{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:00:41Z","timestamp":1743127241956,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031124228"},{"type":"electronic","value":"9783031124235"}],"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\/978-3-031-12423-5_2","type":"book-chapter","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T17:03:28Z","timestamp":1659027808000},"page":"17-31","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Syntax-Informed Question Answering with\u00a0Heterogeneous Graph Transformer"],"prefix":"10.1007","author":[{"given":"Fangyi","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lok You","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"See-Kiong","family":"Ng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"St\u00e9phane","family":"Bressan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"issue":"6","key":"2_CR1","first-page":"635","volume":"32","author":"MA Calijorne Soares","year":"2020","unstructured":"Calijorne Soares, M.A., Parreiras, F.S.: A literature review on question answering techniques, paradigms and systems. J. King Saud Univ. Comput. Inf. Sci. 32(6), 635\u2013646 (2020)","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"issue":"2","key":"2_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-02154-1","volume":"7","author":"P Cimiano","year":"2014","unstructured":"Cimiano, P., Unger, C., McCrae, J.: Ontology-based interpretation of natural language. Synth. Lect. Hum. Lang. Technol. 7(2), 1\u2013178 (2014)","journal-title":"Synth. Lect. Hum. Lang. Technol."},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"De Cao, N., Aziz, W., Titov, I.: Question answering by reasoning across documents with graph convolutional networks. arXiv arXiv:1808.09920 [cs, stat] (April 2019)","DOI":"10.18653\/v1\/N19-1240"},{"key":"2_CR4","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding (2019)"},{"key":"2_CR5","unstructured":"Dozat, T., Manning, C.D.: Deep biaffine attention for neural dependency parsing. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings. OpenReview.net (2017)"},{"key":"2_CR6","unstructured":"d\u2019Avila Garcez, A., Gori, M., Lamb, L.C., Serafini, L., Spranger, M., Tran, S.N.: Neural-symbolic computing: an effective methodology for principled integration of machine learning and reasoning (2019)"},{"key":"2_CR7","unstructured":"Goldberg, Y.: Assessing BERT\u2019s syntactic abilities. arXiv arXiv:1901.05287 [cs] (January 2019)"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Green, B.F., Wolf, A.K., Chomsky, C., Laughery, K.: Baseball: an automatic question-answerer. In: Papers Presented at the May 9\u201311, 1961, Western Joint IRE-AIEE-ACM Computer Conference, IRE-AIEE-ACM 1961 (Western), pp. 219\u2013224. Association for Computing Machinery, New York, NY, USA (1961)","DOI":"10.1145\/1460690.1460714"},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Han, X., Gao, T., Yao, Y., Ye, D., Liu, Z., Sun, M.: OpenNRE: an open and extensible toolkit for neural relation extraction. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, Hong Kong, China, pp. 169\u2013174. Association for Computational Linguistics (November 2019). https:\/\/doi.org\/10.18653\/v1\/D19-3029. https:\/\/aclanthology.org\/D19-3029","DOI":"10.18653\/v1\/D19-3029"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Han, X., et al.: FewRel: a large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, pp. 4803\u20134809. Association for Computational Linguistics (October\u2013November 2018). https:\/\/doi.org\/10.18653\/v1\/D18-1514. https:\/\/aclanthology.org\/D18-1514","DOI":"10.18653\/v1\/D18-1514"},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR12","doi-asserted-by":"publisher","unstructured":"Honnibal, M., Montani, I., Van Landeghem, S., Boyd, A.: spaCy: industrial-strength natural language processing in Python (2020). https:\/\/doi.org\/10.5281\/zenodo.1212303","DOI":"10.5281\/zenodo.1212303"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Hu, Z., Dong, Y., Wang, K., Sun, Y.: Heterogeneous graph transformer. In: Proceedings of the Web Conference 2020, WWW 2020, New York, NY, USA, pp. 2704\u20132710. Association for Computing Machinery (2020)","DOI":"10.1145\/3366423.3380027"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Jawahar, G., Sagot, B., Seddah, D.: What does BERT learn about the structure of language? In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, pp. 3651\u20133657. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/P19-1356"},{"key":"2_CR15","unstructured":"Jurafsky, D., Martin, J.H.: Dependency parsing. In: Speech and Language Processing, 3rd edn. (2020)"},{"key":"2_CR16","volume-title":"Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition","author":"D Jurafsky","year":"2000","unstructured":"Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 1st edn. Prentice Hall, USA (2000)","edition":"1"},{"key":"2_CR17","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015)"},{"key":"2_CR18","doi-asserted-by":"publisher","unstructured":"Kitaev, N., Klein, D.: Constituency parsing with a self-attentive encoder. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia, pp. 2676\u20132686. Association for Computational Linguistics (July 2018). https:\/\/doi.org\/10.18653\/v1\/P18-1249. https:\/\/aclanthology.org\/P18-1249","DOI":"10.18653\/v1\/P18-1249"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Kuncoro, A., et al.: Syntactic structure distillation pretraining for bidirectional encoders. arXiv arXiv:2005.13482 [cs] (May 2020)","DOI":"10.1162\/tacl_a_00345"},{"key":"2_CR20","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: ALBERT: a lite BERT for self-supervised learning of language representations. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, 26\u201330 April 2020. OpenReview.net (2020). https:\/\/openreview.net\/forum?id=H1eA7AEtvS"},{"key":"2_CR21","unstructured":"Crabtree, M., Powers, J.: Language Files: Materials for an Introduction to Language, 5th edn. Ohio State University Press, Columbus (1991)"},{"key":"2_CR22","unstructured":"Mao, J., Gan, C., Kohli, P., Tenenbaum, J.B., Wu, J.: The neuro-symbolic concept learner: interpreting scenes, words, and sentences from natural supervision. CoRR abs\/1904.12584 (2019). http:\/\/arxiv.org\/abs\/1904.12584"},{"key":"2_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/978-3-319-93417-4_38","volume-title":"The Semantic Web","author":"M Schlichtkrull","year":"2018","unstructured":"Schlichtkrull, M., Kipf, T.N., Bloem, P., van\u00a0den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593\u2013607. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93417-4_38"},{"key":"2_CR24","unstructured":"Nivre, J.: An efficient algorithm for projective dependency parsing. In: Proceedings of the 8th International Conference on Parsing Technologies, Nancy, France, pp. 149\u2013160 (April 2003). https:\/\/aclanthology.org\/W03-3017"},{"key":"2_CR25","doi-asserted-by":"publisher","unstructured":"Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), New Orleans, Louisiana, pp. 2227\u20132237. Association for Computational Linguistics (June 2018). https:\/\/doi.org\/10.18653\/v1\/N18-1202. https:\/\/www.aclweb.org\/anthology\/N18-1202","DOI":"10.18653\/v1\/N18-1202"},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P., Jia, R., Liang, P.: Know what you don\u2019t know: unanswerable questions for SQuAD. arXiv:1806.03822 [cs] (June 2018)","DOI":"10.18653\/v1\/P18-2124"},{"key":"2_CR27","doi-asserted-by":"publisher","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), Melbourne, Australia, pp. 784\u2013789. Association for Computational Linguistics (July 2018). https:\/\/doi.org\/10.18653\/v1\/P18-2124. https:\/\/aclanthology.org\/P18-2124","DOI":"10.18653\/v1\/P18-2124"},{"issue":"1","key":"2_CR28","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. Trans. Neur. Netw. 20(1), 61\u201380 (2009). https:\/\/doi.org\/10.1109\/TNN.2008.2005605","journal-title":"Trans. Neur. Netw."},{"key":"2_CR29","doi-asserted-by":"publisher","unstructured":"Shen, D., Klakow, D.: Exploring correlation of dependency relation paths for answer extraction. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia, pp. 889\u2013896. Association for Computational Linguistics (July 2006). https:\/\/doi.org\/10.3115\/1220175.1220287. https:\/\/aclanthology.org\/P06-1112","DOI":"10.3115\/1220175.1220287"},{"key":"2_CR30","doi-asserted-by":"publisher","unstructured":"Tu, M., Wang, G., Huang, J., Tang, Y., He, X., Zhou, B.: Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, pp. 2704\u20132713. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/P19-1260. https:\/\/www.aclweb.org\/anthology\/P19-1260","DOI":"10.18653\/v1\/P19-1260"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Vashishth, S., Bhandari, M., Yadav, P., Rai, P., Bhattacharyya, C., Talukdar, P.: Incorporating syntactic and semantic information in word embeddings using graph convolutional networks. arXiv arXiv:1809.04283 [cs] (July 2019)","DOI":"10.18653\/v1\/P19-1320"},{"key":"2_CR32","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)"},{"key":"2_CR33","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"2_CR34","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Heterogeneous graph attention network. In: The World Wide Web Conference, New York, NY, USA, pp. 2022\u20132032. Association for Computing Machinery (2019)","DOI":"10.1145\/3308558.3313562"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Welbl, J., Stenetorp, P., Riedel, S.: Constructing datasets for multi-hop reading comprehension across documents. Trans. Assoc. Comput. Linguist. 6, 287\u2013302 (2018). https:\/\/aclanthology.org\/Q18-1021","DOI":"10.1162\/tacl_a_00021"},{"key":"2_CR36","unstructured":"Wu, Y., et al.: Google\u2019s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)"},{"issue":"1","key":"2_CR37","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"2_CR38","doi-asserted-by":"publisher","unstructured":"Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.V.: Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019, pp. 793\u2013803. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3292500.3330961","DOI":"10.1145\/3292500.3330961"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wu, Y., Zhou, J., Duan, S., Zhao, H., Wang, R.: SG-Net: syntax-guided machine reading comprehension. arXiv arXiv:1908.05147 [cs] (November 2019)","DOI":"10.1609\/aaai.v34i05.6511"},{"key":"2_CR40","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57\u201381 (2020)","journal-title":"AI Open"}],"container-title":["Lecture Notes in Computer Science","Database and Expert Systems Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-12423-5_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:57:31Z","timestamp":1710359851000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-12423-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031124228","9783031124235"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-12423-5_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"29 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEXA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database and Expert Systems Applications","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"33","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.dexa.org\/dexa2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"120","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":"43","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":"20","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":"36% - 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":"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":"4","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":"Mixed review process- Single and double blind","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)"}}]}}