{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T22:24:19Z","timestamp":1779315859142,"version":"3.51.4"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71871148"],"award-info":[{"award-number":["71871148"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71971041"],"award-info":[{"award-number":["71971041"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Oper Res"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s10479-023-05650-6","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T15:02:57Z","timestamp":1698678177000},"page":"959-990","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Interpretable multi-hop knowledge reasoning for gastrointestinal disease"],"prefix":"10.1007","volume":"347","author":[{"given":"Dujuan","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Zoynul","family":"Abedin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6603-6047","authenticated-orcid":false,"given":"Sutong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunqiang","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"issue":"2","key":"5650_CR1","doi-asserted-by":"publisher","first-page":"556","DOI":"10.3390\/make4020026","volume":"4","author":"A Angerschmid","year":"2022","unstructured":"Angerschmid, A., Zhou, J., Theuermann, K., Chen, F., & Holzinger, A. (2022). Fairness and explanation in AI-informed decision making. Machine Learning and Knowledge Extraction, 4(2), 556\u2013579. https:\/\/doi.org\/10.3390\/make4020026","journal-title":"Machine Learning and Knowledge Extraction"},{"key":"5650_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107144","volume":"103","author":"L Bai","year":"2021","unstructured":"Bai, L., Yu, W., Chen, M., & Ma, X. (2021). Multi-hop reasoning over paths in temporal knowledge graphs using reinforcement learning. Applied Soft Computing, 103, 107144. https:\/\/doi.org\/10.1016\/j.asoc.2021.107144","journal-title":"Applied Soft Computing"},{"key":"5650_CR3","doi-asserted-by":"publisher","unstructured":"Bansal, T., Juan, D. C., Ravi, S., & McCallum, A. (2020). A2n: Attending to neighbors for knowledge graph inference. In ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, (pp. 4387\u20134392). https:\/\/doi.org\/10.18653\/v1\/p19-1431","DOI":"10.18653\/v1\/p19-1431"},{"key":"5650_CR4","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00423-6","author":"DW Bates","year":"2021","unstructured":"Bates, D. W., Levine, D., Syrowatka, A., Kuznetsova, M., Craig, K. J. T., Rui, A., et al. (2021). The potential of artificial intelligence to improve patient safety: A scoping review. NPJ Digit. Med. https:\/\/doi.org\/10.1038\/s41746-021-00423-6","journal-title":"NPJ Digit. Med."},{"key":"5650_CR5","doi-asserted-by":"publisher","first-page":"805386","DOI":"10.3389\/fpsyg.2022.805386","volume":"13","author":"SC Bellini-Leite","year":"2022","unstructured":"Bellini-Leite, S. C. (2022). Dual process theory: Embodied and predictive symbolic and classical. Frontiers in Psychology, 13, 805386. https:\/\/doi.org\/10.3389\/fpsyg.2022.805386","journal-title":"Frontiers in Psychology"},{"key":"5650_CR6","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.cor.2018.03.005","volume":"106","author":"S Ben\u00edtez-Pe\u00f1a","year":"2019","unstructured":"Ben\u00edtez-Pe\u00f1a, S., Blanquero, R., Carrizosa, E., & Ram\u00edrez-Cobo, P. (2019). Cost-sensitive feature selection for support vector machines. Computers and Operations Research, 106, 169\u2013178. https:\/\/doi.org\/10.1016\/j.cor.2018.03.005","journal-title":"Computers and Operations Research"},{"key":"5650_CR7","doi-asserted-by":"publisher","unstructured":"Bhatia, P., Celikkaya, B., & Khalilia, M. (2020). Joint entity extraction and assertion detection for clinical text. In ACL 2019-57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. https:\/\/doi.org\/10.18653\/v1\/p19-1091","DOI":"10.18653\/v1\/p19-1091"},{"key":"5650_CR8","doi-asserted-by":"publisher","unstructured":"Chen, X., Chen, M., Shi, W., Sun, Y., & Zaniolo, C. (2019). Embedding