Knowledge Graph Application in Education: a Literature Review

Authors

DOI:

https://doi.org/10.18778/0208-6018.342.01

Keywords:

Knowledge Graph, Knowledge Schema, Resource Description Framework, information, data, knowledge, model, didactic process

Abstract

In modern and developing economic systems, Knowledge Management (KM) is considered to be one of the most important activities of almost any organization. KM process at universities includes didactic processes, among which we distinguish the process of individualization of education. Such process requires a large amount of information to be processed both by university workers and students. This paper suggests that Knowledge Graphs are a technology that facilitates and enhances KM processes at universities and gives an extended review of a Knowledge Graph phenomenon.

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References

Blumauer A. (2014), From Taxonomies over Ontologies to Knowledge Graphs – The Semantic Puzzle, https://semantic‑web.com/2014/07/15/from‑taxonomies‑over‑ontologies‑to‑knowledge‑graphs [accessed: 17.02.2019].
Google Scholar

Bordes A., Usunier N., Garcia‑Duran A., Weston J., Yakhnenko O. (2013), Translating embeddings for modeling multi‑relational data, [in:] C. J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, K. Q. Weinberger (eds.), Proceedings of the Conference Advances in Neural Information Processing Systems 26 (NIPS 2013), pp. 2787–2795, http://papers.nips.cc/paper/5071‑translating‑embeddings‑for‑modeling‑multi‑relational‑data.pdf [accessed: 17.02.2019].
Google Scholar

Bordes A., Weston J., Collobert R., Bengio Y. (2011), Learning structured embeddings of knowledge bases, Twenty‑Fifth AAAI Conference on Artificial Intelligence, http://ronan.collobert.com/pub/matos/2011_knowbases_aaai.pdf [accessed: 1.07.2018].
Google Scholar

Chang K. W., Yih W. T., Yang B., Meek C. (2014), Typed tensor decomposition of knowledge bases for relation extraction, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1568–1579, http://www.aclweb.org/anthology/D14–1165 [accessed: 5.07.2018].
Google Scholar

Chowdhury F. R.R., Ma C., Islam M. R., Namaki M. H., Faruk M. O., Doppa J. R. (2017), Select‑and‑Evaluate: A Learning Framework for Large‑Scale Knowledge Graph Search, Asian Conference on Machine Learning, pp. 129–144, http://proceedings.mlr.press/v77/chowdhury17a/chowdhury17a.pdf [accessed: 17.02.2019].
Google Scholar

Cmap Software (2019), https://cmap.ihmc.us/ [accessed: 17.04.2019].
Google Scholar

Cui Q., Gao B., Bian J., Qiu S., Dai H., Liu T. Y. (2015), KNET: A general framework for learning word embedding using morphological knowledge, “ACM Transactions on Information Systems (TOIS)”, no. 34(1), https://arxiv.org/pdf/1407.1687.pdf [accessed: 17.02.2019].
Google Scholar

DBpedia (2017), DBpedia Version 2016–10, https://wiki.dbpedia.org/develop/datasets/dbpedia‑version–2016–10 [accessed: 17.04.2019].
Google Scholar

Galkin M., Auer S., Vidal M. E., Scerri S. (2017), Enterprise Knowledge Graphs: A Semantic Approach for Knowledge Management in the Next Generation of Enterprise Information Systems, Proceedings of the 19th International Conference on Enterprise Information Systems 2017, vol. 2, pp. 88–98, http://www.scitepress.org/Papers/2017/63252/63252.pdf [accessed: 17.02.2019].
Google Scholar

Gomez‑Perez J. M., Pan J. Z., Vetere G., Wu H. (2017), Enterprise knowledge graph: An introduction, [in:] J. Z. Pan, G. Vetere, J. M. Gomez-Perez and H. Wu (eds.), Exploiting linked data and knowledge graphs in large organisations, Springer, Cham, pp. 1–14.
Google Scholar

Jetschni J., Meister V. G. (2017), Schema engineering for enterprise knowledge graphs: A reflecting survey and case study, 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), IEEE, pp. 271–277.
Google Scholar

Ji G., He S., Xu L., Liu K., Zhao J. (2015), Knowledge graph embedding via dynamic mapping matrix, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 1, pp. 687–696, http://www.aclweb.org/anthology/P15–1067 [accessed: 9.07.2018].
Google Scholar

Lin Y., Liu Z., Sun M., Liu Y., Zhu X. (2015), Learning entity and relation embeddings for knowledge graph completion, Proceedings of the Twenty‑Ninth AAAI Conference on Artificial Intelligence, https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9571/9523 [accessed: 17.02.2019].
Google Scholar

Ma S., Ding J., Jia W., Wang K., Guo M. (2017), TransT: Type‑based multiple embedding representations for knowledge graph completion, [in:] Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, Cham, pp. 717–733, http://ecmlpkdd2017.ijs.si/papers/paperID314.pdf [accessed: 10.07.2018].
Google Scholar

Masuch L., Muszynski H., Raethlein B. (2014), Enterprise Knowledge Graph, www.slideshare.net/BenjaminRaethlein/enterprise‑knowledge‑graph–56858153 [accessed: 11.06.2018].
Google Scholar

Mikolov T., Chen K., Corrado G., Dean J. (2013a), Efficient estimation of word representations in vector space, https://arxiv.org/pdf/1301.3781.pdf [accesses: 17.02.2019].
Google Scholar

