{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T11:50:08Z","timestamp":1774353008286,"version":"3.50.1"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100022812","name":"Aero Engine Corporation of China","doi-asserted-by":"publisher","award":["HFZL20 24CXY003"],"award-info":[{"award-number":["HFZL20 24CXY003"]}],"id":[{"id":"10.13039\/100022812","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019069","name":"Chinese Academy of Engineering","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100019069","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012130","name":"Chinese Aeronautical Establishment","doi-asserted-by":"publisher","award":["2024L039057003"],"award-info":[{"award-number":["2024L039057003"]}],"id":[{"id":"10.13039\/501100012130","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Web Semantics"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.websem.2026.100879","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:36:56Z","timestamp":1772728616000},"page":"100879","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Leverage Knowledge Graph and Large Language Model for law article recommendation: A case study of Chinese criminal law"],"prefix":"10.1016","volume":"89","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4174-1173","authenticated-orcid":false,"given":"Yongming","family":"Chen","sequence":"first","affiliation":[]},{"given":"Miner","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Ye","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Juan","family":"Pei","sequence":"additional","affiliation":[]},{"given":"Siyu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yifan","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3238-5406","authenticated-orcid":false,"given":"Songan","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.websem.2026.100879_b1","article-title":"Court performance around the world: a comparative perspective","author":"Dakolias","year":"2014","journal-title":"Yale Hum. Rights Dev. Law J."},{"issue":"1","key":"10.1016\/j.websem.2026.100879_b2","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1111\/sjpe.12357","article-title":"Judicial efficiency and economic growth: Evidence based on European Union data","volume":"71","author":"Kapopoulos","year":"2024","journal-title":"Scott. J. Political Econ."},{"key":"10.1016\/j.websem.2026.100879_b3","series-title":"Courts, Justice, and Efficiency: A Socio-Legal Study of Economic Rationality in Adjudication","author":"Fix-Fierro","year":"2003"},{"key":"10.1016\/j.websem.2026.100879_b4","series-title":"The supreme People\u2019s court released major data on judicial work in the first half of 2024","author":"Han","year":"2024"},{"key":"10.1016\/j.websem.2026.100879_b5","series-title":"The construction of the rule of law requires the empowerment of science and technology","author":"Wu","year":"2024"},{"issue":"8","key":"10.1016\/j.websem.2026.100879_b6","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.websem.2026.100879_b7","doi-asserted-by":"crossref","unstructured":"Yoon Kim, Convolutional neural networks for sentence classification, in: Proceedings of EMNLP, 2014.","DOI":"10.3115\/v1\/D14-1181"},{"key":"10.1016\/j.websem.2026.100879_b8","article-title":"Learning phrase representations using rnn encoder-decoder for statistical machine translation","author":"Cho","year":"2014","journal-title":"Comput. Sci."},{"key":"10.1016\/j.websem.2026.100879_b9","unstructured":"Siwei Lai, et al., Recurrent convolutional neural networks for text classification, in: Proceedings of AAAI, Vol. 333, 2015, pp. 2267\u20132273."},{"key":"10.1016\/j.websem.2026.100879_b10","doi-asserted-by":"crossref","unstructured":"Zichao Yang, et al., Hierarchical attention networks for document classification, in: Proceedings of NAACL, 2016, pp. 1480\u20131489.","DOI":"10.18653\/v1\/N16-1174"},{"key":"10.1016\/j.websem.2026.100879_b11","doi-asserted-by":"crossref","unstructured":"Rie Johnson, Tong Zhang, Deep pyramid convolutional neural networks for text categorization, in: Proceedings of ACL, Vol. 1, 2017, pp. 562\u2013570.","DOI":"10.18653\/v1\/P17-1052"},{"issue":"4","key":"10.1016\/j.websem.2026.100879_b12","first-page":"413","article-title":"Large language models for automated q&a involving legal documents: a survey on algorithms, frameworks and applications","volume":"20","author":"Yang","year":"2024","journal-title":"Int. J. Web Inf. Syst."