uncertain knowledge graphs. In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, (pp. 3363\u20133370). https:\/\/doi.org\/10.1609\/aaai.v33i01.33013363","DOI":"10.1609\/aaai.v33i01.33013363"},{"key":"5650_CR9","doi-asserted-by":"publisher","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. EMNLP 2014\u20132014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, (pp. 1724\u20131734). https:\/\/doi.org\/10.3115\/v1\/d14-1179","DOI":"10.3115\/v1\/d14-1179"},{"key":"5650_CR10","unstructured":"Cohen, W. W. (2016). TensorLog: A differentiable deductive database, (Nips). http:\/\/arxiv.org\/abs\/1605.06523"},{"key":"5650_CR11","unstructured":"Das, R., Dhuliawala, S., Zaheer, M., Vilnis, L., Durugkar, I., Krishnamurthy, A., et al. (2018). Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning. In 6th International Conference on Learning Representations, ICLR 2018-Conference Track Proceedings."},{"key":"5650_CR12","doi-asserted-by":"publisher","unstructured":"Deng, Y., Li, Y., Shen, Y., Du, N., Fan, W., Yang, M., & Lei, K. (2019). MedTruth: A semi-supervised approach to discovering knowledge condition information from multi-source medical data. In International Conference on Information and Knowledge Management, Proceedings. https:\/\/doi.org\/10.1145\/3357384.3357934","DOI":"10.1145\/3357384.3357934"},{"key":"5650_CR13","doi-asserted-by":"crossref","unstructured":"Dettmers, T., Minervini, P., Stenetorp, P., & Riedel, S. (2018). ConvE. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, (pp. 1811\u20131818).","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"5650_CR14","doi-asserted-by":"publisher","DOI":"10.1287\/isre.7.3.342","author":"JS Dhaliwal","year":"1996","unstructured":"Dhaliwal, J. S., & Benbasat, I. (1996). The use and effects of knowledge-based system explanations: Theoretical foundations and a framework for empirical evaluation. Information Systems Research. https:\/\/doi.org\/10.1287\/isre.7.3.342","journal-title":"Information Systems Research"},{"issue":"4","key":"5650_CR15","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1287\/isre.1100.0284","volume":"22","author":"A Dimoka","year":"2011","unstructured":"Dimoka, A., Pavlou, P. A., & Davis, F. D. (2011). NeuroIS: The potential of cognitive neuroscience for information systems research. Information Systems Research, 22(4), 687\u2013702. https:\/\/doi.org\/10.1287\/isre.1100.0284","journal-title":"Information Systems Research"},{"key":"5650_CR16","doi-asserted-by":"publisher","unstructured":"Ding, M., Zhou, C., Chen, Q., Yang, H., & Tang, J. (2020). Cognitive graph for multi-hop reading comprehension at scale. In ACL 2019-57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. https:\/\/doi.org\/10.18653\/v1\/p19-1259","DOI":"10.18653\/v1\/p19-1259"},{"issue":"4","key":"5650_CR17","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s42524-022-0218-0","volume":"9","author":"B Dorneanu","year":"2022","unstructured":"Dorneanu, B., Zhang, S., Ruan, H., Heshmat, M., Chen, R., Vassiliadis, V. S., & Arellano-Garcia, H. (2022). Big data and machine learning: A roadmap towards smart plants. Frontiers of Engineering Management, 9(4), 623\u2013639. https:\/\/doi.org\/10.1007\/s42524-022-0218-0","journal-title":"Frontiers of Engineering Management"},{"key":"5650_CR18","doi-asserted-by":"publisher","unstructured":"Du, N., Wang, M., Tran, L., Li, G., & Shafran, I. (2019). Learning to infer entities, properties and their relations from clinical conversations. In EMNLP-IJCNLP 2019-2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference. https:\/\/doi.org\/10.18653\/v1\/d19-1503","DOI":"10.18653\/v1\/d19-1503"},{"issue":"1\u20132","key":"5650_CR19","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1007\/s10479-019-03283-2","volume":"299","author":"P du Jardin","year":"2021","unstructured":"du Jardin, P. (2021). Forecasting bankruptcy using biclustering and neural network-based