Mikolov T., Sutskever I., Chen K., Corrado G. S., Dean J. (2013b), Distributed representations of words and phrases and their compositionality, https://arxiv.org/abs/1310.4546 [accesses: 17.02.2019].
Google Scholar

Nickel M., Murphy K., Tresp V., Gabrilovich E. (2016), A review of relational machine learning for knowledge graphs, Proceedings of the Institute of Electrical and Electronics Engineers, no. 104(1), pp. 11–33, https://arxiv.org/abs/1503.00759 [accessed: 27.05.2018].
Google Scholar

Nickel M., Tresp V., Kriegel H. P. (2011), A Three‑Way Model for Collective Learning on Multi‑Relational Data, Proceedings of the 28th International Conference on Machine Learning, vol. 11, pp. 809–816, http://www.icml–2011.org/papers/438_icmlpaper.pdf [accessed: 8.07.2018].
Google Scholar

Paulheim H. (2016), Knowledge graph refinement: A survey of approaches and evaluation methods, “Semantic Web”, no. 8(3), pp. 489–508, www.semantic‑web‑journal.net/system/files/swj1167.pdf [accessed: 27.05.2018].
Google Scholar

Pirrò G. (2015), Explaining and suggesting relatedness in knowledge graphs, [in:] International Semantic Web Conference, [in:] M. Arenas, O. Corcho, E. Simperl, M. Strohmaier, M. d’Aquin, K. Srinivas, P. Groth, M. Dumontier, J. Heflin, K. Thirunarayan and S. Staab (eds.), The Semantic Web – ISWC 2015. ISWC 2015. Lecture Notes in Computer Science, vol. 9366. Springer, Cham, http://disi.unitn.it/~pavel/OM/articles/93660561.pdf [accessed: 17.02.2019].
Google Scholar

Pujara J., London B., Getoor L., Cohen W. W. (2015), Online Inference for Knowledge Graph Construction, Fifth International Workshop on Statistical Relational AI, www.semanticscholar.org [accessed: 27.05.2018].
Google Scholar

Singhal A. (2012), Introducing the Knowledge Graph: Things, not Strings, https://www.blog.google/products/search/introducing‑knowledge‑graph‑things‑not [accessed: 17.04.2019].
Google Scholar

Socher R., Chen D., Manning C. D., Ng A. (2013), Reasoning with neural tensor networks for knowledge base completion, [in:] C. J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani and K. Q. Weinberger (eds.), Proceedings of the conference Advances in neural information processing systems 26 (NIPS 2013), pp. 926–934, https://papers.nips.cc/paper/5028‑reasoning‑with‑neural‑tensor‑networks‑for‑knowledge‑base‑completion.pdf [accessed: 17.02.2019].
Google Scholar

Tian F., Gao B., Chen E. H., Liu T. Y. (2016), Learning better word embedding by asymmetric low‑rank projection of knowledge graph, “Journal of Computer Science and Technology”, no. 31(3), pp. 624–634, https://link.springer.com/content/pdf/10.1007/s11390–016–1651–5.pdf [accessed: 17.02.2019].
Google Scholar

Velampalli S., Jonnalagedda M. V. (2017), Graph based knowledge discovery using map reduce and subdue algorithm, “Data & Knowledge Engineering”, no. 111, pp. 103–113.
Google Scholar

Wang Z., Zhang J., Feng J., Chen Z. (2014), Knowledge graph embedding by translating on hyper planes, Proceedings of the Twenty‑Eighth AAAI Conference on Artificial Intelligence, https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8531/8546 [accessed: 17.02.2019].
Google Scholar

Wikidata (2018), https://www.wikidata.org/wiki/Wikidata: Main_Page [accessed: 17.04.2019].
Google Scholar

Wikimedia Commons (2019), https://commons.wikimedia.org/wiki/Main_Page [accessed: 17.04.2019].
Google Scholar

World Wide Web Consortium W3C (2014), Resource Description Framework, https://www.w3.org/2001/sw/wiki/RDF [accessed: 17.04.2019].
Google Scholar

World Wide Web Consortium W3C (2019), https://www.w3.org/ [accessed: 17.04.2019].
Google Scholar

Wu Y., Pan J., Lu P., Lin K., Yu Z. (2017), Knowledge Graph Embedding Translation Based on Constraints, “Journal of Information Hiding and Multimedia Signal Processing, Ubiquitous International”, no. 8(5), pp. 1119–1131, bit.kuas.edu.tw/~jihmsp/2017/vol8/JIH‑MSP–2017–05–016.pdf [accessed: 17.02.2019].
Google Scholar

Xu C., Bai Y., Bian J., Gao B., Wang G., Liu X., Liu T. Y. (2014), Rc‑net: A general framework for incorporating knowledge into word representations, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, ACM, pp. 1219–1228, http://ylbai.asiteof.me/km0814‑xu.pdf [accessed: 17.02.2019].
Google Scholar

Yu M., Dredze M. (2014), Improving lexical embeddings with semantic knowledge, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 545–550, www.aclweb.org/anthology/P14–2089 [accessed: 17.02.2019].
Google Scholar

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Published

2019-08-21

How to Cite

Rizun, M. (2019). Knowledge Graph Application in Education: a Literature Review. Acta Universitatis Lodziensis. Folia Oeconomica, 3(342), 7–19. https://doi.org/10.18778/0208-6018.342.01

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