},{"key":"10.1016\/j.websem.2026.100879_b13","series-title":"Leveraging large language models for relevance judgments in legal case retrieval","author":"Ma","year":"2024"},{"key":"10.1016\/j.websem.2026.100879_b14","series-title":"Findings of EMNLP 2023","first-page":"2206","article-title":"A comprehensive evaluation of large language models on legal judgment prediction","author":"Shui","year":"2023"},{"issue":"1","key":"10.1016\/j.websem.2026.100879_b15","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1093\/jla\/laae003","article-title":"Large legal fictions: Profiling legal hallucinations in large language models","volume":"16","author":"Dahl","year":"2024","journal-title":"J. Leg. Anal."},{"issue":"2","key":"10.1016\/j.websem.2026.100879_b16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3703155","article-title":"A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions","volume":"43","author":"Huang","year":"2025","journal-title":"ACM Trans. Inf. Syst.","ISSN":"https:\/\/id.crossref.org\/issn\/1558-2868","issn-type":"print"},{"key":"10.1016\/j.websem.2026.100879_b17","article-title":"Survey and analysis of hallucinations in large language models: attribution to prompting strategies or model behavior","volume":"8","author":"Dang","year":"2025","journal-title":"Front. Artif. Intell."},{"key":"10.1016\/j.websem.2026.100879_b18","series-title":"Chain-of-thought prompting elicits reasoning in large language models","author":"Wei","year":"2023"},{"key":"10.1016\/j.websem.2026.100879_b19","series-title":"Self-consistency improves chain of thought reasoning in language models","author":"Wang","year":"2023"},{"key":"10.1016\/j.websem.2026.100879_b20","series-title":"Findings of the Association for Computational Linguistics","first-page":"3563","article-title":"Chain-of-verification reduces hallucination in large language models","author":"Dhuliawala","year":"2024"},{"key":"10.1016\/j.websem.2026.100879_b21","doi-asserted-by":"crossref","unstructured":"Potsawee Manakul, Adian Liusie, Mark Gales, Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models, in: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023, pp. 9004\u20139017.","DOI":"10.18653\/v1\/2023.emnlp-main.557"},{"key":"10.1016\/j.websem.2026.100879_b22","unstructured":"Patrick Lewis, et al., Retrieval-augmented generation for knowledge-intensive NLP, in: Proceedings of NeurIPS, 2020, pp. 21320\u201321336."},{"key":"10.1016\/j.websem.2026.100879_b23","series-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"3417","article-title":"Recent advances in retrieval-augmented text generation","author":"Cai","year":"2022"},{"key":"10.1016\/j.websem.2026.100879_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.jss.2024.111982","article-title":"RRGcode: Deep hierarchical search-based code generation","volume":"211","author":"Gou","year":"2024","journal-title":"J. Syst. Softw."},{"issue":"4","key":"10.1016\/j.websem.2026.100879_b25","doi-asserted-by":"crossref","first-page":"4579","DOI":"10.1109\/TIV.2024.3396450","article-title":"VistaRAG: Toward safe and trustworthy autonomous driving through retrieval-augmented generation","volume":"9","author":"Dai","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"issue":"1","key":"10.1016\/j.websem.2026.100879_b26","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MIS.2023.3345591","article-title":"The rise and design of enterprise large language models","volume":"39","author":"O\u2019Leary","year":"2024","journal-title":"IEEE Intell. Syst."},{"issue":"1","key":"10.1016\/j.websem.2026.100879_b27","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/S0306-4573(02)00021-3","article-title":"An information-theoretic perspective of tf\u2013idf measures","volume":"39","author":"Aizawa","year":"2003","journal-title":"Inf. Process. Manage."},{"issue":"4","key":"10.1016\/j.websem.2026.100879_b28","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1561\/1500000019","article-title":"The probabilistic relevance framework: BM25 and beyond","volume":"3","author":"Robertson","year":"2009","journal-title":"Found. Trends Inf. Retr."},{"key":"10.1016\/j.websem.2026.100879_b29","series-title":"International Conference on Machine Learning","first-page":"2206","article-title":"Improving language models by retrieving from trillions of tokens","author":"Borgeaud","year":"2022"},{"issue":"1","key":"10.1016\/j.websem.2026.100879_b30","first-page":"17","article-title":"Knowledge graphs: introduction, history and, perspectives","volume":"43","author":"Chaudhri","year":"2022","journal-title":"AI Mag."