ensembles. Annals of Operations Research, 299(1\u20132), 531\u2013566. https:\/\/doi.org\/10.1007\/s10479-019-03283-2","journal-title":"Annals of Operations Research"},{"key":"5650_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2021.3104310","author":"Z Du","year":"2021","unstructured":"Du, Z., Zhou, C., Yao, J., Tu, T., Cheng, L., Yang, H., et al. (2021). CogKR: Cognitive graph for multi-hop knowledge reasoning. IEEE Transactions on Knowledge and Data Engineering. https:\/\/doi.org\/10.1109\/tkde.2021.3104310","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"4","key":"5650_CR21","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1007\/s42524-021-0169-x","volume":"8","author":"D Duan","year":"2021","unstructured":"Duan, D., Wu, X., & Si, S. (2021). Novel interpretable mechanism of neural networks based on network decoupling method. Frontiers of Engineering Management, 8(4), 572\u2013581. https:\/\/doi.org\/10.1007\/s42524-021-0169-x","journal-title":"Frontiers of Engineering Management"},{"issue":"3","key":"5650_CR22","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1016\/j.ejor.2021.06.053","volume":"297","author":"E Dumitrescu","year":"2022","unstructured":"Dumitrescu, E., Hu\u00e9, S., Hurlin, C., & Tokpavi, S. (2022). Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects. European Journal of Operational Research, 297(3), 1178\u20131192. https:\/\/doi.org\/10.1016\/j.ejor.2021.06.053","journal-title":"European Journal of Operational Research"},{"issue":"12","key":"5650_CR23","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.1038\/s41551-022-00872-8","volume":"6","author":"G Erion","year":"2022","unstructured":"Erion, G., Janizek, J. D., Hudelson, C., Utarnachitt, R. B., McCoy, A. M., Sayre, M. R., et al. (2022). A cost-aware framework for the development of AI models for healthcare applications. Nature Biomedical Engineering, 6(12), 1384\u20131398. https:\/\/doi.org\/10.1038\/s41551-022-00872-8","journal-title":"Nature Biomedical Engineering"},{"issue":"1","key":"5650_CR24","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.ejor.2018.05.068","volume":"272","author":"S Feuerriegel","year":"2019","unstructured":"Feuerriegel, S., & Gordon, J. (2019). News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions. European Journal of Operational Research, 272(1), 162\u2013175. https:\/\/doi.org\/10.1016\/j.ejor.2018.05.068","journal-title":"European Journal of Operational Research"},{"issue":"1","key":"5650_CR25","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1111\/den.13974","volume":"34","author":"JR Glissen Brown","year":"2022","unstructured":"Glissen Brown, J. R., Waljee, A. K., Mori, Y., Sharma, P., & Berzin, T. M. (2022). Charting a path forward for clinical research in artificial intelligence and gastroenterology. Digestive Endoscopy, 34(1), 4\u201312. https:\/\/doi.org\/10.1111\/den.13974","journal-title":"Digestive Endoscopy"},{"issue":"21","key":"5650_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/cancers13215494","volume":"13","author":"H Goyal","year":"2021","unstructured":"Goyal, H., Sherazi, S. A. A., Mann, R., Gandhi, Z., Perisetti, A., Aziz, M., et al. (2021). Scope of artificial intelligence in gastrointestinal oncology. Cancers, 13(21), 1\u201323. https:\/\/doi.org\/10.3390\/cancers13215494","journal-title":"Cancers"},{"key":"5650_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-022-04578-7","author":"N Gradojevic","year":"2022","unstructured":"Gradojevic, N., & Kukolj, D. (2022). Unlocking the black box: Non-parametric option pricing before and during COVID-19. