Excursus in knowledge graphs

I.A. Volkova, E.D. Shamaeva


This review examines various aspects of knowledge graphs, which have recently become a popular information storage technology. The graph concept is used to store information about entities and the relationships between them. Knowledge graphs can be useful both for enriching software systems with structured information about the world, and as reference information for people. The knowledge graph concept is revealed, their varieties are noted. Some features of the knowledge graph internal structure are considered, including aspects related to data storage and acquisition, as well as the new knowledge reasoning based on existing one. The issues related to the knowledge graph construction, adding new information to them (including automatic one) and providing users with a modified knowledge graph version are touched upon. The knowledge graph application fields are also indicated. Special attention is paid to their application in various automatic text processing systems, recommendation systems, and machine learning. The process of creating a knowledge graph vector representation is considered separately. In addition, the tasks related to knowledge graphs that have not been solved at the moment are listed. This review will be most interesting for specialists, beginning to get acquainted with knowledge graphs.

Full Text:

PDF (Russian)


T. Hubauer, S. Lamparter, P. Haase, and D. M. Herzig, “Use Cases of the Industrial Knowledge Graph at Siemens.,” 2018.

P. Schneider, T. Schopf, J. Vladika, M. Galkin, E. Simperl, and F. Matthes, “A Decade of Knowledge Graphs in Natural Language Processing: A Survey,” arXiv preprint arXiv:2210.00105, 2022.

D. Fensel et al., Knowledge graphs. Springer, 2020.

D. Vrandečić and M. Krötzsch, “Wikidata: A Free Collaborative Knowledgebase,” Commun. ACM, vol. 57, no. 10, pp. 78–85, Sep. 2014, doi: 10.1145/2629489.

Д. Муромцев, Д. Волчек, and А. Романов, “Индустриальные графы знаний-интеллектуальное ядро цифровой экономики,” Control Engineering Россия, no. 5, pp. 32–39, 2019.

J. Lehmann et al., “DBpedia – A large-scale, multilingual knowledge base extracted from Wikipedia,” Semant Web, vol. 6, no. 2, pp. 167–195, 2015, doi: 10.3233/SW-140134.

A. Hogan et al., “Knowledge graphs,” ACM Computing Surveys (CSUR), vol. 54, no. 4, pp. 1–37, 2021.

Q. Wang, Z. Mao, B. Wang, and L. Guo, “Knowledge graph embedding: A survey of approaches and applications,” IEEE Trans. Knowl. Data Eng., vol. 29, no. 12, pp. 2724–2743, Dec. 2017, doi: 10.1109/TKDE.2017.2754499.

G. Buchgeher, D. Gabauer, J. Martinez-Gil, and L. Ehrlinger, “Knowledge graphs in manufacturing and production: A systematic literature review,” IEEE Access, vol. 9, pp. 55537–55554, 2021.

“BioPortal,” BioPortal. http://bioportal.bioontology.org (accessed Dec. 20, 2022).

F. M. Suchanek, G. Kasneci, and G. Weikum, “Yago: A core of semantic knowledge,” in Proceedings of the 16th international conference on World Wide Web - WWW ’07, New York, New York, USA, May 2007, p. 697, doi: 10.1145/1242572.1242667.

A. Carlson, J. Betteridge, R. C. Wang, E. R. Hruschka, and T. M. Mitchell, “Coupled semi-supervised learning for information extraction,” in Proceedings of the third ACM international conference on Web search and data mining - WSDM ’10, New York, New York, USA, Feb. 2010, p. 101, doi: 10.1145/1718487.1718501.

D. B. Lenat, “CYC: a large-scale investment in knowledge infrastructure,” Commun. ACM, vol. 38, no. 11, pp. 33–38, Nov. 1995, doi: 10.1145/219717.219745.

G. O. Blog, “Introducing the knowledge graph: thing, not strings,” Introducing the Knowledge Graph: things, not strings, 2012.

X. Dong et al., “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 - KDD ’14, New York, New York, USA, Aug. 2014, pp. 601–610, doi: 10.1145/2623330.2623623.

SH. Bhatt, C. CHzhao, A. Seth, and V. SHalin, “Grafy znanij kak sredstvo uluchsheniya iskusstvennogo intellekta,” Otkrytye sistemy. SUBD, no. 3, pp. 24–26, 2020.

