{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:08:41Z","timestamp":1743059321961,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031472398"},{"type":"electronic","value":"9783031472404"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-47240-4_2","type":"book-chapter","created":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T08:02:40Z","timestamp":1698825760000},"page":"23-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dense Re-Ranking with\u00a0Weak Supervision for\u00a0RDF Dataset Search"],"prefix":"10.1007","author":[{"given":"Qiaosheng","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixian","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqing","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tengteng","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3539-7776","authenticated-orcid":false,"given":"Gong","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","first-page":"101846","DOI":"10.1016\/j.is.2021.101846","volume":"104","author":"AG Anadiotis","year":"2022","unstructured":"Anadiotis, A.G., et al.: Graph integration of structured, semistructured and unstructured data for data journalism. Inf. Syst. 104, 101846 (2022). https:\/\/doi.org\/10.1016\/j.is.2021.101846","journal-title":"Inf. Syst."},{"doi-asserted-by":"publisher","unstructured":"Benjelloun, O., Chen, S., Noy, N.F.: Google dataset search by the numbers. In: ISWC 2020, vol. 12507, pp. 667\u2013682 (2020). https:\/\/doi.org\/10.1007\/978-3-030-62466-8_41","key":"2_CR2","DOI":"10.1007\/978-3-030-62466-8_41"},{"doi-asserted-by":"publisher","unstructured":"Brickley, D., Burgess, M., Noy, N.F.: Google dataset search: building a search engine for datasets in an open Web ecosystem. In: WWW 2019, pp. 1365\u20131375 (2019). https:\/\/doi.org\/10.1145\/3308558.3313685","key":"2_CR3","DOI":"10.1145\/3308558.3313685"},{"issue":"3","key":"2_CR4","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s00778-018-0528-3","volume":"28","author":"S Cebiric","year":"2019","unstructured":"Cebiric, S., Goasdou\u00e9, F., Kondylakis, H., Kotzinos, D., Manolescu, I., Troullinou, G., Zneika, M.: Summarizing semantic graphs: a survey. VLDB J. 28(3), 295\u2013327 (2019). https:\/\/doi.org\/10.1007\/s00778-018-0528-3","journal-title":"VLDB J."},{"issue":"1","key":"2_CR5","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/s00778-019-00564-x","volume":"29","author":"A Chapman","year":"2020","unstructured":"Chapman, A., Simperl, E., Koesten, L., Konstantinidis, G., Ib\u00e1\u00f1ez, L., Kacprzak, E., Groth, P.: Dataset search: a survey. VLDB J. 29(1), 251\u2013272 (2020). https:\/\/doi.org\/10.1007\/s00778-019-00564-x","journal-title":"VLDB J."},{"doi-asserted-by":"publisher","unstructured":"Chen, J., Wang, X., Cheng, G., Kharlamov, E., Qu, Y.: Towards more usable dataset search: From query characterization to snippet generation. In: CIKM 2019, pp. 2445\u20132448 (2019). https:\/\/doi.org\/10.1145\/3357384.3358096","key":"2_CR6","DOI":"10.1145\/3357384.3358096"},{"doi-asserted-by":"publisher","unstructured":"Chen, J., Chen, Q., Li, D., Huang, Y.: Sedr: segment representation learning for long documents dense retrieval. CoRR abs\/2211.10841 (2022). https:\/\/doi.org\/10.48550\/arXiv.2211.10841","key":"2_CR7","DOI":"10.48550\/arXiv.2211.10841"},{"doi-asserted-by":"publisher","unstructured":"Cheng, G., Jin, C., Ding, W., Xu, D., Qu, Y.: Generating illustrative snippets for open data on the Web. In: WSDM 2017, pp. 151\u2013159 (2017). https:\/\/doi.org\/10.1145\/3018661.3018670","key":"2_CR8","DOI":"10.1145\/3018661.3018670"},{"unstructured":"Cheng, G., Jin, C., Qu, Y.: HIEDS: a generic and efficient approach to hierarchical dataset summarization. In: IJCAI 2016, pp. 3705\u20133711 (2016)","key":"2_CR9"},{"doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT 2019, vol. 1, pp. 4171\u20134186 (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1423","key":"2_CR10","DOI":"10.18653\/v1\/n19-1423"},{"unstructured":"Izacard, G., et al.: Unsupervised dense information retrieval with contrastive learning. CoRR abs\/2112.09118 (2021). 