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-022-04578-7","journal-title":"Annals of Operations Research"},{"issue":"10","key":"5650_CR28","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2017","unstructured":"Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017). LSTM: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222\u20132232. https:\/\/doi.org\/10.1109\/TNNLS.2016.2582924","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"5650_CR29","doi-asserted-by":"publisher","DOI":"10.2307\/249487","author":"S Gregor","year":"1999","unstructured":"Gregor, S., & Benbasat, I. (1999). Explanations from intelligent systems: Theoretical foundations and implications for practice. MIS Quarterly Management Information Systems. https:\/\/doi.org\/10.2307\/249487","journal-title":"MIS Quarterly Management Information Systems"},{"key":"5650_CR30","doi-asserted-by":"publisher","unstructured":"Hildebrandt, M., Serna, J. A. Q., Ma, Y., Ringsquandl, M., Joblin, M., & Tresp, V. (2020). Reasoning on knowledge graphs with debate dynamics. In AAAI 2020-34th AAAI Conference on Artificial Intelligence, (pp. 4123\u20134131). https:\/\/doi.org\/10.1609\/aaai.v34i04.6600","DOI":"10.1609\/aaai.v34i04.6600"},{"key":"5650_CR31","doi-asserted-by":"publisher","first-page":"102166","DOI":"10.1016\/j.techsoc.2022.102166","volume":"72","author":"MT Ho","year":"2023","unstructured":"Ho, M. T., Le, N. T. B., Mantello, P., Ho, M. T., & Ghotbi, N. (2023). Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: A cross-sectional survey of clinic visitors\u2019 attitude. Technology in Society, 72, 102166. https:\/\/doi.org\/10.1016\/j.techsoc.2022.102166","journal-title":"Technology in Society"},{"key":"5650_CR32","doi-asserted-by":"publisher","unstructured":"Holzinger, A. (2021). The Next Frontier: AI We Can Really Trust. Communications in Computer and Information Science (Vol. 1524 CCIS). Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-93736-2_33","DOI":"10.1007\/978-3-030-93736-2_33"},{"issue":"3","key":"5650_CR33","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1016\/j.ejor.2018.11.072","volume":"284","author":"S H\u00f6ppner","year":"2020","unstructured":"H\u00f6ppner, S., Stripling, E., Baesens, B., Broucke, S., & vanden, & Verdonck, T. (2020). Profit driven decision trees for churn prediction. European Journal of Operational Research, 284(3), 920\u2013933. https:\/\/doi.org\/10.1016\/j.ejor.2018.11.072","journal-title":"European Journal of Operational Research"},{"key":"5650_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-021-04492-4","author":"RK Jana","year":"2022","unstructured":"Jana, R. K., & Ghosh, I. (2022). A residual driven ensemble machine learning approach for forecasting natural gas prices: analyses for pre-and during-COVID-19 phases. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-021-04492-4","journal-title":"Annals of Operations Research"},{"issue":"1","key":"5650_CR35","doi-asserted-by":"publisher","first-page":"371","DOI":"10.25300\/MISQ\/2021\/1578","volume":"45","author":"GC Kane","year":"2021","unstructured":"Kane, G. C., Young, A. G., Majchrzak, A., & Ransbotham, S. (2021). Avoiding an oppressive future of machine learning: A design theory for emancipatory assistants. MIS Quarterly Management Information Systems, 45(1), 371\u2013396. https:\/\/doi.org\/10.25300\/MISQ\/2021\/1578","journal-title":"MIS Quarterly Management Information Systems"},{"issue":"1\u20132","key":"5650_CR36","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10479-018-2891-2","volume":"276","author":"A Kocheturov","year":"2019","unstructured":"Kocheturov, A., Pardalos, P. M., & Karakitsiou, A. (2019). Massive datasets and machine learning for computational biomedicine: Trends and challenges. Annals of Operations Research, 276(1\u20132), 5\u201334. https:\/\/doi.org\/10.1007\/s10479-018-2891-2","journal-title":"Annals of Operations Research"},{"key":"5650_CR37","doi-asserted-by":"publisher","unstructured":"Lin, X. V., Socher, R., & Xiong, C. (2018). Multi-hop knowledge graph reasoning with reward shaping. