{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T02:01:56Z","timestamp":1780020116836,"version":"3.53.1"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.knosys.2026.116177","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:31:42Z","timestamp":1778859102000},"page":"116177","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Scaffolding thought: Imposing logical structure on LLMs with knowledge graphs for counterfactual generation"],"prefix":"10.1016","volume":"346","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6870-5678","authenticated-orcid":false,"given":"Jiasheng","family":"Si","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3078-1964","authenticated-orcid":false,"given":"Yingjie","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeqing","family":"Teng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoqun","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenpeng","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoming","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deyu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"6380","key":"10.1016\/j.knosys.2026.116177_b1","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1126\/science.aap9559","article-title":"The spread of true and false news online","volume":"359","author":"Vosoughi","year":"2018","journal-title":"Science"},{"key":"10.1016\/j.knosys.2026.116177_b2","series-title":"Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"2117","article-title":"BRENDA: Browser extension for fake news detection","author":"Botnevik","year":"2020"},{"key":"10.1016\/j.knosys.2026.116177_b3","series-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics","first-page":"6981","article-title":"Fact-checking complex claims with program-guided reasoning","author":"Pan","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b4","series-title":"Proceedings of the First Workshop on Fact Extraction and VERIfication","first-page":"85","article-title":"Where is your evidence: Improving fact-checking by justification modeling","author":"Alhindi","year":"2018"},{"key":"10.1016\/j.knosys.2026.116177_b5","series-title":"Proceedings of the International Joint Conference on Artificial Intelligence","first-page":"3892","article-title":"Multi-hop fact checking of political claims","author":"Ostrowski","year":"2021"},{"key":"10.1016\/j.knosys.2026.116177_b6","series-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","first-page":"7534","article-title":"Fact or fiction: Verifying scientific claims","author":"Wadden","year":"2020"},{"key":"10.1016\/j.knosys.2026.116177_b7","series-title":"Proceedings of Findings of the Association for Computational Linguistics: NAACL","first-page":"61","article-title":"MultiVerS: Improving scientific claim verification with weak supervision and full-document context","author":"Wadden","year":"2022"},{"key":"10.1016\/j.knosys.2026.116177_b8","series-title":"Proceedings of Findings of the Association for Computational Linguistics: EMNLP 2021","first-page":"3499","article-title":"Evidence-based fact-checking of health-related claims","author":"Sarrouti","year":"2021"},{"key":"10.1016\/j.knosys.2026.116177_b9","series-title":"Findings of the Association for Computational Linguistics: ACL 2023","first-page":"14114","article-title":"Check-COVID: Fact-checking COVID-19 news claims with scientific evidence","author":"Wang","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b10","series-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","first-page":"107","article-title":"Annotation artifacts in natural language inference data","author":"Gururangan","year":"2018"},{"key":"10.1016\/j.knosys.2026.116177_b11","series-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing","first-page":"13377","article-title":"EXPLAIN, EDIT, GENERATE: Rationale-sensitive counterfactual data augmentation for multi-hop fact verification","author":"Zhu","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b12","series-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics","first-page":"5514","article-title":"DISCO: Distilling counterfactuals with large language models","author":"Chen","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b13","series-title":"International Conference on Learning Representations","article-title":"Learning the difference that makes a difference with counterfactually-augmented data","author":"Kaushik","year":"2020"},{"key":"10.1016\/j.knosys.2026.116177_b14","series-title":"Proceedings of Findings of the Association