link prediction

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PRM-KGED: paper recommender model using knowledge graph embedding and deep neural network

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APPLIED INTELLIGENCE 2023年第24期53卷 30535-30551页

作者： Thierry, Nimbeshaho Bao, Bing-Kun Ali, Zafar Tan, Zhiyi Chatelain, Ingabire Batamira Christ Kefalas, Pavlos Nanjing Univ Posts & Telecommun Coll Informat & Commun Engn Wenyuan Rd Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Xin Mofan Rd Nanjing 210003 Jiangsu Peoples R China Southeast Univ Sch Comp Sci & Engn Southeast Rd Nanjing 211189 Jiangsu Peoples R China Aristotle Univ Thessaloniki Dept Informat Thessaloniki 54124 Greece

During the past decades, several academic paper recommendation systems were introduced in literature aiming to assist users in finding relevant papers close to their needs. In particular, those models have used users' past preferences combined with papers' side information to personalize their recommendations. Unfortunately, the majority of those models fail to utilize the implicit relationship between the crucial elements along with the semantic relations of the nodes in a Heterogeneous Information Network (HIN), which can further improve the accuracy of the models. In this paper, we propose a research article recommendation model that aims to tackle those issues by exploiting both textual and graph representations, simultaneously. The model employs SPECTER document embedding to learn context-preserving research article representations. In particular, it learns the features from a HIN, which is a knowledge graph of the node entities and their relationships. Then, an attention module strengthens the semantic feature extraction process and learns enhanced representations. The combined semantic and structural features are then provided as input to a Deep Neural Network (DNN) in order to learn high-level representations of query and candidate papers. We have evaluated our model against state-of-the-art models over two popular datasets. The results indicate a significant improvement in terms of MAP, recall, and MRR.

关键词： Recommendation systems Research paper recommendations knowledge graph embedding Deep learning

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CoRelatE: Learning the correlation in multi-fold relations for knowledge graph embedding

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knowledge-BASED SYSTEMS 2021年 213卷 106601-106601页

作者： Huang, Yan Sun, Haili Xu, Ke Lu, Songfeng Wang, Tongyang Zhang, Xinfang Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518063 Peoples R China South Cent Univ Nationalities Coll Comp Sci Wuhan 430074 Peoples R China

Existing approaches for knowledge graph embedding usually represent knowledge as triples of binary relations between entities and embed them into continuous vector space, which are not practical for the complex facts in the form of multi-fold relations in real life. In this work, we address the problem of multi-fold relation embedding in knowledge graph and propose a new framework CoRelatE that learns correlations between entities, facts and relations from the instances. We first model the entity-relation correlation directly via combinational operator, then utilize a graph convolutional network to model the correlation between entities and their related facts. Finally, the facts are forced to embed close to their corresponding relations to learn the fact-relation correlation. We formulate the objective as a joint optimization problem and introduce an efficient algorithm to solve it. We compare the proposed CoRelatE model with several state-of-the-art models including HypE, NaLP and RAE, m-TransH. Experimental results on four datasets of multi-fold relations and two datasets of binary relations for link prediction and instance classification tasks validate the effectiveness and merits of our model. (C) 2020 Elsevier B.V. All rights reserved.

关键词： knowledge graph embedding Representation learning Multi-fold relation Correlation learning

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Hierarchical-aware relation rotational knowledge graph embedding for link prediction

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NEUROCOMPUTING 2021年 458卷 259-270页

作者： Wang, Shensi Fu, Kun Sun, Xian Zhang, Zequn Li, Shuchao Jin, Li Chinese Acad Sci Aeroscope Informat Res Inst Beijing 100190 Peoples R China Chinese Acad Sci Aeroscope Informat Res Inst Key Lab Network Informat Syst Technol NIST Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100190 Peoples R China