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, (pp. 3243\u20133253). https:\/\/doi.org\/10.18653\/v1\/d18-1362","DOI":"10.18653\/v1\/d18-1362"},{"key":"5650_CR38","doi-asserted-by":"publisher","unstructured":"Lin, X., Quan, Z., Wang, Z. J., Ma, T., & Zeng, X. (2020). KGNN: Knowledge graph neural network for drug-drug interaction prediction. In IJCAI International Joint Conference on Artificial Intelligence, 2021-Jan, (pp. 2739\u20132745). https:\/\/doi.org\/10.24963\/ijcai.2020\/380","DOI":"10.24963\/ijcai.2020\/380"},{"key":"5650_CR39","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-021-04416-2","author":"Z Lipai","year":"2021","unstructured":"Lipai, Z., Xiqiang, X., & Mengyuan, L. (2021). Corporate governance reform in the era of artificial intelligence: research overview and prospects based on knowledge graph. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-021-04416-2","journal-title":"Annals of Operations Research"},{"issue":"8","key":"5650_CR40","doi-asserted-by":"publisher","first-page":"5261","DOI":"10.1109\/TSMC.2019.2949342","volume":"51","author":"HC Liu","year":"2021","unstructured":"Liu, H. C., Xu, D. H., Duan, C. Y., & Xiong, Y. (2021). Pythagorean fuzzy petri nets for knowledge representation and reasoning in large group context. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(8), 5261\u20135271. https:\/\/doi.org\/10.1109\/TSMC.2019.2949342","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"5650_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108235","volume":"241","author":"H Liu","year":"2022","unstructured":"Liu, H., Zhou, S., Chen, C., Gao, T., Xu, J., & Shu, M. (2022). Dynamic knowledge graph reasoning based on deep reinforcement learning. Knowledge-Based Systems, 241, 108235. https:\/\/doi.org\/10.1016\/j.knosys.2022.108235","journal-title":"Knowledge-Based Systems"},{"issue":"3","key":"5650_CR42","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1287\/ISRE.2019.0911","volume":"31","author":"X Liu","year":"2020","unstructured":"Liu, X., Alan Wang, G., Fan, W., & Zhang, Z. (2020). Finding useful solutions in online knowledge communities: A theory-driven design and multilevel analysis. Information Systems Research, 31(3), 731\u2013752. https:\/\/doi.org\/10.1287\/ISRE.2019.0911","journal-title":"Information Systems Research"},{"key":"5650_CR43","doi-asserted-by":"publisher","unstructured":"Lv, X., Gu, Y., Han, X., Hou, L., Li, J., & Liu, Z. (2019). Adapting meta knowledge graph information for multi-hop reasoning over few-shot relations. In EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, (pp. 3376\u20133381). https:\/\/doi.org\/10.18653\/v1\/d19-1334","DOI":"10.18653\/v1\/d19-1334"},{"key":"5650_CR44","doi-asserted-by":"publisher","DOI":"10.1287\/isre.2022.1184","author":"M Lysyakov","year":"2022","unstructured":"Lysyakov, M., & Viswanathan, S. (2022). Threatened by AI: Analyzing users\u2019 responses to the introduction of AI in a crowd-sourcing platform. Information Systems Research. https:\/\/doi.org\/10.1287\/isre.2022.1184","journal-title":"Information Systems Research"},{"key":"5650_CR45","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.neunet.2021.06.008","volume":"143","author":"T Ma","year":"2021","unstructured":"Ma, T., Lv, S., Huang, L., & Hu, S. (2021). HiAM: A Hierarchical Attention based Model for knowledge graph multi-hop reasoning. Neural Networks, 143, 261\u2013270. https:\/\/doi.org\/10.1016\/j.neunet.2021.06.008","journal-title":"Neural Networks"},{"issue":"1","key":"5650_CR46","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s10796-020-10078-5","volume":"24","author":"S Mertens","year":"2022","unstructured":"Mertens, S., Gailly, F., Van Sassenbroeck, D., & Poels, G. (2022). Integrated declarative process and decision discovery of the emergency care process. Information Systems Frontiers, 24(1), 85\u2013114. https:\/\/doi.org\/10.1007\/s10796-020-10078-5","journal-title":"Information Systems Frontiers"},{"issue":"June","key":"5650_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2020.113346","volume":"136","author":"L Nizzoli","year":"2020","unstructured":"Nizzoli, L., Avvenuti, M., Tesconi, M., & Cresci, S. (2020). Geo-semantic-parsing: AI-powered