Abstract
With the extensive growth of data that has been joined with the thriving development of the Internet in this century, finding or getting valuable information and knowledge from these huge noisy data became harder. The Concept of Knowledge Graph (KG) is one of the concepts that has come into the public view as a result of this development. In addition, with that thriving development especially in the last two decades, the need to process and extract valuable information in a more efficient way is increased. KG presents a common framework for knowledge representation, based on the analysis and extraction of entities and relationships. Techniques for KG construction can extract information from either structured, unstructured or even semi-structured data sources, and finally organize the information into knowledge, represented in a graph. This paper presents a characterization of different types of KGs along with their construction approaches. It reviews the existing academia, industry and expert KG systems and discusses in detail about the features of it. A systematic review methodology has been followed to conduct the review. Several databases (Scopus, GS, WoS) and journals (SWJ, Applied Ontology, JWS) are analysed to collect the relevant study and filtered by using inclusion and exclusion criteria. This review includes the state-of-the-art, literature review, characterization of KGs, and the knowledge extraction techniques of KGs. In addition, this paper overviews the current KG applications, problems, and challenges as well as discuss the perspective of future research. The main aim of this paper is to analyse all existing KGs with their features, techniques, applications, problems, and challenges. To the best of our knowledge, such a characterization table among these most commonly used KGs has not been presented earlier.








Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
References
Abouenour L, Nasri M, Bouzoubaa K, Kabbaj A, Rosso P (2014) Construction of an ontology for intelligent Arabic QA systems leveraging the conceptual graphs representation. J Intell Fuzzy Syst 27(6):2869–2881
Abualigah LM, Khader AT, Hanandeh ES (2018) A novel weighting scheme applied to improve the text document clustering techniques. In: Innovative computing, optimization and its applications. Springer, Cham, pp 305–320
Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin, pp 1–165
Al-Aswadi FN, Chan HY, Gan KH (2019) Automatic ontology construction from text: a review from shallow to deep learning trend. Artificial Intelligence Review 1–28
Angeli G, Manning CD (2013) Philosophers are mortal: Inferring the truth of unseen facts. In Proceedings of the seventeenth conference on computational natural language learning (pp. 133-142)
Arnold P, Rahm E (2014) Extracting semantic concept relations from wikipedia. In Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14) (pp. 1-11)
Baker CF, Fillmore CJ, Lowe JB (1998) The berkeley framenet project. In 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1 (pp. 86-90)
Banko M, Etzioni O (2008) The tradeoffs between open and traditional relation extraction. In Proceedings of ACL-08: HLT (pp. 28-36)
Belleau F, Nolin MA, Tourigny N, Rigault P, Morissette J (2008) Bio2RDF: towards a mashup to build bioinformatics knowledge systems. J Biomed Inf 41(5):706–716
Berners-Lee T (2006). Linked Data http://www.w3.org/DesignIssues.LinkedData.html
Berners-Lee T, Hendler J (2001) Publishing on the semantic web. Nature 410(6832):1023–1024
Bizer C, Heath T, Berners-Lee T (2011) Linked data: The story so far. In Semantic services, interoperability and web applications: emerging concepts (pp. 205-227). IGI Global
Bollacker K, Cook R, Tufts P (2007) Freebase: A shared database of structured general human knowledge. In AAAI (Vol. 7, pp. 1962-1963)
Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data (pp. 1247-1250)
Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka E, Mitchell T (2010) Toward an architecture for never-ending language learning. In: Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 24, No. 1)
Chekol MW, Pirrò G, Schoenfisch J, Stuckenschmidt H (2017) Marrying uncertainty and time in knowledge graphs. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (pp. 88-94)
Chen Y, Kuang J, Cheng D, Zheng J, Gao M, Zhou A (2019) AgriKG: an agricultural knowledge graph and its applications. In International Conference on Database Systems for Advanced Applications. Springer, Cham, pp. 533–537
Chen Y, Li W, Liu Y, Zheng D, Zhao T (2010) Exploring deep belief network for chinese relation extraction. In: CIPS-SIGHAN Joint Conference on Chinese Language Processing
Culotta A, McCallum A (2005) Joint deduplication of multiple record types in relational data. In Proceedings of the 14th ACM international conference on Information and knowledge management (pp. 257-258)
Davis R, Shrobe H, Szolovits P (1993) What is a knowledge representation? AI Mag 14(1):17
De Sa C, Ratner A, Ré C, Shin J, Wang F, Wu S, Zhang C (2016) Deepdive: declarative knowledge base construction. ACM SIGMOD Record 45(1):60–67
Dong Z, Dong Q (2003) HowNet-a hybrid language and knowledge resource. In International Conference on Natural Language Processing and Knowledge Engineering, 2003. Proceedings. 2003 (pp. 820-824). IEEE
Dong X, Gabrilovich E, Heitz G, Horn W, Lao N, Murphy K, Strohmann T, Sun S, Zhang W (2014) Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 601-610)
Etzioni O, Cafarella M, Downey D, Popescu AM, Shaked T, Soderland S, Weld DS, Yates A (2005) Unsupervised named-entity extraction from the web: an experimental study. Artif Intell 165(1):91–134
Etzioni O, Banko M, Soderland S, Weld DS (2008) Open information extraction from the web. Commun ACM 51(12):68–74
Färber M, Bartscherer F, Menne C, Rettinger A (2018) Linked data quality of dbpedia, freebase, opencyc, wikidata, and yago. Sem Web 9(1):77–129
Ferre S (2019, June). Link prediction in knowledge graphs with concepts of nearest neighbours. In European Semantic Web Conference (pp. 84-100). Springer, Cham
Fortunato S (2010) Community detection in graphs. Phys Rep 486(3–5):75–174
Gaurav D, Tiwari SM, Goyal A, Gandhi N, Abraham A (2020) Machine intelligence-based algorithms for spam filtering on document labeling. Soft Comput 24(13):9625–9638
Hakkani-Tür D, Heck L, Tur G (2013) Using a knowledge graph and query click logs for unsupervised learning of relation detection. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 8327-8331). IEEE
Hearst MA (1992) Automatic acquisition of hyponyms from large text corpora. In Coling 1992 volume 2: The 15th international conference on computational linguistics
Heck L, Hakkani-Tür D, Tur G (2013) Leveraging knowledge graphs for web-scale unsupervised semantic parsing
Heist N (2018) Towards knowledge graph construction from entity Co-occurrence. In EKAW (Doctoral Consortium)
Hoffart J, Suchanek FM, Berberich K, Weikum G (2013) YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. Artif Intell 194:28–61
Jia Y, Qi Y, Shang H, Jiang R, Li A (2018) A practical approach to constructing a knowledge graph for cybersecurity. Engineering 4(1):53–60
Ji S, Pan S, Cambria E, Marttinen P, Yu PS (2020) A survey on knowledge graphs: Representation, acquisition and applications. arXiv preprint arXiv:2002.00388
Kambhatla N (2004 ) Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations. In Proceedings of the ACL 2004 on Interactive poster and demonstration sessions (pp. 22-es)
Keele S (2007) Guidelines for performing systematic literature reviews in software engineering (Vol. 5). Technical report, Ver. 2.3 EBSE Technical Report. EBSE
