{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:46:33Z","timestamp":1763664393108,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030624651"},{"type":"electronic","value":"9783030624668"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-62466-8_35","type":"book-chapter","created":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T12:02:53Z","timestamp":1604145773000},"page":"568-584","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["NEO: A Tool for Taxonomy Enrichment with New Emerging Occupations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9419-4800","authenticated-orcid":false,"given":"Anna","family":"Giabelli","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0222-9365","authenticated-orcid":false,"given":"Lorenzo","family":"Malandri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6864-2702","authenticated-orcid":false,"given":"Fabio","family":"Mercorio","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0399-2810","authenticated-orcid":false,"given":"Mario","family":"Mezzanzanica","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7132-7703","authenticated-orcid":false,"given":"Andrea","family":"Seveso","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,1]]},"reference":[{"issue":"7","key":"35_CR1","doi-asserted-by":"publisher","first-page":"eaao6030","DOI":"10.1126\/sciadv.aao6030","volume":"4","author":"A Alabdulkareem","year":"2018","unstructured":"Alabdulkareem, A., Frank, M.R., Sun, L., AlShebli, B., Hidalgo, C., Rahwan, I.: Unpacking the polarization of workplace skills. Sci. Adv. 4(7), eaao6030 (2018)","journal-title":"Sci. Adv."},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"Aly, R., Acharya, S., Ossa, A., K\u00f6hn, A., Biemann, C., Panchenko, A.: Every child should have parents: a taxonomy refinement algorithm based on hyperbolic term embeddings. arXiv preprint arXiv:1906.02002 (2019)","DOI":"10.18653\/v1\/P19-1474"},{"key":"35_CR3","unstructured":"Anh, T.L., Tay, Y., Hui, S.C., Ng, S.K.: Learning term embeddings for taxonomic relation identification using dynamic weighting neural network. In: EMNLP (2016)"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Baroni, M., Dinu, G., Kruszewski, G.: Don\u2019t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. In: ACL (2014)","DOI":"10.3115\/v1\/P14-1023"},{"key":"35_CR5","unstructured":"Bentivogli, L., Bocco, A., Pianta, E.: ArchiWordNet: integrating wordnet with domain-specific knowledge. In: International Global Wordnet Conference (2004)"},{"key":"35_CR6","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. TACL 5, 135\u2013146 (2017)","journal-title":"TACL"},{"issue":"3","key":"35_CR7","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1007\/s10844-017-0488-x","volume":"51","author":"R Boselli","year":"2018","unstructured":"Boselli, R., et al.: WoLMIS: a labor market intelligence system for classifying web job vacancies. J. Intell. Inf. Syst. 51(3), 477\u2013502 (2018). https:\/\/doi.org\/10.1007\/s10844-017-0488-x","journal-title":"J. Intell. Inf. Syst."},{"key":"35_CR8","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.future.2018.03.035","volume":"86","author":"R Boselli","year":"2018","unstructured":"Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M.: Classifying online job advertisements through machine learning. Future Gener. Comput. Syst. 86, 319\u2013328 (2018)","journal-title":"Future Gener. Comput. Syst."},{"key":"35_CR9","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1007\/978-3-319-71273-4_27","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"R Boselli","year":"2017","unstructured":"Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M.: Using machine learning for labour market intelligence. In: Altun, Y., et al. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10536, pp. 330\u2013342. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71273-4_27"},{"key":"35_CR10","unstructured":"CEDEFOP: Real-time labour market information on skill requirements: setting up the EU system for online vacancy analysis (2016). https:\/\/goo.gl\/5FZS3E"},{"key":"35_CR11","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.infoecopol.2019.05.003","volume":"47","author":"E Colombo","year":"2019","unstructured":"Colombo, E., Mercorio, F., Mezzanzanica, M.: AI meets labor market: exploring the link between automation and skills. Inf. Econ. Policy 47, 27\u201337 (2019)","journal-title":"Inf. Econ. Policy"},{"key":"35_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1007\/978-3-319-68288-4_16","volume-title":"The Semantic Web \u2013 ISWC 2017","author":"V Efthymiou","year":"2017","unstructured":"Efthymiou, V., Hassanzadeh, O., Rodriguez-Muro, M., Christophides, V.: Matching web tables with knowledge base entities: from entity lookups to entity embeddings. In: d\u2019Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 260\u2013277. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68288-4_16"},{"key":"35_CR13","doi-asserted-by":"crossref","unstructured":"Espinosa-Anke, L., Camacho-Collados, J., Delli Bovi, C., Saggion, H.: Supervised distributional hypernym discovery via domain adaptation. In: EMNLP (2016)","DOI":"10.18653\/v1\/D16-1041"},{"issue":"3","key":"35_CR14","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.jbi.2005.09.004","volume":"39","author":"C Fellbaum","year":"2006","unstructured":"Fellbaum, C., Hahn, U., Smith, B.: Towards new information resources for public health\u2013from WordNet to MedicalWordNet. J. Biomed. Inform. 39(3), 321\u2013332 (2006)","journal-title":"J. Biomed. Inform."