Topic analysis and development in knowledge graph research: A bibliometric review on three decades

Abstract Knowledge graph as a research topic is increasingly popular to represent structural relations between entities. Recent years have witnessed the release of various open-source and enterprise-supported knowledge graphs with dramatic growth in applying knowledge representation and reasoning into different areas like natural language processing and computer vision. This study aims to comprehensively explore the status and trends – particularly the thematic research structure – of knowledge graphs. Specifically, based on 386 research articles published from 1991 to 2020, we conducted analyses in terms of the (1) visualization of the trends of annual article and citation counts, (2) recognition of major institutions, countries/regions, and publication sources, (3) visualization of scientific collaborations of major institutions and countries/regions, and (4) detection of major research themes and their developmental tendencies. Interest in knowledge graph research has clearly increased from 1991 to 2020 and is continually expanding. China is the most prolific country in knowledge graph research. Moreover, countries/regions and institutions that have higher levels of international collaboration are more impactful. Several widely studied issues such as knowledge graph embedding, search and query based on knowledge graphs, and knowledge graphs for intangible cultural heritage are highlighted. Based on the results, we further summarize perspective directions and suggestions for researchers, practitioners, and project managers to facilitate future research on knowledge graphs.

[1]  Qiang Zhou,et al.  A Model of Text-Enhanced Knowledge Graph Representation Learning With Mutual Attention , 2020, IEEE Access.

[2]  Hongyu Guo,et al.  Dynamic Graph Convolutional Networks for Entity Linking , 2020, WWW.

[3]  David G. Rand,et al.  Structural Topic Models for Open‐Ended Survey Responses , 2014, American Journal of Political Science.

[4]  Erik Cambria,et al.  Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching , 2016, Cognitive Computation.

[5]  Hoang Long Nguyen,et al.  Social event decomposition for constructing knowledge graph , 2019, Future Gener. Comput. Syst..

[6]  Christopher D. Manning,et al.  Graph Convolution over Pruned Dependency Trees Improves Relation Extraction , 2018, EMNLP.

[7]  Parth Gupta,et al.  Cross-language plagiarism detection over continuous-space- and knowledge graph-based representations of language , 2016, Knowl. Based Syst..

[8]  Lichuan Gu,et al.  A collective entity linking algorithm with parallel computing on large-scale knowledge base , 2019, The Journal of Supercomputing.

[9]  Erik M. van Mulligen,et al.  Automated extraction of potential migraine biomarkers using a semantic graph , 2017, J. Biomed. Informatics.

[10]  Qingfeng Du,et al.  A Causality Mining and Knowledge Graph Based Method of Root Cause Diagnosis for Performance Anomaly in Cloud Applications , 2020, Applied Sciences.

[11]  Lizhe Wang,et al.  Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards , 2019, ISPRS Int. J. Geo Inf..

[12]  Xin Hu,et al.  Scalable aggregate keyword query over knowledge graph , 2020, Future Gener. Comput. Syst..

[13]  Anna Lisa Gentile,et al.  Large-scale relation extraction from web documents and knowledge graphs with human-in-the-loop , 2020, J. Web Semant..

[14]  Wenzhong Guo,et al.  Generative adversarial networks based on Wasserstein distance for knowledge graph embeddings , 2020, Knowl. Based Syst..

[15]  Heiko Paulheim,et al.  Knowledge graph refinement: A survey of approaches and evaluation methods , 2016, Semantic Web.

[16]  Freddy Lécué,et al.  On The Role of Knowledge Graphs in Explainable AI , 2020, PROFILES/SEMEX@ISWC.

[17]  Cungen Cao,et al.  HAPE: A programmable big knowledge graph platform , 2020, Inf. Sci..

[18]  Li Wei,et al.  A comparative quantitative study of utilizing artificial intelligence on electronic health records in the USA and China during 2008–2017 , 2018, BMC Medical Informatics and Decision Making.

[19]  Sangjin Shin,et al.  Processing knowledge graph-based complex questions through question decomposition and recomposition , 2020, Inf. Sci..

[20]  Xiangji Huang,et al.  ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding , 2020, ACL.

[21]  Yongjun Chen,et al.  Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations , 2019, WWW.

[22]  Ronald Denaux,et al.  Vecsigrafo: Corpus-based word-concept embeddings , 2019, Semantic Web.

[23]  Muhammad Jawad Hussain,et al.  An approach for measuring semantic similarity between Wikipedia concepts using multiple inheritances , 2020, Inf. Process. Manag..

[24]  Shan Wang,et al.  A bibliometric analysis of event detection in social media , 2019, Online Inf. Rev..