knowledge graph embedding, as the upstream task of link prediction which aims to predict new links between entities under the premise of known relations, its reliability greatly affects the performance of link prediction. However, previous distance-based models focus on modeling complicated relation patterns while ignoring the semantic hierarchy of knowledge graph, from TransE to RotatE. In this setting, all entities are regarded as the same type, and the fact that different entities belong to different levels is neglected. Therefore, we propose the general form of RotatE, the hierarchical-aware relation rotational knowledge graph embedding (HA-RotatE), to model the hierarchical-aware knowledge graph. HARotatE represents entities and relations as complex vectors and uses different moduli of entity embed dings to indicate the different hierarchical levels they belong to. The transformation of modulus and rotation from head entity to tail entity depends on different relations. Some relations are used to link entities of the same level, and others are used to link entities of different levels. We adopt the shared modulus transformation parameter method for avoiding overfitting. As the general form of RotatE, HA-RotatE also has the ability to model and infer various relation modes, i.e., symmetry/antisymmetric, inversion and composition. On benchmark datasets WN18RR and FB15k-237, the experiments on link prediction tasks show that: (1) HA-RotatE can effectively model the semantic hierarchy of the knowledge graph;(2) Compared with competitive benchmarks, our model substantially outperforms them in most metrics. (c) 2021 Elsevier B.V. All rights reserved.

关键词： knowledge graph embedding Hierarchical-aware Complex vectors Transformation of modulus Link prediction

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Hierarchical relation and entity rotation-based multi-feature knowledge graph embedding

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期45卷 11481-11493页

作者： Zhang, Lei Gu, Yue Xia, Pengfei Wei, Chuyuan Yang, Chengwei Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing Peoples R China Shandong Univ Finance & Econ Sch Management Sci & Engn Jinan Peoples R China

knowledge graphs are knowledge bases that represent entities and relations in the objective world through graph structure, and they have a boosting effect on many artificial intelligence tasks. To facilitate the development of downstream artificial intelligence tasks, knowledge graph embedding (KGE) is proposed. It aims to express semantic information for each entity and relation in the knowledge graph within a low-dimensional space. However, when it comes to semantic hierarchy, multiple relation patterns and multi-fold relational structures in knowledge graphs, most of the existing models tend to focus on only one or two aspects, often neglecting the importance of considering all three simultaneously. Therefore, we propose a new knowledge graph embedding model, Hierarchical relation and Entity Rotation-based Multi-Feature knowledge graph embedding (HERotMFE). Concerning hierarchical relation rotation and entity rotation, it can represent semantic hierarchy, multiple relation patterns and multi-fold relations simultaneously. Self-attention mechanism is used to learn the weights of the two-part rotation to further enhance the model's performance. According to the findings of the experiments, HERotMFE outperforms existing models on most metrics and achieves state-of-the-art results.

关键词： knowledge graph embedding multi-feature multi-fold relation hierarchical relation self-attention mechanism

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Improving temporal knowledge graph embedding using tensor factorization

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APPLIED INTELLIGENCE 2023年第8期53卷 8746-8760页

作者： He, Peng Zhou, Gang Zhang, Mengli Wei, Jianghong Chen, Jing Informat Engn Univ Zhengzhou Peoples R China Zhengzhou Univ Technol Zhengzhou Peoples R China

The approach of knowledge graph embedding (KGE) enables it possible to represent facts of a knowledge graph (KG) in low-dimensional continuous vector spaces. Consequently, it can significantly reduce the complexity of those operations performed on the underlying KG, and has attracted a lot of attention in recent years. However, most of KGE approaches have only been developed over static facts and ignore the time attribute. As a matter of effect, in some real-world KGs, a fact might only be valid for a specific time interval or point in time. For instance, the fact (Barack Obama, is president of, US, [2009-2017]) is only valid between 2009 and 2017. To conquer this issue, based on a famous tensor factorization approach, canonical polyadic decomposition, we propose two new temporal KGE models called TSimplE and TNTSimplE that integrates time information besides static facts. A non-temporal component is also added to deal with heterogeneous temporal KGs that include both temporal and non-temporal relations. We prove that the proposed models are fully expressive which has a bound on the dimensionality of their embeddings, and can incorporate several important types of background knowledge including symmetry, antisymmetry and inversion. In addition, our models are capable of dealing with two common challenges in real-world temporal KGs, i.e., modeling time intervals and predicting time for facts with missing time information. We conduct extensive experiments on three real-world temporal KGs: ICEWS, YAGO3 and Wikidata. The results indicate that our models achieve start-of-the-art performance with lower time or space complexity.

关键词： knowledge graph Temporal knowledge graph knowledge graph embedding knowledge graph representation learning

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