},{"key":"35_CR15","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.techfore.2016.08.019","volume":"114","author":"CB Frey","year":"2017","unstructured":"Frey, C.B., Osborne, M.A.: The future of employment: how susceptible are jobs to computerisation? Technol. Forecast. Soc. Change 114, 254\u2013280 (2017)","journal-title":"Technol. Forecast. Soc. Change"},{"key":"35_CR16","doi-asserted-by":"publisher","unstructured":"Giabelli, A., Malandri, L., Mercorio, F., Mezzanzanica, M.: GraphLMI: a data driven system for exploring labor market information through graph databases. Multimed. Tools Appl. (2020). https:\/\/doi.org\/10.1007\/s11042-020-09115-x. ISSN 1573-7721","DOI":"10.1007\/s11042-020-09115-x"},{"issue":"2\u20133","key":"35_CR17","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1080\/00437956.1954.11659520","volume":"10","author":"ZS Harris","year":"1954","unstructured":"Harris, Z.S.: Distributional structure. Word 10(2\u20133), 146\u2013162 (1954)","journal-title":"Word"},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Jurgens, D., Pilehvar, M.T.: Reserating the awesometastic: an automatic extension of the WordNet taxonomy for novel terms. In: ACL, pp. 1459\u20131465 (2015)","DOI":"10.3115\/v1\/N15-1169"},{"issue":"2","key":"35_CR19","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/5254.920602","volume":"16","author":"A Maedche","year":"2001","unstructured":"Maedche, A., Staab, S.: Ontology learning for the semantic web. IEEE Intell. Syst. 16(2), 72\u201379 (2001)","journal-title":"IEEE Intell. Syst."},{"issue":"4","key":"35_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2641575","volume":"5","author":"M Mezzanzanica","year":"2015","unstructured":"Mezzanzanica, M., Boselli, R., Cesarini, M., Mercorio, F.: A model-based approach for developing data cleansing solutions. J. Data Inf. Qual. (JDIQ) 5(4), 1\u201328 (2015)","journal-title":"J. Data Inf. Qual. (JDIQ)"},{"key":"35_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":"35_CR22","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS (2013)"},{"key":"35_CR23","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: EMNLP, pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"35_CR24","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1613\/jair.514","volume":"11","author":"P Resnik","year":"1999","unstructured":"Resnik, P.: Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. JAIR 11, 95\u2013130 (1999)","journal-title":"JAIR"},{"key":"35_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1007\/978-3-319-46523-4_30","volume-title":"The Semantic Web \u2013 ISWC 2016","author":"P Ristoski","year":"2016","unstructured":"Ristoski, P., Paulheim, H.: RDF2Vec: RDF graph embeddings for data mining. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 498\u2013514. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46523-4_30"},{"key":"35_CR26","doi-asserted-by":"crossref","unstructured":"Schlichtkrull, M., Alonso, H.M.: MSejrKu at SemEval-2016 Task 14: taxonomy enrichment by evidence ranking. In: SemEval, pp. 1337\u20131341 (2016)","DOI":"10.18653\/v1\/S16-1209"},{"key":"35_CR27","doi-asserted-by":"crossref","unstructured":"Shen, J., Shen, Z., Xiong, C., Wang, C., Wang, K., Han, J.: TaxoExpan: self-supervised taxonomy expansion with position-enhanced graph neural network. In: WWW, pp. 486\u2013497 (2020)","DOI":"10.1145\/3366423.3380132"},{"key":"35_CR28","unstructured":"Sumida, A., Torisawa, K.: Hacking Wikipedia for hyponymy relation acquisition. In: IJCNLP (2008)"},{"key":"35_CR29","unstructured":"Toral, A., Monachini, M.: Named entity wordnet. In: LREC (2008)"},{"key":"35_CR30","doi-asserted-by":"crossref","unstructured":"Vedula, N., Nicholson, P.K., Ajwani, D., Dutta, S., Sala, A., Parthasarathy, S.: Enriching taxonomies with functional domain knowledge. In: SIGIR (2018)","DOI":"10.1145\/3209978.3210000"},{"key":"35_CR31","doi-asserted-by":"crossref","unstructured":"Wang, C., He, X., Zhou, A.: A short survey on taxonomy learning from text corpora: issues, resources and recent advances. In: EMLP, pp. 1190\u20131203 (2017)","DOI":"10.18653\/v1\/D17-1123"},{"key":"35_CR32","doi-asserted-by":"crossref","unstructured":"Wang, J., Kang, C., Chang, Y., Han, J.: A hierarchical dirichlet model for taxonomy expansion for search engines. In: WWW, pp. 961\u2013970 (2014)","DOI":"10.1145\/2566486.2568037"},{"key":"35_CR33","doi-asserted-by":"crossref","unstructured":"Xu, T., Zhu, H., Zhu, C., Li, P., Xiong, H.: Measuring the popularity of job skills in recruitment market: a multi-criteria approach. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11847"},{"key":"35_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, D., et al.: Job2Vec: job title benchmarking with collective multi-view representation learning. In: CIKM, pp. 2763\u20132771 (2019)","DOI":"10.1145\/3357384.3357825"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web \u2013 ISWC 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-62466-8_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T23:02:12Z","timestamp":1761865332000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-62466-8_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030624651","9783030624668"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62466-8_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"1 November 2020","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":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"semweb2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iswc2020.semanticweb.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind for the Research Track; single-blind for the Resources and In-use Track","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"287","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"81","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference took place virtually due to the COVID-19 pandemic","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}