{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T01:38:04Z","timestamp":1782351484786,"version":"3.54.5"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319682877","type":"print"},{"value":"9783319682884","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"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":[[2017]]},"DOI":"10.1007\/978-3-319-68288-4_41","type":"book-chapter","created":{"date-parts":[[2017,10,4]],"date-time":"2017-10-04T08:59:43Z","timestamp":1507107583000},"page":"694-710","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Towards Holistic Concept Representations: Embedding Relational Knowledge, Visual Attributes, and Distributional Word Semantics"],"prefix":"10.1007","author":[{"given":"Steffen","family":"Thoma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Achim","family":"Rettinger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabian","family":"Both","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,10,4]]},"reference":[{"key":"41_CR1","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv preprint arXiv:1603.04467 (2016)"},{"key":"41_CR2","first-page":"1137","volume":"3","author":"Y Bengio","year":"2003","unstructured":"Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137\u20131155 (2003)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"41_CR3","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s10994-013-5363-6","volume":"94","author":"A Bordes","year":"2014","unstructured":"Bordes, A., Glorot, X., Weston, J., Bengio, Y.: A semantic matching energy function for learning with multi-relational data. Mach. Learn. 94(2), 233\u2013259 (2014)","journal-title":"Mach. Learn."},{"key":"41_CR4","unstructured":"Bordes, A., Usunier, N., Garc\u00eda-Dur\u00e1n, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS 26, pp. 2787\u20132795 (2013)"},{"key":"41_CR5","doi-asserted-by":"crossref","unstructured":"Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: AAAI 2011, pp. 301\u2013306 (2011)","DOI":"10.1609\/aaai.v25i1.7917"},{"key":"41_CR6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1613\/jair.4135","volume":"49","author":"E Bruni","year":"2014","unstructured":"Bruni, E., Tran, N., Baroni, M.: Multimodal distributional semantics. J. Artif. Intell. Res. (JAIR) 49, 1\u201347 (2014)","journal-title":"J. Artif. Intell. Res. (JAIR)"},{"key":"41_CR7","doi-asserted-by":"crossref","unstructured":"Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: ICML 2008, pp. 160\u2013167 (2008)","DOI":"10.1145\/1390156.1390177"},{"key":"41_CR8","doi-asserted-by":"crossref","unstructured":"F\u00e4rber, M., Bartscherer, F., Menne, C., Rettinger, A.: Linked Data Quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Semant. Web J. (2017, to be published)","DOI":"10.3233\/SW-170275"},{"key":"41_CR9","doi-asserted-by":"crossref","unstructured":"Faruqui, M., Dodge, J., Jauhar, S.K., Dyer, C., Hovy, E.H., Smith, N.A.: Retrofitting word vectors to semantic lexicons. In: NAACL HLT 2015, pp. 1606\u20131615 (2015)","DOI":"10.3115\/v1\/N15-1184"},{"key":"41_CR10","doi-asserted-by":"crossref","unstructured":"Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., Ruppin, E.: Placing search in context: the concept revisited. In: WWW 2001, pp. 406\u2013414 (2001)","DOI":"10.1145\/371920.372094"},{"key":"41_CR11","doi-asserted-by":"crossref","unstructured":"Goikoetxea, J., Agirre, E., Soroa, A.: Single or multiple? combining word representations independently learned from text and WordNet. In: AAAI 2016, pp. 2608\u20132614 (2016)","DOI":"10.1609\/aaai.v30i1.10321"},{"key":"41_CR12","doi-asserted-by":"crossref","unstructured":"Goikoetxea, J., Soroa, A., Agirre, E.: Random walks and neural network language models on knowledge bases. In: NAACL HLT 2015, pp. 1434\u20131439 (2015)","DOI":"10.3115\/v1\/N15-1165"},{"key":"41_CR13","doi-asserted-by":"crossref","unstructured":"Halawi, G., Dror, G., Gabrilovich, E., Koren, Y.: Large-scale learning of word relatedness with Constraints. In: ACM SIGKDD 2012, pp. 1406\u20131414 (2012)","DOI":"10.1145\/2339530.2339751"},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Hill, F., Korhonen, A.: Learning abstract concept embeddings from multi-modal data: since you probably can\u2019t see what i mean. In: EMNLP 2014, pp. 255\u2013265 (2014)","DOI":"10.3115\/v1\/D14-1032"},{"issue":"4","key":"41_CR15","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1162\/COLI_a_00237","volume":"41","author":"F Hill","year":"2015","unstructured":"Hill, F., Reichart, R., Korhonen, A.: SimLex-999: evaluating semantic models with (genuine) similarity estimation. Comput. Linguist. 41(4), 665\u2013695 (2015)","journal-title":"Comput. Linguist."