Представление знаний в виде графа: основные технологии и подходы
Аннотация
В последние десятилетия объем накопленной человечеством информации увеличился невероятно. Люди не могут эффективно анализировать такой объем с помощью традиционных алгоритмов и структур данных из-за того, что они не позволяют использовать семантические связи.
Таким образом, назрела необходимость в таком представлении информации, которое бы позволяло бы с одной стороны хранить огромное количество объектов и связей между ними, а с другой предоставляло высокоскоростной доступ к хранящимся данным, и, кроме того, сохраняло семантику. Одной из самых эффективных структур данных, позволяющей решать задачи подобного класса, является граф знаний, который относительно недавно появился и стал предметом исследований в последние годы. Пик интереса к графу знаний пришелся на то время, когда Google представил свою реализацию в 2012 году и стал использовать в своей поисковой машине, что значительно улучшило качество поиска. Однако до сих пор неясно, как воспользоваться данной технологией на практике из-за небольшого количества имеющейся информации по этой теме.
В этой статье мы рассматриваем все этапы реализации графа знаний, а также проблемы, с которыми возможно придется столкнуться при создании собственного экземпляра данной абстракции. Помимо этого, мы рассмотрим методы создания векторного представления информации для ее эффективного хранения в графе, а также практические шаги по его использованию.
Литература
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[15] Hogan M. Facebook Data Storage Centers as the Archive’s Underbelly. Television & New Media. 2013; 16(1):3-18. (In Eng.) DOI: 10.1177/1527476413509415
[16] Huang X., Zhang J., Li D., Li P. Knowledge Graph Embedding Based Question Answering. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (WSDM ’19). Association for Computing Machinery, New York, NY, USA, 2019, pp. 105-113. (In Eng.) DOI: 10.1145/3289600.3290956
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[21] Kushmerick N. Wrapper induction: Efficiency and expressiveness. Artificial Intelligence. 2000; 118(1):15-68. (In Eng.) DOI: 10.1016/S0004-3702(99)00100-9
[22] Lin C.Y., Xue N., Zhao D., Huang X., Feng Y. (Eds.): Natural Language Understanding and Intelligent Applications: 5th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2016, and 24th International Conference on Computer Processing of Oriental Languages, ICCPOL 2016, Kunming, China, December 2-6, 2016, Proceedings, Lecture Notes in Computer Science, vol. 10102. Springer International Publishing, Cham, 2016. (In Eng.) DOI: 10.1007/978-3-319-50496-4
[23] Lissandrini M., Brugnara M., Velegrakis Y. Beyond Macrobenchmarks: Microbenchmark-Based Graph Database Evaluation. Proceedings of the VLDB Endowment. 2018; 12(4):390-403. (In Eng.) DOI: 10.14778/3297753.3297759
[24] Luo A., Gao S., Xu Y. Deep Semantic Match Model for Entity Linking Using Knowledge Graph and Text. Procedia Computer Science . 2018; 129:110-114. (In Eng.) DOI: 10.1016/j.procs.2018.03.057
[25] Malewicz G., Austern M.H., Bik A.J., Dehnert J.C., Horn I., Leiser N., Czajkowski G. Pregel: A System for Large-Scale Graph Processing. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data. Association for Computing Machinery, New York, NY, USA, 2010, pp. 135-146. (In Eng.) DOI: 10.1145/1807167.1807184
[26] Manning C., Surdeanu M., Bauer J., Finkel J., Bethard S., McClosky D. The Stanford CoreNLP Natural Language Processing Toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Association for Computational Linguistics, Baltimore, Maryland, 2014, pp. 55-60. (In Eng.) DOI: 10.3115/v1/P14-5010
[27] Miller G.A. WordNet: A Lexical Database for English. Communications of the ACM. 1995; 38(11):39-41. (In Eng.) DOI: 10.1145/219717.219748
[28] Mitchell T., Cohen W., Hruschka E., Talukdar P., Yang B., Betteridge J., Carlson A., Dalvi B., Gardner M., Kisiel B., Krishnamurthy J., Lao N., Mazaitis K., Mohamed T., Nakashole N., Platanios E., Ritter A., Samadi M., Settles B., Wang R., Wijaya D., Gupta A., Chen X., Saparov A., Greaves M., Welling J. Never-Ending Learning. Communications of the ACM . 2018; 61(5):103-115. (In Eng.) DOI: 10.1145/3191513
[29] Moon C., Jones P., Samatova N.F. Learning Entity Type Embeddings for Knowledge Graph Completion. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM ’17). Association for Computing Machinery, New York, NY, USA, 2017, pp. 2215-2218. (In Eng.) DOI: 10.1145/3132847.3133095
[30] Muslea I., Minton S., Knoblock C.A. Hierarchical Wrapper Induction for Semistructured Information Sources. Autonomous Agents and Multi-Agent Systems . 2001; 4 (1):93-114. (In Eng.) DOI: 10.1023/A:1010022931168
[31] Nguyen N.T., Miwa M., Tsuruoka Y., Chikayama T., Tojo S. Wide-coverage relation extraction from MEDLINE using deep syntax. BMC Bioinformatics. 2015; 16 (1):107. (In Eng.) DOI: 10.1186/s12859-015-0538-8
[32] Pan S.J., Toh Z., Su J. Transfer Joint Embedding for Cross-Domain Named Entity Recognition. ACM Transactions on Information Systems. 2013; 31 (2):1-27. (In Eng.) DOI: 10.1145/2457465.2457467
[33] Peng N., Poon H., Quirk C., Toutanova K., Yih W-t. Cross-Sentence N -ary Relation Extraction with Graph LSTMs. Transactions of the Association for Computational Linguistics . 2017; 5:101-115. (In Eng.) DOI: 10.1162/tacl\_a\_00049
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Опубликована
2019-12-23
Как цитировать
GURIN, Vladislav Sergeevich et al.
Представление знаний в виде графа: основные технологии и подходы.
Современные информационные технологии и ИТ-образование, [S.l.], v. 15, n. 4, p. 932-944, dec. 2019.
ISSN 2411-1473. Доступно на: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/561>. Дата доступа: 20 apr. 2024
doi: https://doi.org/10.25559/SITITO.15.201904.932-944.
Раздел
Исследования и разработки в области новых ИТ и их приложений
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