Klyne G, Carroll JJ, McBride B (2004) Resource description framework (RDF): concepts and abstract syntax. W3C Recommendation, Feb. 2004
Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, Mendes PN, Hellmann S, Morsey M, Van Kleef P, Auer S, Bizer C (2015) DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Sem Web 6(2):167–195
Li L, Wang P, Yan J, Wang Y, Li S, Jiang J, Sun Z, Tang B, Chang TH, Wang S, Liu Y (2020) Real-world data medical knowledge graph: construction and applications. Artif Intell Med 103:101817
Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031
Lin Y, Han X, Xie R, Liu Z, Sun M (2018) Knowledge representation learning: A quantitative review. arXiv preprint arXiv:1812.10901
Liu Z, Han X (2018) Deep learning in knowledge graph. Springer, Singapore
Liu H, Singh P (2004) ConceptNet-a practical commonsense reasoning tool-kit. BT Technol J 22(4):211–226
Matuszek C, Witbrock M, Cabral J, DeOliveira J (2006) An introduction to the syntax and content of Cyc. UMBC Computer Science and Electrical Engineering Department Collection
Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11):39–41
Minsky M (1974). A framework for representing knowledge
Mishra S, Jain S (2019) An intelligent knowledge treasure for military decision support. Int J Web-Based Learn Teaching Technol (IJWLTT) 14(3):55–75
Momtchev V, Peychev D, Primov T, Georgiev G (2009) Expanding the pathway and interaction knowledge in linked life data. Proc. of International Semantic Web Challenge
Nakashole N, Theobald M, Weikum G (2011) Scalable knowledge harvesting with high precision and high recall. In Proceedings of the fourth ACM international conference on Web search and data mining (pp. 227-236)
Newcombe HB, Kennedy JM, Axford SJ, James AP (1959) Automatic linkage of vital records. Science 130(3381):954–959
Newman ME (2001) The structure of scientific collaboration networks. Proc Nat Acad Sci 98(2):404–409
Nicholson DN, Greene CS (2020) Constructing knowledge graphs and their biomedical applications. Comput Struct Biotechnol J 18:1414
Nickel M, Murphy K, Tresp V, Gabrilovich E (2015) A review of relational machine learning for knowledge graphs. Proc IEEE 104(1):11–33
Niu X, Sun X, Wang H, Rong S, Qi G, Yu Y (2011) Zhishi. me-weaving chinese linking open data. In International Semantic Web Conference (pp. 205-220). Springer, Berlin, Heidelberg
Noy N, Gao Y, Jain A, Narayanan A, Patterson A, Taylor J (2019) Industry-scale knowledge graphs: lessons and challenges. Queue 17(2):48–75
Paulheim H (2017) Knowledge graph refinement: a survey of approaches and evaluation methods. Sem Web 8(3):489–508
Rahm E, Bernstein PA (2001) A survey of approaches to automatic schema matching. VLDB J 10(4):334–350
Rahul M, Kohli N, Agarwal R, Mishra S (2019) Facial expression recognition using geometric features and modified hidden Markov model. Int J Grid Util Comput 10(5):488–496
Ringler D, Paulheim H (2017) One knowledge graph to rule them all? Analyzing the differences between DBpedia, YAGO, Wikidata & co. In Joint GermanAustrian Conference on Artificial Intelligence (Künstliche Intelligenz) (pp. 366-372). Springer, Cham
Ruttenberg A, Rees JA, Samwald M, Marshall MS (2009) Life sciences on the Semantic Web: the Neurocommons and beyond. Brief Bioinf 10(2):193–204
Saïs F (2019). Knowledge Graph Refinement: Link Detection, Link Invalidation, Key Discovery and Data Enrichment (Doctoral dissertation, Université Paris Sud)
Sengupta S (2013) Facebook unveils a new search tool. NY Times, New York
Singhal A (2012) Introducing the knowledge graph: things, not strings. Official google blog, 5
Sowa JF (2006) Semantic Networks [Electronic resource]. Access mode: http://www.jfsowa.com/pubs/semnet.htm
Suchanek FM, Kasneci G, Weikum G (2007) Yago: a core of semantic knowledge. In Proceedings of the 16th international conference on World Wide Web (pp. 697-706)
