{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T11:24:09Z","timestamp":1772709849909,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Alma Mater Studiorum - Universit\u00e0 di Bologna"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Converting natural language text into structured, logically coherent knowledge graphs (KGs) enhances the ability to retrieve, organize, and analyze vast amounts of information at scale. This paper introduces Text2AMR2FRED, a text-to-KG pipeline that converts multilingual natural language text into logically coherent, interoperable KGs. Designed to support large-scale information retrieval and knowledge extraction, this pipeline addresses key limitations of existing semantic parsers and machine readers, including issues with logical consistency and interoperability. By adhering to Semantic Web standards, Text2AMR2FRED systematically structures text-based information and enhances it through integration with external knowledge sources, delivering enriched, semantically sound KGs ready for diverse applications. We obtain the output KGs by leveraging Abstract Meaning Representation (AMR) as an intermediate semantic parsing formalism, exploiting the progress achieved by text-to-AMR parsers employing pre-trained language models. We produce a manually validated\n                    <jats:sc>KG<\/jats:sc>\n                    s bank created by transforming a dataset of natural language sentences into KGs using Text2AMR2FRED and applying an intrinsic evaluation method that leverages Open Knowledge Extraction motifs.\n                  <\/jats:p>","DOI":"10.1007\/s10115-025-02631-y","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T09:14:56Z","timestamp":1767690896000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Text2AMR2FRED, converting text into RDF\/OWL knowledge graphs via abstract meaning representation"],"prefix":"10.1007","volume":"68","author":[{"given":"Aldo","family":"Gangemi","sequence":"first","affiliation":[]},{"given":"Arianna","family":"Graciotti","sequence":"additional","affiliation":[]},{"given":"Antonello","family":"Meloni","sequence":"additional","affiliation":[]},{"given":"Andrea Giovanni","family":"Nuzzolese","sequence":"additional","affiliation":[]},{"given":"Valentina","family":"Presutti","sequence":"additional","affiliation":[]},{"given":"Diego","family":"Reforgiato\u00a0Recupero","sequence":"additional","affiliation":[]},{"given":"Alessandro","family":"Russo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"2631_CR1","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1017\/S1351324921000395","volume":"28","author":"R Baradaran","year":"2020","unstructured":"Baradaran R, Ghiasi R, Amirkhani H (2020) A survey on machine reading comprehension systems. Nat Lang Eng 28:683\u2013732","journal-title":"Nat Lang Eng"},{"issue":"4","key":"2631_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447772","volume":"54","author":"A Hogan","year":"2021","unstructured":"Hogan A, Blomqvist E, Cochez M, D\u2019amato C, Melo G, Gutierrez C, Kirrane S, Gayo J, Navigli R, Neumaier S, Ngomo A, Polleres A, Rashid S, Rula A, Schmelzeisen L, Sequeda J, Staab S, Zimmermann A (2021) Knowledge graphs. ACM Comput Surv 54(4):1\u201337. https:\/\/doi.org\/10.1145\/3447772. (Place: New York, NY, USA Publisher: Association for Computing Machinery)","journal-title":"ACM Comput Surv"},{"issue":"5","key":"2631_CR3","doi-asserted-by":"publisher","first-page":"1989","DOI":"10.1007\/s10115-022-01826-x","volume":"65","author":"MY Jaradeh","year":"2023","unstructured":"Jaradeh MY, Singh K, Stocker M, Both A, Auer S (2023) Information extraction