[25]  Tianyong Hao,et al.  Research topics, author profiles, and collaboration networks in the top-ranked journal on educational technology over the past 40 years: a bibliometric analysis , 2019, Journal of Computers in Education.

[26]  Jianzhong Qiao,et al.  Modeling the Correlations of Relations for Knowledge Graph Embedding , 2018, Journal of Computer Science and Technology.

[27]  Zhao Huang,et al.  Interpreting and predicting social commerce intention based on knowledge graph analysis , 2019, Electronic Commerce Research.

[28]  Yang Zhou,et al.  Construction of Space Object Situation Information Service Based on Knowledge Graph , 2020, IEEE Access.

[29]  Shirui Pan,et al.  Adaptive knowledge subgraph ensemble for robust and trustworthy knowledge graph completion , 2019, World Wide Web.

[30]  Gary Cheng,et al.  A Decade of Sentic Computing: Topic Modeling and Bibliometric Analysis , 2021, Cognitive Computation.

[31]  Rui Wang,et al.  Knowledge Graph Embedding via Graph Attenuated Attention Networks , 2020, IEEE Access.

[32]  Erik Cambria,et al.  Ensemble application of ELM and GPU for real-time multimodal sentiment analysis , 2017, Memetic Computing.

[33]  Erik Cambria,et al.  SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis , 2020, CIKM.

[34]  Gary Cheng,et al.  Topics and trends in artificial intelligence assisted human brain research , 2020, PloS one.

[35]  Tianlong Gu,et al.  Knowledge Graph Embedding by Dynamic Translation , 2017, IEEE Access.

[36]  Zheng Hu,et al.  Distributed representation of knowledge graphs with subgraph-aware proximity , 2020, Theor. Comput. Sci..

[37]  Meng Wang,et al.  Triple Context-Based Knowledge Graph Embedding , 2018, IEEE Access.

[38]  Erik M. van Mulligen,et al.  Using predicate and provenance information from a knowledge graph for drug efficacy screening , 2018, Journal of Biomedical Semantics.

[39]  Tianyong Hao,et al.  A bibliometric analysis of natural language processing in medical research , 2018, BMC Medical Informatics and Decision Making.

[40]  Xiaohui Tao,et al.  Mining health knowledge graph for health risk prediction , 2020, World Wide Web.

[41]  Feng Guo,et al.  A similarity model based on reinforcement local maximum connected same destination structure oriented to disordered fusion of knowledge graphs , 2020, Applied Intelligence.

[42]  Haisheng Li,et al.  A relationship extraction method for domain knowledge graph construction , 2020, World Wide Web.

[43]  Zhifei Li,et al.  Multi-Scale Dynamic Convolutional Network for Knowledge Graph Embedding , 2020, IEEE Transactions on Knowledge and Data Engineering.

[44]  Haoran Xie,et al.  A Structural Topic Modeling-Based Bibliometric Study of Sentiment Analysis Literature , 2020, Cognitive Computation.

[45]  Erik Cambria,et al.  The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools , 2020, Inf. Fusion.

[46]  Yongming Han,et al.  Semantic relation extraction using sequential and tree-structured LSTM with attention , 2020, Inf. Sci..

[47]  Heuiseok Lim,et al.  GREG: A Global Level Relation Extraction with Knowledge Graph Embedding , 2020, Applied Sciences.

[48]  Jiebo Luo,et al.  Constructing biomedical domain-specific knowledge graph with minimum supervision , 2019, Knowledge and Information Systems.

[49]  Xiaoli Tang,et al.  Timespan-Aware Dynamic Knowledge Graph Embedding by Incorporating Temporal Evolution , 2020, IEEE Access.

[50]  Haoran Xie,et al.  Fifty years of British Journal of Educational Technology: A topic modeling based bibliometric perspective , 2020, Br. J. Educ. Technol..

[51]  Sameh K. Mohamed,et al.  Discovering protein drug targets using knowledge graph embeddings , 2019, Bioinform..

[52]  Corey A Lester,et al.  A text mining analysis of medication quality related event reports from community pharmacies. , 2019, Research in social & administrative pharmacy : RSAP.

[53]  Sinno Jialin Pan,et al.  Integrating Deep Learning with Logic Fusion for Information Extraction , 2019, AAAI.

[54]  Jing Gao,et al.  Multi-source knowledge integration based on machine learning algorithms for domain ontology , 2018, Neural Computing and Applications.

[55]  V G Vinod Vydiswaran,et al.  Describing the patient experience from Yelp reviews of community pharmacies. , 2019, Journal of the American Pharmacists Association : JAPhA.