},{"issue":"7","key":"10.1016\/j.websem.2026.100879_b31","doi-asserted-by":"crossref","first-page":"3580","DOI":"10.1109\/TKDE.2024.3352100","article-title":"Unifying large language models and knowledge graphs: A roadmap","volume":"36","author":"Pan","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.websem.2026.100879_b32","series-title":"From local to global: A graph RAG approach to query-focused summarization","author":"Edge","year":"2024"},{"key":"10.1016\/j.websem.2026.100879_b33","series-title":"LightRAG: Simple and fast retrieval-augmented generation","author":"Guo","year":"2024"},{"key":"10.1016\/j.websem.2026.100879_b34","unstructured":"Jacob Devlin, Chang Ming-wei, Kenton Lee, et al., BERT: pre-training of deep bidirectional transformers for language understanding, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019, pp. 4171\u20134186."},{"key":"10.1016\/j.websem.2026.100879_b35","doi-asserted-by":"crossref","unstructured":"Ilias Chalkidis, et al., LEGAL-BERT: The muppets straight out of law school, in: Proceedings of the Findings of EMNLP, 2020, pp. 2898\u20132904.","DOI":"10.18653\/v1\/2020.findings-emnlp.261"},{"key":"10.1016\/j.websem.2026.100879_b36","doi-asserted-by":"crossref","unstructured":"Ilias Chalkidis, Ion Androutsopoulos, Nikolaos Aletras, Neural Legal Judgment Prediction in English, in: Proceedings of the 57th Annual Meeting of the ACL, 2019, pp. 4317\u20134323.","DOI":"10.18653\/v1\/P19-1424"},{"key":"10.1016\/j.websem.2026.100879_b37","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.93","article-title":"Predicting judicial decisions of the European court of human rights: a natural language processing perspective","volume":"2","author":"Aletras","year":"2016","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.websem.2026.100879_b38","series-title":"The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3\u20137, 2018, Proceedings","first-page":"593","article-title":"Modeling relational data with graph convolutional networks","volume":"Vol. 15","author":"Schlichtkrull","year":"2018"},{"key":"10.1016\/j.websem.2026.100879_b39","series-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"10.1016\/j.websem.2026.100879_b40","series-title":"Embedding entities and relations for learning and inference in knowledge bases","author":"Yang","year":"2014"},{"key":"10.1016\/j.websem.2026.100879_b41","series-title":"Amendment XI to the criminal law of the People\u2019s Republic of China","author":"Standing Committee of the National People\u2019s Congress","year":"2020"},{"key":"10.1016\/j.websem.2026.100879_b42","series-title":"China judgments online","author":"Supreme People\u2019s Court of the People\u2019s Republic of China","year":"2023"},{"key":"10.1016\/j.websem.2026.100879_b43","series-title":"ChatGPT (4.0)","author":"OpenAI","year":"2023"},{"key":"10.1016\/j.websem.2026.100879_b44","series-title":"The llama 3 herd of models","author":"Grattafiori","year":"2024"},{"key":"10.1016\/j.websem.2026.100879_b45","first-page":"143","article-title":"Legal knowledge extraction for knowledge graph based question-answering","volume":"334","author":"Sovrano","year":"2020","journal-title":"Frontiers Artificial Intelligence Appl."},{"issue":"4","key":"10.1016\/j.websem.2026.100879_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104082","article-title":"An LLM-assisted ETL pipeline to build a high-quality knowledge graph of the Italian legislation","volume":"62","author":"Colombo","year":"2025","journal-title":"Inf. Process. Manage."}],"container-title":["Journal of Web Semantics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1570826826000053?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1570826826000053?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T10:00:16Z","timestamp":1774346416000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1570826826000053"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":46,"alternative-id":["S1570826826000053"],"URL":"https:\/\/doi.org\/10.1016\/j.websem.2026.100879","relation":{},"ISSN":["1570-8268"],"issn-type":[{"value":"1570-8268","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Leverage Knowledge Graph and Large Language Model for law article recommendation: A case study of Chinese criminal law","name":"articletitle","label":"Article Title"},{"value":"Journal of Web Semantics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.websem.2026.100879","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"100879"}}