geoparsing by traversing semantic knowledge graphs. Decision Support Systems, 136(June), 113346. https:\/\/doi.org\/10.1016\/j.dss.2020.113346","journal-title":"Decision Support Systems"},{"issue":"11","key":"5650_CR48","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.1002\/asi.24198","volume":"70","author":"MS Park","year":"2019","unstructured":"Park, M. S. (2019). Understanding characteristics of semantic associations in health consumer generated knowledge representation in social media. Journal of the Association for Information Science and Technology, 70(11), 1210\u20131222. https:\/\/doi.org\/10.1002\/asi.24198","journal-title":"Journal of the Association for Information Science and Technology"},{"issue":"2","key":"5650_CR49","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1053\/j.gastro.2021.10.017","volume":"162","author":"AF Peery","year":"2022","unstructured":"Peery, A. F., Crockett, S. D., Murphy, C. C., Jensen, E. T., Kim, H. P., Egberg, M. D., et al. (2022). Burden and cost of gastrointestinal, liver, and pancreatic diseases in the United States: Update 2021. Gastroenterology, 162(2), 621\u2013644. https:\/\/doi.org\/10.1053\/j.gastro.2021.10.017","journal-title":"Gastroenterology"},{"issue":"July","key":"5650_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2019.113115","volume":"125","author":"J Ren","year":"2019","unstructured":"Ren, J., Long, J., & Xu, Z. (2019). Financial news recommendation based on graph embeddings. Decision Support Systems, 125(July), 113115. https:\/\/doi.org\/10.1016\/j.dss.2019.113115","journal-title":"Decision Support Systems"},{"key":"5650_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-022-04692-6","author":"M Repetto","year":"2022","unstructured":"Repetto, M. (2022). Multicriteria interpretability driven deep learning. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-022-04692-6","journal-title":"Annals of Operations Research"},{"issue":"1","key":"5650_CR52","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21708","volume":"72","author":"RL Siegel","year":"2022","unstructured":"Siegel, R. L., Miller, K. D., Fuchs, H. E., & Jemal, A. (2022). Cancer statistics, 2022. CA A Cancer Journal for Clinicians, 72(1), 7\u201333. https:\/\/doi.org\/10.3322\/caac.21708","journal-title":"CA A Cancer Journal for Clinicians"},{"issue":"8","key":"5650_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/cancers14081906","volume":"14","author":"MC Silva","year":"2022","unstructured":"Silva, M. C., Eug\u00e9nio, P., Faria, D., & Pesquita, C. (2022). Ontologies and knowledge graphs in oncology research. Cancers, 14(8), 1\u201327. https:\/\/doi.org\/10.3390\/cancers14081906","journal-title":"Cancers"},{"issue":"11","key":"5650_CR54","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1145\/3458652","volume":"64","author":"K St\u00f6ger","year":"2021","unstructured":"St\u00f6ger, K., Schneeberger, D., & Holzinger, A. (2021). Medical artificial intelligence: The European legal perspective. Communications of the ACM, 64(11), 34\u201336.","journal-title":"Communications of the ACM"},{"key":"5650_CR55","doi-asserted-by":"publisher","unstructured":"Stoica, G., Stretcu, O., Platanios, E. A., Mitchell, T. M., & P\u00f3czos, B. (2020). Contextual parameter generation for knowledge graph link prediction. In AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, (2008), (pp. 3000\u20133008). https:\/\/doi.org\/10.1609\/aaai.v34i03.5693","DOI":"10.1609\/aaai.v34i03.5693"},{"key":"5650_CR56","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3154792","author":"X Su","year":"2022","unstructured":"Su, X., You, Z. H., Huang, D. S., Wang, L., Wong, L., Ji, B., & Zhao, B. (2022). Biomedical knowledge graph embedding with capsule network for multi-label drug-drug interaction prediction. IEEE Transactions on Knowledge and Data Engineering. https:\/\/doi.org\/10.1109\/TKDE.2022.3154792","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"2","key":"5650_CR57","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s42524-020-0126-0","volume":"8","author":"L