[56]  Haoran Xie,et al.  A bibliometric and visual analysis of artificial intelligence technologies-enhanced brain MRI research , 2020, Multimedia Tools and Applications.

[57]  Young-Tack Park,et al.  Path-based reasoning approach for knowledge graph completion using CNN-BiLSTM with attention mechanism , 2020, Expert Syst. Appl..

[58]  Paolo Rosso,et al.  A systematic study of knowledge graph analysis for cross-language plagiarism detection , 2016, Inf. Process. Manag..

[59]  Jia Zhu,et al.  A semi-supervised model for knowledge graph embedding , 2019, Data Mining and Knowledge Discovery.

[60]  Patrick Gallinari,et al.  Embedding Learning with Triple Trustiness on Noisy Knowledge Graph , 2019, Entropy.

[61]  Catia Pesquita,et al.  Evolving knowledge graph similarity for supervised learning in complex biomedical domains , 2020, BMC Bioinformatics.

[62]  Erik Cambria,et al.  Extracting Time Expressions and Named Entities with Constituent-Based Tagging Schemes , 2020, Cognitive Computation.

[63]  Jiafu Wan,et al.  KnowIME: A System to Construct a Knowledge Graph for Intelligent Manufacturing Equipment , 2020, IEEE Access.

[64]  Yao Wang,et al.  Real-world data medical knowledge graph: construction and applications , 2020, Artif. Intell. Medicine.

[65]  Jun Zhu,et al.  An on-demand construction method of disaster scenes for multilevel users , 2020, Natural Hazards.

[66]  Åsta Dyrnes Nordø,et al.  Citizens’ preferences for tackling climate change. Quantitative and qualitative analyses of their freely formulated solutions , 2017 .

[67]  Jianguo Chen,et al.  Information extraction and knowledge graph construction from geoscience literature , 2018, Comput. Geosci..

[68]  Peijin Cong,et al.  Utilizing Textual Information in Knowledge Graph Embedding: A Survey of Methods and Applications , 2020, IEEE Access.

[69]  Kevin Chen-Chuan Chang,et al.  Embedding Both Finite and Infinite Communities on Graphs [Application Notes] , 2019, IEEE Comput. Intell. Mag..

[70]  Lixin Ji,et al.  Iterative Cross-Lingual Entity Alignment Based on TransC , 2020, IEICE Trans. Inf. Syst..

[71]  Li Shi,et al.  Prospecting Information Extraction by Text Mining Based on Convolutional Neural Networks–A Case Study of the Lala Copper Deposit, China , 2018, IEEE Access.

[72]  C.-C. Jay Kuo,et al.  Graph representation learning: a survey , 2019, APSIPA Transactions on Signal and Information Processing.

[73]  Yuanzhuo Wang,et al.  Path-specific knowledge graph embedding , 2018, Knowl. Based Syst..

[74]  Qi Wang,et al.  ADRL: An attention-based deep reinforcement learning framework for knowledge graph reasoning , 2020, Knowl. Based Syst..

[75]  Zhengya Sun,et al.  Improve the translational distance models for knowledge graph embedding , 2020, Journal of Intelligent Information Systems.

[76]  Jian Du,et al.  A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development , 2020, JMIR medical informatics.

[77]  Haoran Xie,et al.  Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education , 2020, Comput. Educ..

[78]  Margaret E. Roberts,et al.  stm: An R Package for Structural Topic Models , 2019, Journal of Statistical Software.

[79]  Andrea Nanetti,et al.  A Review of Shorthand Systems: From Brachygraphy to Microtext and Beyond , 2020, Cognitive Computation.

[80]  Xiaojun Chen,et al.  A review: Knowledge reasoning over knowledge graph , 2020, Expert Syst. Appl..

[81]  Maria-Esther Vidal,et al.  Compacting frequent star patterns in RDF graphs , 2020, Journal of Intelligent Information Systems.

[82]  Yaqian Wang,et al.  Graph2Seq: Fusion Embedding Learning for Knowledge Graph Completion , 2019, IEEE Access.

[83]  Erik Cambria,et al.  A survey of graph processing on graphics processing units , 2018, The Journal of Supercomputing.

[84]  SoYeop Yoo,et al.  Automating the expansion of a knowledge graph , 2020, Expert Syst. Appl..

[85]  Xiang Zhang,et al.  Entity Profiling in Knowledge Graphs , 2020, IEEE Access.

[86]  Mehran Mohsenzadeh,et al.  Data-driven construction of SPARQL queries by approximate question graph alignment in question answering over knowledge graphs , 2020, Expert Syst. Appl..