10.48550\/arXiv.2112.09118","key":"2_CR11"},{"doi-asserted-by":"publisher","unstructured":"Karpukhin, V., et al.: Dense passage retrieval for open-domain question answering. In: EMNLP 2020, pp. 6769\u20136781 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.550","key":"2_CR12","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"doi-asserted-by":"publisher","unstructured":"Kato, M.P., Ohshima, H., Liu, Y., Chen, H.: A test collection for ad-hoc dataset retrieval. In: SIGIR 2021, pp. 2450\u20132456 (2021). https:\/\/doi.org\/10.1145\/3404835.3463261","key":"2_CR13","DOI":"10.1145\/3404835.3463261"},{"doi-asserted-by":"publisher","unstructured":"Khattab, O., Zaharia, M.: ColBERT: efficient and effective passage search via contextualized late interaction over BERT. In: SIGIR 2020, pp. 39\u201348 (2020). https:\/\/doi.org\/10.1145\/3397271.3401075","key":"2_CR14","DOI":"10.1145\/3397271.3401075"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR 2015 (2015)","key":"2_CR15"},{"doi-asserted-by":"publisher","unstructured":"Koesten, L.M., Kacprzak, E., Tennison, J.F.A., Simperl, E.: The trials and tribulations of working with structured data - a study on information seeking behaviour. In: CHI 2017, pp. 1277\u20131289 (2017). https:\/\/doi.org\/10.1145\/3025453.3025838","key":"2_CR16","DOI":"10.1145\/3025453.3025838"},{"doi-asserted-by":"publisher","unstructured":"Lin, J., Nogueira, R.F., Yates, A.: Pretrained Transformers for Text Ranking: BERT and Beyond. Synthesis Lectures on Human Language Technologies, Morgan & Claypool Publishers, San Rafael (2021). https:\/\/doi.org\/10.2200\/S01123ED1V01Y202108HLT053","key":"2_CR17","DOI":"10.2200\/S01123ED1V01Y202108HLT053"},{"doi-asserted-by":"publisher","unstructured":"Lin, T., et al.: ACORDAR: a test collection for ad hoc content-based (RDF) dataset retrieval. In: SIGIR 2022, pp. 2981\u20132991 (2022). https:\/\/doi.org\/10.1145\/3477495.3531729","key":"2_CR18","DOI":"10.1145\/3477495.3531729"},{"doi-asserted-by":"publisher","unstructured":"Liu, D., Cheng, G., Liu, Q., Qu, Y.: Fast and practical snippet generation for RDF datasets. ACM Trans. Web 13(4), 19:1\u201319:38 (2019). https:\/\/doi.org\/10.1145\/3365575","key":"2_CR19","DOI":"10.1145\/3365575"},{"key":"2_CR20","doi-asserted-by":"publisher","first-page":"100647","DOI":"10.1016\/j.websem.2021.100647","volume":"69","author":"Q Liu","year":"2021","unstructured":"Liu, Q., Cheng, G., Gunaratna, K., Qu, Y.: Entity summarization: state of the art and future challenges. J. Web Semant. 69, 100647 (2021). https:\/\/doi.org\/10.1016\/j.websem.2021.100647","journal-title":"J. Web Semant."},{"doi-asserted-by":"publisher","unstructured":"Luo, H., Li, S., Gao, M., Yu, S., Glass, J.R.: Cooperative self-training of machine reading comprehension. In: NAACL 2022, pp. 244\u2013257 (2022). https:\/\/doi.org\/10.18653\/v1\/2022.naacl-main.18","key":"2_CR21","DOI":"10.18653\/v1\/2022.naacl-main.18"},{"doi-asserted-by":"crossref","unstructured":"Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: ACL 2009, pp. 1003\u20131011 (2009)","key":"2_CR22","DOI":"10.3115\/1690219.1690287"},{"unstructured":"Nguyen, T., et al.: MS MARCO: a human generated machine reading comprehension dataset. In: CoCo 2016, vol. 1773 (2016)","key":"2_CR23"},{"doi-asserted-by":"publisher","unstructured":"Pietriga, E., et al.: Browsing linked data