Tang","year":"2021","unstructured":"Tang, L., & Meng, Y. (2021). Data analytics and optimization for smart industry. Frontiers of Engineering Management, 8(2), 157\u2013171. https:\/\/doi.org\/10.1007\/s42524-020-0126-0","journal-title":"Frontiers of Engineering Management"},{"key":"5650_CR58","unstructured":"Tavares, Z., Burroni, J., Minasyan, E., Lezama, A. S., & Ranganath, R. (2019). Predicate exchange: Inference with declarative knowledge. In 36th International Conference on Machine Learning, ICML 2019, (pp. 10792\u201310801)."},{"key":"5650_CR59","doi-asserted-by":"publisher","DOI":"10.1126\/science.1192788","author":"JB Tenenbaum","year":"2011","unstructured":"Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science. https:\/\/doi.org\/10.1126\/science.1192788","journal-title":"Science"},{"key":"5650_CR60","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Ciaussier, E., & Bouchard, G. (2016). Complex embeddings for simple link prediction. In 33rd International Conference on Machine Learning, ICML 2016, (pp. 3021\u20133032)."},{"issue":"1","key":"5650_CR61","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1287\/isre.3.1.36","volume":"3","author":"JG Walls","year":"2001","unstructured":"Walls, J. G., & Sawy, O. A. E. I. (2001). Building an information system design theory for vigilant EIS. Information Systems Research, 3(1), 36\u201339.","journal-title":"Information Systems Research"},{"key":"5650_CR62","doi-asserted-by":"publisher","unstructured":"Wan, G., Pan, S., Gong, C., Zhou, C., & Haffari, G. (2020). Reasoning like human: Hierarchical reinforcement learning for knowledge graph reasoning. IJCAI International Joint Conference on Artificial Intelligence, 2021-Jan, (pp. 1926\u20131932). https:\/\/doi.org\/10.24963\/ijcai.2020\/267","DOI":"10.24963\/ijcai.2020\/267"},{"key":"5650_CR63","doi-asserted-by":"publisher","unstructured":"Wan, G., & Du, B. (2021). GaussianPath:A Bayesian multi-hop reasoning framework for knowledge graph reasoning. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 5B). https:\/\/doi.org\/10.1609\/aaai.v35i5.16565","DOI":"10.1609\/aaai.v35i5.16565"},{"key":"5650_CR64","doi-asserted-by":"publisher","unstructured":"Wang, Z., Lee, J., Lin, S., & Sun, H. (2020). Rationalizing medical relation prediction from corpus-level statistics, (pp. 8078\u20138092). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.719","DOI":"10.18653\/v1\/2020.acl-main.719"},{"issue":"2","key":"5650_CR65","doi-asserted-by":"publisher","first-page":"201","DOI":"10.26599\/BDMA.2022.9020021","volume":"6","author":"X Wu","year":"2023","unstructured":"Wu, X., Duan, J., Pan, Y., & Li, M. (2023). Medical knowledge graph: Data sources, construction, reasoning, and applications. Big Data Mining and Analytics, 6(2), 201\u2013217. https:\/\/doi.org\/10.26599\/BDMA.2022.9020021","journal-title":"Big Data Mining and Analytics"},{"key":"5650_CR66","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-022-05012-8","author":"H Xia","year":"2022","unstructured":"Xia, H., Weng, J., Boubaker, S., Zhang, Z., & Jasimuddin, S. M. (2022). Cross-influence of information and risk effects on the IPO market: Exploring risk disclosure with a machine learning approach. Annals of Operations Research. https:\/\/doi.org\/10.1007\/s10479-022-05012-8","journal-title":"Annals of Operations Research"},{"key":"5650_CR67","unstructured":"Xu, X., Zu, S., Gao, C., Zhang, Y., & Feng, W. (2018). Modeling attention flow on graphs, (pp. 1\u201320). http:\/\/arxiv.org\/abs\/1811.00497"},{"key":"5650_CR68","unstructured":"Yang, B., Yih, W. tau, He, X., Gao, J., & Deng, L. (2015). Embedding entities and relations for learning and inference in knowledge bases. In 3rd International Conference on Learning Representations, ICLR 2015-Conference Track Proceedings, (pp. 1\u201312)."