{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:25:38Z","timestamp":1742912738613,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030883607"},{"type":"electronic","value":"9783030883614"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-88361-4_14","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T07:07:22Z","timestamp":1632899242000},"page":"235-251","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Open Domain Question Answering over Knowledge Graphs Using Keyword Search, Answer Type Prediction, SPARQL and Pre-trained Neural Models"],"prefix":"10.1007","author":[{"given":"Christos","family":"Nikas","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2788-526X","authenticated-orcid":false,"given":"Pavlos","family":"Fafalios","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8847-2130","authenticated-orcid":false,"given":"Yannis","family":"Tzitzikas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Abbasiantaeb, Z., Momtazi, S.: Text-based question answering from information retrieval and deep neural network perspectives: a survey (2020)","DOI":"10.1002\/widm.1412"},{"key":"14_CR2","unstructured":"Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on Freebase from question-answer pairs. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1533\u20131544 (Oct 2013)"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Deng, J., Krause, J., Berg, A.C., Fei-Fei, L.: Hedging your bets: optimizing accuracy-specificity trade-offs in large scale visual recognition. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3450\u20133457. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6248086"},{"key":"14_CR4","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding (2018)"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Dimitrakis, E., Sgontzos, K., Tzitzikas, Y.: A survey on question answering systems over linked data and documents. J. Intell. Inf. Syst., 1\u201327 (2019)","DOI":"10.1007\/s10844-019-00584-7"},{"issue":"3","key":"14_CR6","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s10791-018-9346-x","volume":"22","author":"D Garigliotti","year":"2019","unstructured":"Garigliotti, D., Hasibi, F., Balog, K.: Identifying and exploiting target entity type information for ad hoc entity retrieval. Inf. Retrieval J. 22(3), 285\u2013323 (2019)","journal-title":"Inf. Retrieval J."},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Hasibi, F., et al.: DBpedia-entity v2: A test collection for entity search. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1265\u20131268 (2017)","DOI":"10.1145\/3077136.3080751"},{"key":"14_CR8","doi-asserted-by":"publisher","unstructured":"Hu, X., Duan, J., Dang, D.: Natural language question answering over knowledge graph: the marriage of SPARQL query and keyword search. Knowl. Inf. Syst. (2021). https:\/\/doi.org\/10.1007\/s10115-020-01534-4","DOI":"10.1007\/s10115-020-01534-4"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Jain, S.: Question answering over knowledge base using factual memory networks. In: Procs of the NAACL Student Research Workshop. Association for Computational Linguistics, San Diego, California, June 2016","DOI":"10.18653\/v1\/N16-2016"},{"key":"14_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-3-030-49461-2_8","volume-title":"The Semantic Web","author":"G Kadilierakis","year":"2020","unstructured":"Kadilierakis, G., Fafalios, P., Papadakos, P., Tzitzikas, Y.: Keyword search over RDF using document-centric information retrieval systems. In: Harth, A., et al. (eds.) ESWC 2020. LNCS, vol. 12123, pp. 121\u2013137. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-49461-2_8"},{"key":"14_CR11","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach (2019)"},{"key":"14_CR12","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.websem.2013.05.006","volume":"21","author":"V Lopez","year":"2013","unstructured":"Lopez, V., Unger, C., Cimiano, P., Motta, E.: Evaluating question answering over linked data. Web Semant. 21, 3\u201313 (2013)","journal-title":"Web Semant."},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Lukovnikov, D., Fischer, A., Lehmann, J., Auer, S.: Neural network-based question answering over knowledge graphs on word and character level. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1211\u20131220 (2017)","DOI":"10.1145\/3038912.3052675"},{"key":"14_CR14","unstructured":"Mihindukulasooriya, N., Dubey, M., Gliozzo, A., Lehmann, J., Ngomo, A.C.N., Usbeck, R.: SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge. CoRR\/arXiv abs\/2012.00555 (2020)"},{"issue":"2","key":"14_CR15","first-page":"1","volume":"12","author":"M Mountantonakis","year":"2020","unstructured":"Mountantonakis, M., Tzitzikas, Y.: Content-based union and complement metrics for dataset search over RDF knowledge graphs. J. Data Inf. Q. (JDIQ) 12(2), 1\u201331 (2020)","journal-title":"J. Data Inf. Q. (JDIQ)"},{"key":"14_CR16","unstructured":"Nikas, C., Fafalios, P., Tzitzikas, Y.: Two-stage semantic answer type prediction for question answering using BERT and class-specificity rewarding. In: Proceedings of the SeMantic AnsweR Type prediction task (SMART) at ISWC 2020, pp. 19\u201328 (2020)"},{"issue":"3","key":"14_CR17","doi-asserted-by":"publisher","first-page":"22","DOI":"10.3390\/bdcc4030022","volume":"4","author":"C Nikas","year":"2020","unstructured":"Nikas, C., Kadilierakis, G., Fafalios, P., Tzitzikas, Y.: Keyword search over RDF: is a single perspective enough? Big Data Cogn. Comput. 4(3), 22 (2020)","journal-title":"Big Data Cogn. Comput."},{"issue":"2","key":"14_CR18","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1145\/3329781.3332266","volume":"17","author":"N Noy","year":"2019","unstructured":"Noy, N., Gao, Y., Jain, A., Narayanan, A., Patterson, A., Taylor, J.: Industry-scale knowledge graphs: lessons and challenges. Queue 17(2), 48\u201375 (2019)","journal-title":"Queue"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Qi, P., Lee, H., Sido, O.T., Manning, C.D.: Retrieve, rerank, read, then iterate: answering open-domain questions of arbitrary complexity from text (2020)","DOI":"10.18653\/v1\/2021.emnlp-main.292"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P., Jia, R., Liang, P.: Know what you don\u2019t know: unanswerable questions for squad. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 784\u2013789 (2018)","DOI":"10.18653\/v1\/P18-2124"},{"key":"14_CR21","unstructured":"Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 (2019)"},{"key":"14_CR22","unstructured":"Setty, V., Balog, K.: Semantic answer type prediction using BERT. In: Procs of the SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge, vol. 2774, pp. 10\u201318 (2020). CEUR-WS.org"},{"key":"14_CR23","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.websem.2014.06.002","volume":"30","author":"S Shekarpour","year":"2015","unstructured":"Shekarpour, S., Marx, E., Ngonga Ngomo, A.C., Auer, S.: SINA: semantic interpretation of user queries for question answering on interlinked data. J. Web Semant. 30, 39\u201351 (2015)","journal-title":"J. Web Semant."},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Singh, K., Lytra, I., Radhakrishna, A.S., Shekarpour, S., Vidal, M.E., Lehmann, J.: No one is perfect: analysing the performance of question answering components over the DBpedia knowledge graph. J. Web Semant. 65, 100594 (2020)","DOI":"10.1016\/j.websem.2020.100594"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web \u2013 ISWC 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88361-4_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:10:02Z","timestamp":1725840602000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88361-4_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030883607","9783030883614"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88361-4_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 September 2021","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"semweb2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iswc2021.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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"202","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":"42","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":"21% - 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":"3.5","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":"2.5","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)"}}]}}