catalogs with LODAtlas. In: ISWC 2018, pp. 137\u2013153 (2018). https:\/\/doi.org\/10.1007\/978-3-030-00668-6_9","key":"2_CR24","DOI":"10.1007\/978-3-030-00668-6_9"},{"key":"2_CR25","first-page":"140:1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 140:1-140:67 (2020)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"2_CR26","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1561\/1500000019","volume":"3","author":"S Robertson","year":"2009","unstructured":"Robertson, S., Zaragoza, H.: The probabilistic relevance framework: Bm25 and beyond. Found. Trends Inf. Retr. 3(4), 333\u2013389 (2009). https:\/\/doi.org\/10.1561\/1500000019","journal-title":"Found. Trends Inf. Retr."},{"unstructured":"Wang, X., Cheng, G., Kharlamov, E.: Towards multi-facet snippets for dataset search. In: PROFILES & SEMEX 2019, pp. 1\u20136 (2019)","key":"2_CR27"},{"doi-asserted-by":"publisher","unstructured":"Wang, X., et al.: PCSG: pattern-coverage snippet generation for RDF datasets. In: ISWC 2021, pp. 3\u201320 (2021). https:\/\/doi.org\/10.1007\/978-3-030-88361-4_1","key":"2_CR28","DOI":"10.1007\/978-3-030-88361-4_1"},{"issue":"2","key":"2_CR29","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1109\/TKDE.2021.3095309","volume":"35","author":"X Wang","year":"2023","unstructured":"Wang, X., Cheng, G., Pan, J.Z., Kharlamov, E., Qu, Y.: BANDAR: benchmarking snippet generation algorithms for (RDF) dataset search. IEEE Trans. Knowl. Data Eng. 35(2), 1227\u20131241 (2023). https:\/\/doi.org\/10.1109\/TKDE.2021.3095309","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"2_CR30","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1162\/dint\\_a_00118","volume":"4","author":"X Wang","year":"2022","unstructured":"Wang, X., Lin, T., Luo, W., Cheng, G., Qu, Y.: CKGSE: a prototype search engine for chinese knowledge graphs. Data Intell. 4(1), 41\u201365 (2022). https:\/\/doi.org\/10.1162\/dint_a_00118","journal-title":"Data Intell."},{"unstructured":"Xiong, L., et al.: Approximate nearest neighbor negative contrastive learning for dense text retrieval. In: ICLR 2021 (2021). https:\/\/openreview.net\/forum?id=zeFrfgyZln","key":"2_CR31"},{"doi-asserted-by":"publisher","unstructured":"Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., Ma, S.: Optimizing dense retrieval model training with hard negatives. In: SIGIR 2021, pp. 1503\u20131512 (2021). https:\/\/doi.org\/10.1145\/3404835.3462880","key":"2_CR32","DOI":"10.1145\/3404835.3462880"},{"doi-asserted-by":"publisher","unstructured":"Zhao, W.X., Liu, J., Ren, R., Wen, J.: Dense text retrieval based on pretrained language models: a survey. CoRR abs\/2211.14876 (2022). https:\/\/doi.org\/10.48550\/arXiv.2211.14876","key":"2_CR33","DOI":"10.48550\/arXiv.2211.14876"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web \u2013 ISWC 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47240-4_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T05:21:19Z","timestamp":1730438479000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47240-4_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031472398","9783031472404"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47240-4_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 October 2023","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"semweb2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iswc2023.semanticweb.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"248","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":"58","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":"23% - 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","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":"1","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)"}}]}}