S. Harris, A. Seaborne, and E. Prud’hommeaux, “SPARQL 1.1 query language,” W3C recommendation, vol. 21, no. 10, p. 778, 2013.

I. Nikishina, M. Tikhomirov, V. Logacheva, Y. Nazarov, A. Panchenko, and N. Loukachevitch, “Taxonomy enrichment with text and graph vector representations,” Semantic Web, no. Preprint, pp. 1–35, 2022.

“DBpedia Snapshot 2022-09 Release,” DBPedia. https://www.dbpedia.org/blog/dbpedia-snapshot-2022-09-release/ (accessed Dec. 19, 2022).

L. Ehrlinger and W. Wöß, “Towards a definition of knowledge graphs.,” SEMANTiCS (Posters, Demos, SuCCESS), vol. 48, no. 1–4, p. 2, 2016.

X. Chen, S. Jia, and Y. Xiang, “A review: Knowledge reasoning over knowledge graph,” Expert Syst. Appl., vol. 141, p. 112948, Mar. 2020, doi: 10.1016/j.eswa.2019.112948.

A. Kumar, D. Kawahara, and S. Kurohashi, “Knowledge-enriched two-layered attention network for sentiment analysis,” arXiv preprint arXiv:1805.07819, 2018.

D. Li, S. Zamani, J. Zhang, and P. Li, “Integration of knowledge graph embedding into topic modeling with hierarchical dirichlet process,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 2019, pp. 940–950.

S. Kumar, S. Jat, K. Saxena, and P. Talukdar, “Zero-shot Word Sense Disambiguation using Sense Definition Embeddings,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Stroudsburg, PA, USA, 2019, pp. 5670–5681, doi: 10.18653/v1/P19-1568.

B. Fu, Y. Qiu, C. Tang, Y. Li, H. Yu, and J. Sun, “A survey on complex question answering over knowledge base: Recent advances and challenges,” arXiv preprint arXiv:2007.13069, 2020.

D. N. Sosa, A. Derry, M. Guo, E. Wei, C. Brinton, and R. B. Altman, “A Literature-Based Knowledge Graph Embedding Method for Identifying Drug Repurposing Opportunities in Rare Diseases,” BioRxiv, Aug. 2019, doi: 10.1101/727925.

J. Zhou, G. Jiang, W. Du, and C. Han, “Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation,” Electronic Commerce Research, pp. 1–21, 2022.

A. Gyrard and K. Boudaoud, “Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health,” Applied Sciences, vol. 12, no. 19, p. 9712, 2022.

S. Sharifirad, B. Jafarpour, and S. Matwin, “Boosting text classification performance on sexist tweets by text augmentation and text generation using a combination of knowledge graphs,” in Proceedings of the 2nd workshop on abusive language online (ALW2), 2018, pp. 107–114.

I. Tiddi and S. Schlobach, “Knowledge graphs as tools for explainable machine learning: A survey,” Artif. Intell., vol. 302, p. 103627, Jan. 2022, doi: 10.1016/j.artint.2021.103627.

Z. Zhang, X. Han, Z. Liu, X. Jiang, M. Sun, and Q. Liu, “ERNIE: Enhanced language representation with informative entities,” arXiv preprint arXiv:1905.07129, 2019.

A. Bosselut, H. Rashkin, M. Sap, C. Malaviya, A. Celikyilmaz, and Y. Choi, “COMET: Commonsense transformers for automatic knowledge graph construction,” arXiv preprint arXiv:1906.05317, 2019.

W. Liu et al., “K-BERT: Enabling Language Representation with Knowledge Graph,” AAAI, vol. 34, no. 03, pp. 2901–2908, Apr. 2020, doi: 10.1609/aaai.v34i03.5681.

X. Wang et al., “KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation,” Trans. Assoc. Comput. Linguist., vol. 9, pp. 176–194, Mar. 2021, doi: 10.1162/tacl_a_00360.

C. Billy, W. K. Eric, and W. T. Eric, “Knowledge Graph Construction and Applications in E-Retailing: A Review of Literature,” 2021.

S. Zhu, X. Cheng, and S. Su, “Knowledge-based question answering by tree-to-sequence learning,” Neurocomputing, vol. 372, pp.64-72, 2020.


  • There are currently no refbacks.

Abava  Absolutech Convergent 2022

ISSN: 2307-8162