},{"issue":"1","key":"41_CR16","doi-asserted-by":"crossref","first-page":"5","DOI":"10.3233\/SW-130100","volume":"5","author":"Y Huang","year":"2014","unstructured":"Huang, Y., Tresp, V., Nickel, M., Rettinger, A., Kriegel, H.: A scalable approach for statistical learning in semantic graphs. Semant. Web 5(1), 5\u201322 (2014)","journal-title":"Semant. Web"},{"key":"41_CR17","unstructured":"Jenatton, R., Roux, N.L., Bordes, A., Obozinski, G.: A latent factor model for highly multi-relational data. In: NIPS 25, pp. 3176\u20133184 (2012)"},{"key":"41_CR18","doi-asserted-by":"crossref","unstructured":"Kiela, D., Bottou, L.: Learning image embeddings using convolutional neural networks for improved multi-modal semantics. In: EMNLP 2014, pp. 36\u201345 (2014)","DOI":"10.3115\/v1\/D14-1005"},{"key":"41_CR19","doi-asserted-by":"publisher","unstructured":"Krompa\u00df, D., Baier, S., Tresp, V.: Type-constrained representation learning in knowledge graphs. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 640\u2013655. Springer, Cham (2015). doi:10.1007\/978-3-319-25007-6_37","DOI":"10.1007\/978-3-319-25007-6_37"},{"key":"41_CR20","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI 2015, pp. 2181\u20132187 (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"41_CR21","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"41_CR22","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS 26, pp. 3111\u20133119 (2013)"},{"key":"41_CR23","unstructured":"Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations. In: NAACL HLT 2013, pp. 746\u2013751 (2013)"},{"key":"41_CR24","unstructured":"Mnih, A., Hinton, G.E.: A scalable hierarchical distributed language model. In: NIPS 21, pp. 1081\u20131088 (2008)"},{"key":"41_CR25","doi-asserted-by":"crossref","unstructured":"Nickel, M., Rosasco, L., Poggio, T.A.: Holographic embeddings of knowledge graphs. In: AAAI 2016, pp. 1955\u20131961 (2016)","DOI":"10.1609\/aaai.v30i1.10314"},{"key":"41_CR26","unstructured":"Nickel, M., Tresp, V., Kriegel, H.: A three-way model for collective learning on multi-relational data. In: ICML 2011, pp. 809\u2013816 (2011)"},{"key":"41_CR27","doi-asserted-by":"crossref","unstructured":"Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: CVPR 2014, pp. 1717\u20131724 (2014)","DOI":"10.1109\/CVPR.2014.222"},{"key":"41_CR28","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP 2014, pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"issue":"3","key":"41_CR29","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/s10618-012-0253-2","volume":"24","author":"A Rettinger","year":"2012","unstructured":"Rettinger, A., L\u00f6sch, U., Tresp, V., d\u2019Amato, C., Fanizzi, N.: Mining the semantic web - statistical learning for next generation knowledge bases. Data Min. Knowl. Discov. 24(3), 613\u2013662 (2012)","journal-title":"Data Min. Knowl. Discov."},{"key":"41_CR30","doi-asserted-by":"crossref","unstructured":"Rothe, S., Sch\u00fctze, H.: AutoExtend: extending word embeddings to embeddings for synsets and lexemes. In: ACL 2015, pp. 1793\u20131803 (2015)","DOI":"10.3115\/v1\/P15-1173"},{"issue":"3","key":"41_CR31","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Li, F.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis."},{"key":"41_CR32","doi-asserted-by":"crossref","unstructured":"Schwartz, R., Reichart, R., Rappoport, A.: Symmetric pattern based word embeddings for improved word similarity prediction. In: CoNLL 2015, pp. 258\u2013267 (2015)","DOI":"10.18653\/v1\/K15-1026"},{"key":"41_CR33","doi-asserted-by":"crossref","unstructured":"Silberer, C., Lapata, M.: Learning grounded meaning representations with autoencoders. In: ACL 2014, pp. 721\u2013732 (2014)","DOI":"10.3115\/v1\/P14-1068"},{"key":"41_CR34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR 2016, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"41_CR35","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML 2016, vol. 48, pp. 2071\u20132080 (2016)"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web \u2013 ISWC 2017"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-68288-4_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:15:22Z","timestamp":1665447322000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-68288-4_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319682877","9783319682884"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-68288-4_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]},"assertion":[{"value":"4 October 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISWC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Semantic Web Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vienna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 October 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2017","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"semweb2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}