for Computational Linguistics: EMNLP","first-page":"1307","article-title":"Evaluating models\u2019 local decision boundaries via contrast sets","author":"Gardner","year":"2020"},{"key":"10.1016\/j.knosys.2026.116177_b15","doi-asserted-by":"crossref","unstructured":"Z. Wang, A. Culotta, Robustness to spurious correlations in text classification via automatically generated counterfactuals, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 14024\u201314031.","DOI":"10.1609\/aaai.v35i16.17651"},{"key":"10.1016\/j.knosys.2026.116177_b16","series-title":"Proceedings of the Annual Meeting of the Association for Computational Linguistics and International Joint Conference on Natural Language Processing","first-page":"306","article-title":"Exploring the efficacy of automatically generated counterfactuals for sentiment analysis","author":"Yang","year":"2021"},{"key":"10.1016\/j.knosys.2026.116177_b17","series-title":"Proceedings of the ACM International Conference on Information & Knowledge Management","first-page":"3181","article-title":"CrossAug: A contrastive data augmentation method for debiasing fact verification models","author":"Lee","year":"2021"},{"key":"10.1016\/j.knosys.2026.116177_b18","series-title":"Proceedings of Findings of the Association for Computational Linguistics: EMNLP","first-page":"2964","article-title":"CORE: A retrieve-then-edit framework for counterfactual data generation","author":"Dixit","year":"2022"},{"key":"10.1016\/j.knosys.2026.116177_b19","series-title":"Proceedings of the Sixth Workshop on Online Abuse and Harms","first-page":"209","article-title":"Flexible text generation for counterfactual fairness probing","author":"Fryer","year":"2022"},{"key":"10.1016\/j.knosys.2026.116177_b20","series-title":"Large language models as counterfactual generator: Strengths and weaknesses","author":"Li","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b21","series-title":"CATfOOD: Counterfactual augmented training for improving out-of-domain performance and calibration","author":"Sachdeva","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b22","series-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing","first-page":"10480","article-title":"People make better edits: Measuring the efficacy of LLM-generated counterfactually augmented data for harmful language detection","author":"Sen","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b23","series-title":"Proceedings of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation","first-page":"13201","article-title":"Prompting large language models for counterfactual generation: An empirical study","author":"Li","year":"2024"},{"key":"10.1016\/j.knosys.2026.116177_b24","series-title":"Zero-shot LLM-guided counterfactual generation for text","author":"Bhattacharjee","year":"2024"},{"key":"10.1016\/j.knosys.2026.116177_b25","series-title":"ERA-CoT: Improving chain-of-thought through entity relationship analysis","author":"Liu","year":"2024"},{"issue":"12","key":"10.1016\/j.knosys.2026.116177_b26","doi-asserted-by":"crossref","first-page":"8825","DOI":"10.1109\/TPAMI.2021.3124805","article-title":"Bringing light into the dark: A large-scale evaluation of knowledge graph embedding models under a unified framework","volume":"44","author":"Ali","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116177_b27","series-title":"Reasoning over different types of knowledge graphs: Static, temporal and multi-modal","author":"Liang","year":"2022"},{"key":"10.1016\/j.knosys.2026.116177_b28","series-title":"Think-on-graph: Deep and responsible reasoning of large language model with knowledge graph","author":"Sun","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b29","unstructured":"S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K.R. Narasimhan, Y. Cao, ReAct: Synergizing Reasoning and Acting in Language Models, in: The Eleventh International Conference on Learning Representations, 2023."