Suchanek FM, Sozio M, Weikum G (2009) SOFIE: a self-organizing framework for information extraction. In Proceedings of the 18th international conference on World wide web (pp. 631-640)
Suh B, Convertino G, Chi EH, Pirolli P (2009) The singularity is not near: slowing growth of Wikipedia. In Proceedings of the 5th International Symposium on Wikis and Open Collaboration (pp. 1-10)
Sun Y, Han J (2012) Mining heterogeneous information networks: principles and methodologies. Synth Lect Data Mining Knowl Discov 3(2):1–159
Tejada S, Knoblock CA, Minton S (2001) Learning object identification rules for information integration. Inf Syst 26(8):607–633
Tiwari SM, Jain S, Abraham A, Shandilya S (2018) Secure Semantic Smart HealthCare (S3HC). J Web Eng 17(8):617–646
Tiwari S, Abraham A (2020) Semantic assessment of smart healthcare ontology. International Journal of Web Information Systems
Vrandecic D (2012) Wikidata: a new platform for collaborative data collection. In Proceedings of the 21st international conference on world wide web (pp. 1063-1064)
Wang J, Liu J, Kong L (2017) Ontology construction based on deep learning. In Advances in Computer Science and Ubiquitous Computing Springer, Singapore
Wang P, Jiang H, Xu J, Zhang Q (2019) Knowledge graph construction and applications for Web search and beyond. Data Intell 1(4):333–349
Wang Z, Li J, Wang Z, Li S, Li M, Zhang D, Shi Y, Liu Y, Zhang P, Tang J (2013) XLore: A Large-scale English-Chinese Bilingual Knowledge Graph. In International semantic web conference (Posters & Demos) (Vol. 1035, pp. 121-124)
Wu T, Qi G, Li C, Wang M (2018) A survey of techniques for constructing Chinese knowledge graphs and their applications. Sustainability 10(9):3245
Wu W, Li H, Wang H, Zhu KQ (2012) Probase: A probabilistic taxonomy for text understanding. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (pp. 481-492)
Wu T, Wang H, Li C, Qi G, Niu X, Wang M, Li L, Shi C (2019) Knowledge graph construction from multiple online encyclopedias. World Wide Web 1–28
Xu B, Xu Y, Liang J, Xie C, Liang B, Cui W, Xiao Y (2017) CN-DBpedia: a never-ending Chinese knowledge extraction system. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 428-438). Springer, Cham
Yan J, Wang C, Cheng W, Gao M, Zhou A (2018) A retrospective of knowledge graphs. Front Comput Sci 12(1):55–74
Zhang J, Liu J, Wang X (2016) Simultaneous entities and relationship extraction from unstructured text. Int J Database Theory Appl 9(6):151–160
Zhang Z, Zhuang F, Qu M, Lin F, He Q (2018) Knowledge graph embedding with hierarchical relation structure. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3198-3207)
Zhao M, Wang H, Guo J, Liu D, Xie C, Liu Q, Cheng Z (2019) Construction of an industrial knowledge graph for unstructured Chinese text learning. Appl Sci 9(13):2720
Zhong B, Liu J, Du Y, Liaozheng Y, Pu J (2016) Extracting attributes of named entity from unstructured text with deep belief network. Int J Database Theory Appl 9(5):187–196
Zhu G, Iglesias CA (2015) Sematch: Semantic Entity Search from Knowledge Graph. In SumPre-HSWI@ ESWC
Zhu J, Nie Z, Liu X, Zhang B, Wen JR (2009) Statsnowball: a statistical approach to extracting entity relationships. In Proceedings of the 18th international conference on World wide web (pp. 101-110)
Zou X (2020) A survey on application of knowledge graph. JPhCS 1487(1):012016
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no competing interests.
Compliance with Ethical Standards
This study was not funded by any grant. No animals were involved. This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Tiwari, S., Al-Aswadi, F.N. & Gaurav, D. Recent trends in knowledge graphs: theory and practice. Soft Comput 25, 8337–8355 (2021). https://doi.org/10.1007/s00500-021-05756-8
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-021-05756-8