{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T03:18:10Z","timestamp":1768447090556,"version":"3.49.0"},"reference-count":27,"publisher":"Cambridge University Press (CUP)","issue":"4","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"unspecified","delay-in-days":24,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Theory and Practice of Logic Programming"],"published-print":{"date-parts":[[2023,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time-consuming, labor-intensive, and error-prone. Human beings learn using both data (through induction) and knowledge (through deduction). Answer Set Programming (ASP) has been a widely utilized approach for knowledge representation and reasoning that is elaboration tolerant and adept at reasoning with incomplete information. This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. In particular, ASPER takes advantage of the factual knowledge (represented as facts in ASP) and derived knowledge (represented as rules in ASP) in the learning process of neural network models. We have conducted experiments on two real datasets and compare our method with three baselines. The results show that our ASPER model consistently outperforms the baselines.<\/jats:p>","DOI":"10.1017\/s1471068423000297","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T09:19:26Z","timestamp":1690276766000},"page":"765-781","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":2,"title":["ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction"],"prefix":"10.1017","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2958-9926","authenticated-orcid":false,"given":"TRUNG HOANG","family":"LE","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1350-1846","authenticated-orcid":false,"given":"HUIPING","family":"CAO","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3689-8433","authenticated-orcid":false,"given":"TRAN","family":"CAO SON","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"key":"S1471068423000297_ref5","unstructured":"Chen, M. , Zhao, Y. , He, B. , Han, Z. , Wu, B. and Yao, J. 2022. 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ACM, 41\u201348.","DOI":"10.1145\/1553374.1553380"},{"key":"S1471068423000297_ref7","first-page":"611","article-title":"Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning","volume":"4","author":"d\u2019Avila Garcez","year":"2019","journal-title":"FLAP 6"},{"key":"S1471068423000297_ref15","unstructured":"Lifschitz, V. and Turner, H. 1994. Splitting a logic program. In Proceedings of the Eleventh International Conference on Logic Programming, P. Van Hentenryck, Ed., 23\u201338."},{"key":"S1471068423000297_ref22","unstructured":"Roth, D. and Yih, W. 2004. A linear programming formulation for global inference in natural language tasks. In CoNLL. ACL, 1\u20138."},{"key":"S1471068423000297_ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.186"},{"key":"S1471068423000297_ref16","doi-asserted-by":"crossref","unstructured":"Luan, Y. , He, L. , Ostendorf, M. and Hajishirzi, H. 2018. 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IEEE, 1070\u20131075.","DOI":"10.1109\/ICDM50108.2020.00127"},{"key":"S1471068423000297_ref9","unstructured":"Gebser, M. , Kaminski, R. , Kaufmann, B. and Schaub, T. 2014. Clingo = ASP + control: Preliminary report. In Technical Communications of the 13th ICLP, vol. 14(4-5)."},{"key":"S1471068423000297_ref18","doi-asserted-by":"publisher","DOI":"10.3115\/1220835.1220855"},{"key":"S1471068423000297_ref25","doi-asserted-by":"crossref","unstructured":"Yang, Z. , Ishay, A. and Lee, J. 2020. Neurasp: Embracing neural networks into answer set programming. In IJCAI 2020, C. Bessiere, Ed. ijcai.org, 1755\u20131762.","DOI":"10.24963\/ijcai.2020\/243"},{"key":"S1471068423000297_ref11","unstructured":"Gupta, P. , Sch\u00fctze, H. and Andrassy, B. 2016. Table filling multi-task recurrent neural network for joint entity and relation extraction. In COLING. ACL, 2537\u20132547."},{"key":"S1471068423000297_ref19","doi-asserted-by":"crossref","unstructured":"Niemel\u00e4, I. 1999. Logic programming with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25, 3,4, 241\u2013273.","DOI":"10.1023\/A:1018930122475"},{"key":"S1471068423000297_ref8","unstructured":"Eberts, M. and Ulges, A. 2020. Span-based joint entity and relation extraction with transformer pre-training. In ECAI 2020 - 24th European Conference on Artificial Intelligence. Frontiers in Artificial Intelligence and Applications, vol. 325. IOS Press, 2006\u20132013."}],"container-title":["Theory and Practice of Logic Programming"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S1471068423000297","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T09:38:38Z","timestamp":1709026718000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S1471068423000297\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7]]},"references-count":27,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["S1471068423000297"],"URL":"https:\/\/doi.org\/10.1017\/s1471068423000297","relation":{},"ISSN":["1471-0684","1475-3081"],"issn-type":[{"value":"1471-0684","type":"print"},{"value":"1475-3081","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7]]},"assertion":[{"value":"\u00a9 The Author(s), 2023. Published by Cambridge University Press","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}}]}}