},{"key":"10.1016\/j.knosys.2026.116177_b30","series-title":"Consistent multi-granular rationale extraction for explainable multi-hop fact verification","author":"Si","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b31","doi-asserted-by":"crossref","unstructured":"J. Si, Y. Zhu, D. Zhou, Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2023.","DOI":"10.1609\/aaai.v37i11.26591"},{"key":"10.1016\/j.knosys.2026.116177_b32","series-title":"Factcheck-GPT: End-to-end fine-grained document-level fact-checking and correction of LLM output","author":"Wang","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b33","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1162\/tacl_a_00486","article-title":"Fact checking with insufficient evidence","volume":"10","author":"Atanasova","year":"2022","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"10.1016\/j.knosys.2026.116177_b34","series-title":"Findings of the Association for Computational Linguistics: EMNLP 2023","first-page":"6288","article-title":"Explainable claim verification via knowledge-grounded reasoning with large language models","author":"Wang","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b35","doi-asserted-by":"crossref","unstructured":"Y. Jiang, S. Bordia, Z. Zhong, C. Dognin, M. Singh, M. Bansal, HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification, in: Proceedings of Findings of the Association for Computational Linguistics: EMNLP, 2020, pp. 3441\u20133460.","DOI":"10.18653\/v1\/2020.findings-emnlp.309"},{"key":"10.1016\/j.knosys.2026.116177_b36","doi-asserted-by":"crossref","unstructured":"R. Aly, Z. Guo, M. Schlichtkrull, J. Thorne, A. Vlachos, C. Christodoulopoulos, O. Cocarascu, A. Mittal, FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information, in: Proceedings of the NeurIPS Track on Datasets and Benchmarks, 2021.","DOI":"10.18653\/v1\/2021.fever-1.1"},{"key":"10.1016\/j.knosys.2026.116177_b37","doi-asserted-by":"crossref","unstructured":"J. Eisenschlos, B. Dhingra, J. Bulian, B. B\u00f6rschinger, J. Boyd-Graber, Fool Me Twice: Entailment from Wikipedia Gamification, in: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021, pp. 352\u2013365.","DOI":"10.18653\/v1\/2021.naacl-main.32"},{"key":"10.1016\/j.knosys.2026.116177_b38","doi-asserted-by":"crossref","unstructured":"T. Schuster, A. Fisch, R. Barzilay, Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence, in: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021, pp. 624\u2013643.","DOI":"10.18653\/v1\/2021.naacl-main.52"},{"key":"10.1016\/j.knosys.2026.116177_b39","series-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b40","unstructured":"A. Zeng, X. Liu, Z. Du, Z. Wang, H. Lai, M. Ding, Z. Yang, Y. Xu, W. Zheng, X. Xia, W.L. Tam, Z. Ma, Y. Xue, J. Zhai, W. Chen, Z. Liu, P. Zhang, Y. Dong, J. Tang, GLM-130B: An Open Bilingual Pre-trained Model, in: The Eleventh International Conference on Learning Representations, 2023."},{"key":"10.1016\/j.knosys.2026.116177_b41","series-title":"Gemini: a family of highly capable multimodal models","author":"Anil","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b42","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116177_b43","series-title":"Introducing chatgpt","author":"OpenAI","year":"2022"},{"key":"10.1016\/j.knosys.2026.116177_b44","series-title":"Gpt-4 technical report","author":"OpenAI","year":"2023"},{"key":"10.1016\/j.knosys.2026.116177_b45","doi-asserted-by":"crossref","unstructured":"J. Wei, K. Zou, EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing and International Joint Conference on Natural Language Processing, 2019, pp. 6382\u20136388.","DOI":"10.18653\/v1\/D19-1670"},{"key":"10.1016\/j.knosys.2026.116177_b46","doi-asserted-by":"crossref","unstructured":"T. Wu, M.T. Ribeiro, J. Heer, D. Weld, Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models, in: Proceedings of the Annual Meeting of the Association for Computational Linguistics and International Joint Conference on Natural Language Processing, 2021, pp. 6707\u20136723.","DOI":"10.18653\/v1\/2021.acl-long.523"},{"key":"10.1016\/j.knosys.2026.116177_b47","series-title":"RoBERTa: A robustly optimized BERT pretraining approach","author":"Liu","year":"2019"},{"key":"10.1016\/j.knosys.2026.116177_b48","doi-asserted-by":"crossref","unstructured":"K. Papineni, S. Roukos, T. Ward, W.-J. Zhu, Bleu: a Method for Automatic Evaluation of Machine Translation, in: Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2002, pp. 311\u2013318.","DOI":"10.3115\/1073083.1073135"},{"key":"10.1016\/j.knosys.2026.116177_b49","doi-asserted-by":"crossref","unstructured":"W. Zhao, M. Peyrard, F. Liu, Y. Gao, C.M. Meyer, S. Eger, MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing and International Joint Conference on Natural Language Processing, 2019, pp. 563\u2013578.","DOI":"10.18653\/v1\/D19-1053"},{"key":"10.1016\/j.knosys.2026.116177_b50","doi-asserted-by":"crossref","unstructured":"T. Schuster, D. Shah, Y.J.S. Yeo, D. Roberto Filizzola Ortiz, E. Santus, R. Barzilay, Towards Debiasing Fact Verification Models, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing and International Joint Conference on Natural Language Processing, 2019, pp. 3419\u20133425.","DOI":"10.18653\/v1\/D19-1341"},{"key":"10.1016\/j.knosys.2026.116177_b51","doi-asserted-by":"crossref","unstructured":"Y. Zhu, Q. Sheng, J. Cao, S. Li, D. Wang, F. Zhuang, Generalizing to the future: Mitigating entity bias in fake news detection, in: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022, pp. 2120\u20132125.","DOI":"10.1145\/3477495.3531816"},{"key":"10.1016\/j.knosys.2026.116177_b52","doi-asserted-by":"crossref","unstructured":"J. Wu, Q. Liu, W. Xu, S. Wu, Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention, in: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022, pp. 2308\u20132313.","DOI":"10.1145\/3477495.3531850"},{"key":"10.1016\/j.knosys.2026.116177_b53","doi-asserted-by":"crossref","unstructured":"W. Xu, Q. Liu, S. Wu, L. Wang, Counterfactual Debiasing for Fact Verification, in: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, 2023, pp. 6777\u20136789.","DOI":"10.18653\/v1\/2023.acl-long.374"},{"key":"10.1016\/j.knosys.2026.116177_b54","unstructured":"B. Wang, L. Ding, Q. Zhong, X. Li, D. Tao, A Contrastive Cross-Channel Data Augmentation Framework for Aspect-Based Sentiment Analysis, in: Proceedings of the 29th International Conference on Computational Linguistics, 2022, pp. 6691\u20136704."},{"key":"10.1016\/j.knosys.2026.116177_b55","series-title":"Proceedings of the Annual Meeting of the Association for Computational Linguistics","first-page":"1670","article-title":"Retrieval-guided counterfactual generation for QA","author":"Paranjape","year":"2022"},{"key":"10.1016\/j.knosys.2026.116177_b56","series-title":"Proceedings of the Annual Meeting of the Association for Computational Linguistics","first-page":"650","article-title":"Breaking NLI systems with sentences that require simple lexical inferences","author":"Glockner","year":"2018"},{"key":"10.1016\/j.knosys.2026.116177_b57","doi-asserted-by":"crossref","unstructured":"A. Bhattacharjee, R. Moraffah, J. Garland, H. Liu, Zero-shot LLM-guided Counterfactual Generation: A Case Study on NLP Model Evaluation, in: 2024 IEEE International Conference on Big Data (BigData), 2024, pp. 1243\u20131248.","DOI":"10.1109\/BigData62323.2024.10825537"},{"key":"10.1016\/j.knosys.2026.116177_b58","doi-asserted-by":"crossref","unstructured":"T. Xia, L. Ding, G. Wan, Y. Zhan, B. Du, D. Tao, Improving complex reasoning over knowledge graph with logic-aware curriculum tuning, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39, 2025, pp. 12881\u201312889.","DOI":"10.1609\/aaai.v39i12.33405"},{"key":"10.1016\/j.knosys.2026.116177_b59","unstructured":"Z. Yang, Z. Du, M. Zhang, W. Du, J. Chen, Z. Duan, S. Zhao, Triples as the Key: Structuring Makes Decomposition and Verification Easier in LLM-based TableQA, in: The Thirteenth International Conference on Learning Representations, 2025."},{"issue":"17","key":"10.1016\/j.knosys.2026.116177_b60","first-page":"19560","article-title":"Tree-of-reasoning question decomposition for complex question answering with large language models","volume":"38","author":"Zhang","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116177_b61","doi-asserted-by":"crossref","unstructured":"Y. Ye, B. Hui, M. Yang, B. Li, F. Huang, Y. Li, Large Language Models are Versatile Decomposers: Decomposing Evidence and Questions for Table-based Reasoning, in: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR \u201923, 2023, pp. 174\u2013184.","DOI":"10.1145\/3539618.3591708"},{"key":"10.1016\/j.knosys.2026.116177_b62","series-title":"The Thirty-Ninth Annual Conference on Neural Information Processing Systems","article-title":"DISC: Dynamic decomposition improves LLM inference scaling","author":"Light","year":"2025"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126009032?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126009032?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T01:06:29Z","timestamp":1780016789000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126009032"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":62,"alternative-id":["S0950705126009032"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116177","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Scaffolding thought: Imposing logical structure on LLMs with knowledge graphs for counterfactual generation","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116177","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116177"}}