{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:27:39Z","timestamp":1743082059238,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030968953"},{"type":"electronic","value":"9783030968960"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-96896-0_3","type":"book-chapter","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T12:16:52Z","timestamp":1657196212000},"page":"53-70","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semantic Vectorization: Text- and Graph-Based Models"],"prefix":"10.1007","author":[{"given":"Shalisha","family":"Witherspoon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dean","family":"Steuer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nirmit","family":"Desai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,8]]},"reference":[{"key":"3_CR1","unstructured":"Devlin J, Chang M, Lee K, Toutanova K (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs\/1810.04805, http:\/\/arxiv.org\/abs\/1810.04805,1810.04805"},{"key":"3_CR2","unstructured":"Frome A, Corrado GS, Shlens J, Bengio S, Dean J, Ranzato M, Mikolov T (2013) Devise: a deep visual-semantic embedding model. In: Advances in neural information processing systems, pp 2121\u20132129"},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1145\/2939672.2939754","volume-title":"Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, KDD\u201916","author":"A Grover","year":"2016","unstructured":"Grover A, Leskovec J (2016) Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, KDD\u201916. Association for Computing Machinery, New York, pp 855\u2013864"},{"issue":"11","key":"3_CR4","first-page":"1","volume":"11","author":"YC Hu","year":"2015","unstructured":"Hu YC, Patel M, Sabella D, Sprecher N, Young V (2015) Mobile edge computing\u2014a key technology towards 5G. ETSI White Pap 11(11):1\u201316","journal-title":"ETSI White Pap"},{"key":"3_CR5","unstructured":"Kanerva P, Kristofersson J, Holst A (2000) Random indexing of text samples for latent semantic analysis. In: Proceedings of the 22nd annual conference of the cognitive science society, vol 1036. Erlbaum, New Jersey"},{"key":"3_CR6","unstructured":"Uesaka Y, Kanerva P, Asoh H, Karlgren J, Sahlgren M (2001) From words to understanding. In: Foundations of real-world intelligence. CSLI Publications, p 294). chapter 26"},{"key":"3_CR7","unstructured":"Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, pp 1188\u20131196"},{"key":"3_CR8","unstructured":"McMahan HB, Moore E, Ramage D, y Arcas BA (2016) Federated learning of deep networks using model averaging. CoRR abs\/1602.05629. http:\/\/arxiv.org\/abs\/1602.05629,1602.05629"},{"key":"3_CR9","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. 1301.3781"},{"key":"3_CR10","unstructured":"Norouzi M, Mikolov T, Bengio S, Singer Y, Shlens J, Frome A, Corrado G, Dean J (2014) Zero-shot learning by convex combination of semantic embeddings. In: Proceedings of 2nd international conference on learning representations"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) GloVe: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"3_CR12","unstructured":"Sahlgren M, Kanerva P (2008) Permutations as a means to encode order in word space. In: Cognitive science\u2014COGSCI"},{"key":"3_CR13","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1145\/1461518.1461544","volume-title":"Proceedings of the fall joint computer conference, AFIPS\u201962 (Fall), 4\u20136 Dec 1962","author":"G Salton","year":"1962","unstructured":"Salton G (1962) Some experiments in the generation of word and document associations. In: Proceedings of the fall joint computer conference, AFIPS\u201962 (Fall), 4\u20136 Dec 1962. Association for Computing Machinery, New York, pp 234\u2013250. https:\/\/doi.org\/10.1145\/1461518.1461544"},{"issue":"11","key":"3_CR14","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1145\/361219.361220","volume":"18","author":"G Salton","year":"1975","unstructured":"Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Commun ACM 18(11):613\u2013620","journal-title":"Commun ACM"},{"issue":"1","key":"3_CR15","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2017.9","volume":"50","author":"M Satyanarayanan","year":"2017","unstructured":"Satyanarayanan M (2017) The emergence of edge computing. Computer 50(1):30\u201339","journal-title":"Computer"},{"issue":"12","key":"3_CR16","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724\u20132743","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"3_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol (TIST) 10(2):1\u201319","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"issue":"8","key":"3_CR18","doi-asserted-by":"publisher","first-page":"1738","DOI":"10.1109\/JPROC.2019.2918951","volume":"107","author":"Z Zhou","year":"2019","unstructured":"Zhou Z, Chen X, Li E, Zeng L, Luo K, Zhang J (2019) Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proc IEEE 107(8):1738\u20131762","journal-title":"Proc IEEE"}],"container-title":["Federated Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-96896-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T12:17:17Z","timestamp":1657196237000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-96896-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030968953","9783030968960"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-96896-0_3","relation":{},"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"8 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}