pipelines for knowledge graphs. Knowl Inf Syst 65(5):1989\u20132016. https:\/\/doi.org\/10.1007\/s10115-022-01826-x","journal-title":"Knowl Inf Syst"},{"key":"2631_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/J.KNOSYS.2022.109945","volume":"258","author":"D Dess\u00ec","year":"2022","unstructured":"Dess\u00ec D, Osborne F, Recupero DR, Buscaldi D, Motta E (2022) SCICERO: a deep learning and NLP approach for generating scientific knowledge graphs in the computer science domain. Knowl. Based Syst. 258:109945. https:\/\/doi.org\/10.1016\/J.KNOSYS.2022.109945","journal-title":"Knowl. Based Syst."},{"issue":"6","key":"2631_CR5","doi-asserted-by":"publisher","first-page":"873","DOI":"10.3233\/SW-160240","volume":"8","author":"A Gangemi","year":"2017","unstructured":"Gangemi A, Presutti V, Recupero DR, Nuzzolese AG, Draicchio F, Mongiov\u00ec M (2017) Semantic web machine reading with FRED. Semantic Web 8(6):873\u2013893. https:\/\/doi.org\/10.3233\/SW-160240","journal-title":"Semantic Web"},{"key":"2631_CR6","unstructured":"Banarescu L, Bonial C, Cai S, Georgescu M, Griffitt K, Hermjakob U, Knight K, Koehn P, Palmer M, Schneider N (2013) Abstract meaning representation for sembanking. In: Proc. of the 7th linguistic annotation workshop and interoperability with discourse, ACL, Sofia, Bulgaria, pp 178\u2013186. https:\/\/aclanthology.org\/W13-2322"},{"key":"2631_CR7","doi-asserted-by":"publisher","unstructured":"Wein S, Opitz J (2024) A survey of AMR applications. In: Al-Onaizan, Y., Bansal, M., Chen, Y.-N. (eds.) Proceedings of the 2024 conference on empirical methods in natural language processing, Association for Computational Linguistics, Miami, Florida, USA, pp 6856\u20136875. https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-main.390","DOI":"10.18653\/v1\/2024.emnlp-main.390"},{"issue":"14","key":"2631_CR8","first-page":"12564","volume":"35","author":"M Bevilacqua","year":"2021","unstructured":"Bevilacqua M, Blloshmi R, Navigli R (2021) One SPRING to rule them both: symmetric AMR semantic parsing and generation without a complex pipeline. Proc. AAAI Conf Artif Intell 35(14):12564\u201312573","journal-title":"Proc. AAAI Conf Artif Intell"},{"key":"2631_CR9","doi-asserted-by":"publisher","unstructured":"Zhou J, Naseem T, Fernandez\u00a0Astudillo R, Lee Y-S, Florian R, Roukos S(2021) Structure-aware fine-tuning of sequence-to-sequence transformers for transition-based AMR parsing. In: Proc. of the 2021 conference on empirical methods in natural language processing, ACL, Online and Punta Cana, Dominican Republic, pp 6279\u20136290. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.507","DOI":"10.18653\/v1\/2021.emnlp-main.507"},{"key":"2631_CR10","doi-asserted-by":"publisher","unstructured":"Blloshmi R, Tripodi R, Navigli R (2020) XL-AMR: enabling cross-lingual AMR parsing with transfer learning techniques. In: Proc. of the 2020 conference on empirical methods in natural language processing (EMNLP), pp 2487\u20132500. ACL, Online. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.195","DOI":"10.18653\/v1\/2020.emnlp-main.195"},{"key":"2631_CR11","doi-asserted-by":"publisher","unstructured":"Procopio L, Tripodi R, Navigli R (2021) SGL: speaking the graph languages of semantic parsing via multilingual translation. In: Proc. of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 325\u2013337. ACL, Online. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.30","DOI":"10.18653\/v1\/2021.naacl-main.30"},{"key":"2631_CR12","doi-asserted-by":"publisher","unstructured":"Oepen S, Abend O, Abzianidze L, Bos J, Hajic J, Hershcovich D, Li B, O\u2019Gorman T, Xue N, Zeman D (2020) MRP 2020: the second shared task on cross-framework and cross-lingual meaning representation parsing. In: Proc. of the CoNLL 2020 shared task: cross-framework meaning representation parsing, pp 1\u201322. ACL, Online. https:\/\/doi.org\/10.18653\/v1\/2020.conll-shared.1","DOI":"10.18653\/v1\/2020.conll-shared.1"},{"key":"2631_CR13","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.knosys.2016.05.023","volume":"108","author":"A Gangemi","year":"2016","unstructured":"Gangemi A, Recupero DR, Mongiov\u00ec M, Nuzzolese AG, Presutti V (2016) Identifying motifs for evaluating open knowledge extraction on the web. Knowl Based Syst 108:33\u201341. https:\/\/doi.org\/10.1016\/j.knosys.2016.05.023. (New Avenues in Knowledge Bases for Natural Language Processing)","journal-title":"Knowl Based Syst"},{"key":"2631_CR14","doi-asserted-by":"publisher","unstructured":"Lacerra C, Tripodi R, Navigli R (2021) Genesis: a generative approach to substitutes in context. In: Proc. of the 2021 conference on empirical methods in natural language processing, pp 10810\u201310823. ACL, Online and Punta Cana, Dominican Republic. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.844","DOI":"10.18653\/v1\/2021.emnlp-main.844"},{"key":"2631_CR15","doi-asserted-by":"publisher","unstructured":"Lacerra C, Pasini T, Tripodi R, Navigli R (2021) Alasca: an automated approach for large-scale lexical substitution. In: Zhou, Z.-H. (ed.) Proceedings of the thirtieth international joint conference on artificial intelligence, IJCAI-21, pp 3836\u20133842. International joint conferences on artificial intelligence organization, Montreal, Canada. https:\/\/doi.org\/10.24963\/ijcai.2021\/528 . Main Track","DOI":"10.24963\/ijcai.2021\/528"},{"issue":"4","key":"2631_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2025.104127","volume":"62","author":"S De Giorgis","year":"2025","unstructured":"De Giorgis S, Gangemi A, Russo A (2025) Neurosymbolic graph enrichment for grounded world models. Inf Process Manag 62(4):104127. https:\/\/doi.org\/10.1016\/j.ipm.2025.104127","journal-title":"Inf Process Manag"},{"key":"2631_CR17","unstructured":"Orlando R, Conia S, Faralli S, Navigli R (2022) Universal semantic annotator: the first unified API for WSD, SRL and semantic parsing. In: Calzolari, N., B\u00e9chet, F., Blache, P., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Isahara, H., Maegaard, B., Mariani, J., Mazo, H., Odijk, J., Piperidis, S. (eds.) Proceedings of the thirteenth language resources and evaluation conference, pp 2634\u20132641. European Language Resources Association, Marseille, France. https:\/\/aclanthology.org\/2022.lrec-1.282\/"},{"key":"2631_CR18","doi-asserted-by":"publisher","unstructured":"Pradhan S, Bonn J, Myers S, Conger K, O\u2019gorman T, Gung J, Wright-bettner K, Palmer M (2022) PropBank comes of Age\u2014Larger, smarter, and more diverse. In: Nastase, V., Pavlick, E., Pilehvar, M.T., Camacho-Collados, J., Raganato, A. (eds.) Proceedings of the 11th joint conference on lexical and computational semantics, pp 278\u2013288. Association for Computational Linguistics, Seattle, Washington. https:\/\/doi.org\/10.18653\/v1\/2022.starsem-1.24","DOI":"10.18653\/v1\/2022.starsem-1.24"},{"key":"2631_CR19","doi-asserted-by":"crossref","unstructured":"Gangemi A, Alam M, Asprino L, Presutti V, Recupero DR (2016) Framester: a wide coverage linguistic linked data hub. In: EKAW 2016, Springer, Bologna, Italy, pp 239\u2013254","DOI":"10.1007\/978-3-319-49004-5_16"},{"key":"2631_CR20","doi-asserted-by":"publisher","unstructured":"Wu L, Petroni F, Josifoski M, Riedel S, Zettlemoyer L (2020) Scalable zero-shot entity linking with dense entity retrieval. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp 6397\u20136407. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.519","DOI":"10.18653\/v1\/2020.emnlp-main.519"},{"key":"2631_CR21","first-page":"1517","volume":"6","author":"O Etzioni","year":"2006","unstructured":"Etzioni O, Banko M, Cafarella MJ (2006) Machine reading. AAAI 6:1517\u20131519","journal-title":"AAAI"},{"issue":"12","key":"2631_CR22","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1145\/1409360.1409378","volume":"51","author":"O Etzioni","year":"2008","unstructured":"Etzioni O, Banko M, Soderland S, Weld DS (2008) Open information extraction from the web. Commun ACM 51(12):68\u201374","journal-title":"Commun ACM"},{"key":"2631_CR23","unstructured":"Singh P, et al (2002) The public acquisition of commonsense knowledge. In: Proceedings of AAAI Spring Symposium: Acquiring (and Using) Linguistic (and World) Knowledge for Information Access, vol 3"},{"issue":"5","key":"2631_CR24","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/3191513","volume":"61","author":"T Mitchell","year":"2018","unstructured":"Mitchell T, Cohen W, Hruschka E, Talukdar P, Yang B, Betteridge J, Carlson A, Dalvi B, Gardner M, Kisiel B et al (2018) Never-ending learning. Commun ACM 61(5):103\u2013115","journal-title":"Commun ACM"},{"key":"2631_CR25","unstructured":"Cimiano P (2010) Ontology learning and population from text: algorithms, evaluation and applications vol. 27. Springer, Berlin\/Heidelberg, Germany. https:\/\/dl.acm.org\/doi\/10.5555\/1942117"},{"issue":"6","key":"2631_CR26","doi-asserted-by":"publisher","first-page":"3901","DOI":"10.1007\/s10462-019-09782-9","volume":"53","author":"FN Al-Aswadi","year":"2020","unstructured":"Al-Aswadi FN, Chan HY, Gan KH (2020) Automatic ontology construction from text: a review from shallow to deep learning trend. Artif Intell Rev 53(6):3901\u20133928. https:\/\/doi.org\/10.1007\/s10462-019-09782-9","journal-title":"Artif Intell Rev"},{"key":"2631_CR27","doi-asserted-by":"publisher","unstructured":"Cimiano P, V\u00f6lker J (2005) A framework for ontology learning and data-driven change discovery. In: Proceedings of the 10th international conference on applications of natural language to information systems (NLDB), Springer, pp 227\u2013238. https:\/\/doi.org\/10.1007\/11428817_21","DOI":"10.1007\/11428817_21"},{"key":"2631_CR28","unstructured":"Witte R, Khamis N, Rilling J (2010) Flexible ontology population from text: The OwlExporter. In: Calzolari, N., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., Tapias, D. (eds.) Proceedings of the seventh international conference on language resources and evaluation (LREC\u201910). European Language Resources Association (ELRA), Valletta, Malta. https:\/\/aclanthology.org\/L10-1633\/"},{"key":"2631_CR29","unstructured":"Tanev H, Magnini B (2006) Weakly supervised approaches for ontology population. In: Proceedings of 11th conference of the European chapter of the association for computational linguistics, pp 17\u201324"},{"key":"2631_CR30","doi-asserted-by":"publisher","first-page":"3356","DOI":"10.1016\/j.procs.2020.09.061","volume":"176","author":"J Watr\u00f3bski","year":"2020","unstructured":"Watr\u00f3bski J (2020) Ontology learning methods from text-an extensive knowledge-based approach. Procedia Comput Sci 176:3356\u20133368","journal-title":"Procedia Comput Sci"},{"key":"2631_CR31","doi-asserted-by":"crossref","unstructured":"Hearst M (1992) Automatic acquisition of hyponyms from large text corpora in proc. In: 14th international conference computational linguistics, Nantes