},{"key":"5650_CR69","unstructured":"Yang, F., Yang, Z., & Cohen, W. W. (2017). Differentiable learning of logical rules for knowledge base reasoning. In Advances in Neural Information Processing Systems, 2017 (Nips), (pp. 2320\u20132329)."},{"key":"5650_CR70","doi-asserted-by":"publisher","unstructured":"Yuan, J., Gao, N., & Xiang, J. (2019). TransGate: Knowledge graph embedding with shared gate structure. In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, (pp. 3100\u20133107). https:\/\/doi.org\/10.1609\/aaai.v33i01.33013100","DOI":"10.1609\/aaai.v33i01.33013100"},{"key":"5650_CR71","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Cai, J., Zhang, Y., & Wang, J. (2020). Learning hierarchy-aware knowledge graph embeddings for link prediction. In AAAI 2020-34th AAAI Conference on Artificial Intelligence, (pp. 3065\u20133072). https:\/\/doi.org\/10.1609\/aaai.v34i03.5701","DOI":"10.1609\/aaai.v34i03.5701"},{"issue":"8","key":"5650_CR72","doi-asserted-by":"publisher","first-page":"3160","DOI":"10.1111\/poms.13743","volume":"31","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Du, Q., & Zhang, Z. (2022). A theory-driven machine learning system for financial disinformation detection. Production and Operations Management, 31(8), 3160\u20133179. https:\/\/doi.org\/10.1111\/poms.13743","journal-title":"Production and Operations Management"},{"key":"5650_CR73","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3113026","author":"X Zhao","year":"2021","unstructured":"Zhao, X., Chen, H., Xing, Z., & Miao, C. (2021). Brain-inspired search engine assistant based on knowledge graph. IEEE Transactions on Neural Networks and Learning Systems. https:\/\/doi.org\/10.1109\/TNNLS.2021.3113026","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"5650_CR74","doi-asserted-by":"publisher","unstructured":"Zhu, M., Celikkaya, B., Bhatia, P., & Reddy, C. K. (2020). LATTE: Latent type modeling for biomedical entity linking. In AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, (pp. 9757\u20139764). https:\/\/doi.org\/10.1609\/aaai.v34i05.6526","DOI":"10.1609\/aaai.v34i05.6526"},{"key":"5650_CR75","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108843","volume":"248","author":"A Zhu","year":"2022","unstructured":"Zhu, A., Ouyang, D., Liang, S., & Shao, J. (2022). Step by step: A hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning. Knowledge-Based Systems, 248, 108843. https:\/\/doi.org\/10.1016\/j.knosys.2022.108843","journal-title":"Knowledge-Based Systems"},{"issue":"4","key":"5650_CR76","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1198\/tech.2004.s242","volume":"46","author":"ER Ziegel","year":"2004","unstructured":"Ziegel, E. R. (2004). System Reliability Theory: Models, Statistical Methods, and Applications. Technometrics, 46(4), 495. https:\/\/doi.org\/10.1198\/tech.2004.s242","journal-title":"Technometrics"}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-023-05650-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10479-023-05650-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-023-05650-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T13:03:56Z","timestamp":1746709436000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10479-023-05650-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,30]]},"references-count":76,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["5650"],"URL":"https:\/\/doi.org\/10.1007\/s10479-023-05650-6","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"value":"0254-5330","type":"print"},{"value":"1572-9338","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,30]]},"assertion":[{"value":"5 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Dujuan Wang declares that she has no conflict of interest. Xinwei Wang declares that he has no conflict of interest. Mohammad Zoynul Abedin declares that he has no conflict of interest. Sutong Wang declares that he has no conflict of interest. Yunqiang Yin declares that he has no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}