{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T15:08:33Z","timestamp":1744384113676,"version":"3.37.3"},"reference-count":29,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T00:00:00Z","timestamp":1592524800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research Program of Shandong Province","award":["2018GGX101052","61973180","ZR2019MF033"],"award-info":[{"award-number":["2018GGX101052","61973180","ZR2019MF033"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2018GGX101052","61973180","ZR2019MF033"],"award-info":[{"award-number":["2018GGX101052","61973180","ZR2019MF033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["2018GGX101052","61973180","ZR2019MF033"],"award-info":[{"award-number":["2018GGX101052","61973180","ZR2019MF033"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2020,6,19]]},"abstract":"<jats:p>Chemical event evolutionary graph (CEEG) is an effective tool to perform safety analysis, early warning, and emergency disposal for chemical accidents. However, it is a complicated work to find causality among events in a CEEG. This paper presents a method to accurately extract event causality by using a neural network and structural analysis. First, we identify the events and their component elements from fault trees by natural language processing technology. Then, causality in accident events is divided into explicit causality and implicit causality. Explicit causality is obtained by analyzing the hierarchical structure relations of event nodes and the semantics of component logic gates in fault trees. By integrating internal structural features of events and semantic features of event sentences, we extract implicit causality by utilizing a bidirectional gated recurrent unit (BiGRU) neural network. An algorithm, named CEFTAR, is presented to extract causality for safety events in chemical accidents from fault trees and accident reports. Compared with the existing methods, experimental results show that our method has a higher accuracy and recall rate in extracting causality.<\/jats:p>","DOI":"10.1155\/2020\/7132072","type":"journal-article","created":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T23:31:08Z","timestamp":1592609468000},"page":"1-12","source":"Crossref","is-referenced-by-count":3,"title":["A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports"],"prefix":"10.1155","volume":"2020","author":[{"given":"Junwei","family":"Du","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Qingdao University of Science and Technology, Qicngdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanrui","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Qingdao University of Science and Technology, Qicngdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangyang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Qingdao University of Science and Technology, Qicngdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7642-5660","authenticated-orcid":true,"given":"Qiang","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Qingdao University of Science and Technology, Qicngdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.3390\/su11102846"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2015.10.008"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.psep.2019.09.003"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.7731\/kifse.2017.31.1.081"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1049\/el.2015.3409"},{"year":"2015","key":"6"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/tr.2017.2778241"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2018.2791503"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2015.03.001"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2015.04.017"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1145\/2699916"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-017-9359-x"},{"first-page":"1375","volume-title":"Event recognition oriented to emergency events and its application","year":"2019","key":"15"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2016.02.006"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-015-0906-9"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1109\/mci.2014.2307227"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1145\/3314943"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.09.066"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2961953"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.2983233"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2897580"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.04.037"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1109\/tcsi.2019.2959886"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.11.016"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.09.011"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.4018\/ijthi.2019070104"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1109\/tie.2017.2733438"},{"first-page":"1945","volume-title":"Batch renormalization: towards reducing minibatch dependence in batch-normalized models","year":"2017","key":"35"},{"issue":"2","key":"38","first-page":"280","volume":"27","year":"2016","journal-title":"Journal of Software"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2020\/7132072.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2020\/7132072.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2020\/7132072.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T23:31:12Z","timestamp":1592609472000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cin\/2020\/7132072\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,19]]},"references-count":29,"alternative-id":["7132072","7132072"],"URL":"https:\/\/doi.org\/10.1155\/2020\/7132072","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"type":"print","value":"1687-5265"},{"type":"electronic","value":"1687-5273"}],"subject":[],"published":{"date-parts":[[2020,6,19]]}}}