France","DOI":"10.3115\/992133.992154"},{"key":"2631_CR32","doi-asserted-by":"crossref","unstructured":"V\u00f6lker J, Rudolph S (2008) Lexico-logical acquisition of owl dl axioms\u2014an integrated approach to ontology refinement. In: Proceedings of ICFCA 2008. Lecture Notes in Artificial Intelligence, vol 4933","DOI":"10.1007\/978-3-540-78137-0_5"},{"key":"2631_CR33","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1515\/9783110199901.373","volume":"34","author":"CJ Fillmore","year":"2006","unstructured":"Fillmore CJ et al (2006) Frame semantics. Cogn Linguist Basic Read 34:373\u2013400","journal-title":"Cogn Linguist Basic Read"},{"key":"2631_CR34","doi-asserted-by":"publisher","unstructured":"Gangemi A, Presutti V (2009) In: Staab, S., Studer, R. (eds.) Ontology Design Patterns, Springer, Berlin, Heidelberg, pp 221\u2013243. https:\/\/doi.org\/10.1007\/978-3-540-92673-3_10","DOI":"10.1007\/978-3-540-92673-3_10"},{"key":"2631_CR35","unstructured":"Alam M, Buscaldi D, Cochez M, Osborne F, Recupero D, et al (2023) Preface-proceedings of the 6th workshop on deep learning for knowledge graphs (dlkg2023) co-located with international semantic web conference 2023. In: CEUR workshop proceedings, vol 3559, CEUR-WS, pp 1\u20134"},{"key":"2631_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-11609-4","author":"P Groth","year":"2022","unstructured":"Groth P, Rula A, Schneider J, Tiddi I, Simperl E, Alexopoulos P, Hoekstra R, Alam M, Dimou A, Tamper M (2022) The semantic web: Eswc 2022 satellite events. Lect Notes Comput Sci. https:\/\/doi.org\/10.1007\/978-3-031-11609-4","journal-title":"Lect Notes Comput Sci"},{"issue":"16","key":"2631_CR37","doi-asserted-by":"publisher","first-page":"8995","DOI":"10.1007\/s00521-024-09646-6","volume":"36","author":"Z Hu","year":"2024","unstructured":"Hu Z, Hou W, Liu X (2024) Deep learning for named entity recognition: a survey. Neural Comput Appl 36(16):8995\u20139022. https:\/\/doi.org\/10.1007\/s00521-024-09646-6","journal-title":"Neural Comput Appl"},{"issue":"3","key":"2631_CR38","doi-asserted-by":"publisher","first-page":"2556","DOI":"10.1109\/TKDE.2021.3117715","volume":"35","author":"W Shen","year":"2023","unstructured":"Shen W, Li Y, Liu Y, Han J, Wang J, Yuan X (2023) Entity linking meets deep learning: techniques and solutions. IEEE Trans Knowl Data Eng 35(3):2556\u20132578. https:\/\/doi.org\/10.1109\/TKDE.2021.3117715","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2631_CR39","doi-asserted-by":"publisher","unstructured":"Han X, Gao T, Lin Y, Peng H, Yang Y, Xiao C, Liu Z, Li P, Zhou J, Sun M (2020) More data, more relations, more context and more openness: a review and outlook for relation extraction. In: Wong, K.-F., Knight, K., Wu, H. (eds.) Proceedings of the 1st conference of the asia-pacific chapter of the association for computational linguistics and the 10th international joint conference on natural language processing, pp 745\u2013758. Association for Computational Linguistics, Suzhou, China. https:\/\/doi.org\/10.18653\/v1\/2020.aacl-main.75","DOI":"10.18653\/v1\/2020.aacl-main.75"},{"key":"2631_CR40","doi-asserted-by":"publisher","unstructured":"Han X, Gao T, Yao Y, Ye D, Liu Z, Sun M (2019) OpenNRE: an open and extensible toolkit for neural relation extraction. In: Pad\u00f3, S., Huang, R. (eds.) Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP): system demonstrations, pp 169\u2013174. Association for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-3029","DOI":"10.18653\/v1\/D19-3029"},{"key":"2631_CR41","doi-asserted-by":"publisher","unstructured":"Zhang N, Xu X, Tao L, Yu H, Ye H, Qiao S, Xie X, Chen X, Li Z, Li L (2021) DeepKE: a deep learning based knowledge extraction toolkit for knowledge base population. In: Che, W., Shutova, E. (eds.) Proceedings of the 2022 conference on empirical methods in natural language processing: system demonstrations, pp 98\u2013108. Association for Computational Linguistics, Abu Dhabi, UAE. https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-demos.10","DOI":"10.18653\/v1\/2022.emnlp-demos.10"},{"key":"2631_CR42","doi-asserted-by":"publisher","unstructured":"Lu Y, Liu Q, Dai D, Xiao X, Lin H, Han X, Sun L, Wu H (2022) Unified structure generation for universal information extraction. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 5755\u20135772. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.395","DOI":"10.18653\/v1\/2022.acl-long.395"},{"issue":"2","key":"2631_CR43","doi-asserted-by":"publisher","first-page":"255","DOI":"10.3233\/SW-180333","volume":"11","author":"JL Martinez-Rodriguez","year":"2020","unstructured":"Martinez-Rodriguez JL, Hogan A, Lopez-Arevalo I (2020) Information extraction meets the semantic web: a survey. Semantic Web 11(2):255\u2013335. https:\/\/doi.org\/10.3233\/SW-180333","journal-title":"Semantic Web"},{"key":"2631_CR44","unstructured":"Yao L, Mao C, Luo Y (2019) Kg-bert: Bert for knowledge graph completion. arXiv:abs\/1909.03193"},{"issue":"4","key":"2631_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3618295","volume":"56","author":"L Zhong","year":"2023","unstructured":"Zhong L, Wu J, Li Q, Peng H, Wu X (2023) A comprehensive survey on automatic knowledge graph construction. ACM Comput. Surv. 56(4):1\u201362. https:\/\/doi.org\/10.1145\/3618295","journal-title":"ACM Comput. Surv."},{"issue":"7","key":"2631_CR46","doi-asserted-by":"publisher","first-page":"3580","DOI":"10.1109\/TKDE.2024.3352100","volume":"36","author":"S Pan","year":"2024","unstructured":"Pan S, Luo L, Wang Y, Chen C, Wang J, Wu X (2024) Unifying large language models and knowledge graphs: a roadmap. IEEE Trans Knowl Data Eng 36(7):3580\u20133599","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2631_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2024.100859","volume":"85","author":"A Gangemi","year":"2025","unstructured":"Gangemi A, Nuzzolese AG (2025) Logic augmented generation. J Web Semant 85:100859. https:\/\/doi.org\/10.1016\/j.websem.2024.100859","journal-title":"J Web Semant"},{"key":"2631_CR48","doi-asserted-by":"publisher","unstructured":"Zhang B, Soh H (2024) Extract, define, canonicalize: An LLM-based framework for knowledge graph construction. In: Al-Onaizan, Y., Bansal, M., Chen, Y.-N. (eds.) Proceedings of the 2024 conference on empirical methods in natural language processing, pp 9820\u20139836. Association for Computational Linguistics, Miami, Florida, USA. https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-main.548","DOI":"10.18653\/v1\/2024.emnlp-main.548"},{"key":"2631_CR49","unstructured":"Orlando R, Conia S, Faralli S, Navigli R (2022) Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing. In: Proc. of LREC 2022, pp 2634\u20132641. European Language Resources Association, Marseille, France. https:\/\/aclanthology.org\/2022.lrec-1.282"},{"key":"2631_CR50","doi-asserted-by":"crossref","unstructured":"Meloni A, Reforgiato Recupero D, Gangemi A (2017) AMR2FRED, A tool for translating abstract meaning representation to motif-based linguistic knowledge graphs. In: The Semantic Web: ESWC 2017 Satellite Events, Springer, Portoro\u017e, Slovenia, pp 43\u201347","DOI":"10.1007\/978-3-319-70407-4_9"},{"key":"2631_CR51","doi-asserted-by":"crossref","unstructured":"Van Durme B, Schubert L (2008) Open knowledge extraction through compositional language processing. In: Bos, J., Delmonte, R. (eds.) Semantics in Text Processing. STEP 2008 Conference Proceedings, pp 239\u2013254. College Publications. https:\/\/aclanthology.org\/W08-2219\/","DOI":"10.3115\/1626481.1626500"},{"key":"2631_CR52","doi-asserted-by":"crossref","unstructured":"Curran J, Clark S, Bos J (2007) Linguistically motivated large-scale NLP with C &C and boxer. In: Ananiadou, S. (ed.) Proceedings of the 45th annual meeting of the association for computational linguistics companion volume proceedings of the demo and poster sessions, pp 33\u201336. Association for Computational Linguistics, Prague, Czech Republic. https:\/\/aclanthology.org\/P07-2009\/","DOI":"10.3115\/1557769.1557781"},{"key":"2631_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2024.100833","volume":"83","author":"S Pramanik","year":"2024","unstructured":"Pramanik S, Alabi J, Roy RS, Weikum G (2024) Uniqorn: unified question answering over RDF knowledge graphs and natural language text. J Web Semant 83:100833. https:\/\/doi.org\/10.1016\/j.websem.2024.100833","journal-title":"J Web Semant"},{"key":"2631_CR54","unstructured":"Graciotti A, Presutti V, Tripodi R (2024) Latent vs explicit knowledge representation: How ChatGPT answers questions about low-frequency entities. In: Calzolari, N., Kan, M.-Y., Hoste, V., Lenci, A., Sakti, S., Xue, N. (eds.) Proceedings of the 2024 joint international conference on computational linguistics, language resources and evaluation (LREC-COLING 2024), pp 10172\u201310185. ELRA and ICCL, Torino, Italia. https:\/\/aclanthology.org\/2024.lrec-main.888"},{"key":"2631_CR55","doi-asserted-by":"crossref","unstructured":"Gangemi A, Graciotti A, Marzi E, Meloni A, Nuzzolese A, Presutti V, Reforgiato Recupero D, Russo A, Tripodi R (2024) MusicBO, an application of Text2AMR2FRED to the Musical Heritage domain. In: 20th extended semantic web conference. CEUR Workshop Proceedings, Crete, Greece","DOI":"10.1007\/978-3-031-78952-6_29"},{"key":"2631_CR56","doi-asserted-by":"publisher","first-page":"1472512","DOI":"10.3389\/fcomp.2024.1472512","volume":"6","author":"C Santini","year":"2024","unstructured":"Santini C (2024) Combining language models for knowledge extraction from Italian TEI editions. Front Comput Sci 6:1472512. https:\/\/doi.org\/10.3389\/fcomp.2024.1472512","journal-title":"Front Comput Sci"},{"key":"2631_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2024.100859","volume":"85","author":"A Gangemi","year":"2025","unstructured":"Gangemi A, Nuzzolese AG (2025) Logic augmented generation. J Web Semant 85:100859. https:\/\/doi.org\/10.1016\/j.websem.2024.100859","journal-title":"J Web Semant"},{"key":"2631_CR58","doi-asserted-by":"crossref","unstructured":"Gangemi A (2013) A comparison of knowledge extraction tools for the semantic web. In: The semantic web: semantics and big data, Springer, Berlin, Heidelberg, pp 351\u2013366","DOI":"10.1007\/978-3-642-38288-8_24"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02631-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-025-02631-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02631-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T09:15:16Z","timestamp":1767690916000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-025-02631-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,6]]},"references-count":58,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["2631"],"URL":"https:\/\/doi.org\/10.1007\/s10115-025-02631-y","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,6]]},"assertion":[{"value":"5 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"47"}}