{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T16:49:16Z","timestamp":1784306956336,"version":"3.55.0"},"publisher-location":"Wiesbaden","reference-count":7,"publisher":"Springer Fachmedien Wiesbaden","isbn-type":[{"value":"9783658437046","type":"print"},{"value":"9783658437053","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:00:00Z","timestamp":1713312000000},"content-version":"vor","delay-in-days":107,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:title>Zusammenfassung<\/jats:title><jats:p>Knowledge Graphs (KG) provide us with a structured, flexible, transparent, cross-system, and collaborative way of organizing our knowledge and data across various domains in society and industrial as well as scientific disciplines. KGs surpass any other form of representation in terms of effectiveness. However, Knowledge Graph Engineering (KGE) requires in-depth experiences of graph structures, web technologies, existing models and vocabularies, rule sets, logic, as well as best practices. It also demands a significant amount of work.<\/jats:p><jats:p>Considering the advancements in large language models (LLMs) and their interfaces and applications in recent years, we have conducted comprehensive experiments with ChatGPT to explore its potential in supporting KGE. In this paper, we present a selection of these experiments and their results to demonstrate how ChatGPT can assist us in the development and management of KGs.<\/jats:p><jats:p><jats:bold>Zusammenfassung.<\/jats:bold> Wissensgraphen (englisch <jats:italic>Knowledge Graphs<\/jats:italic>, KGs), bieten uns eine strukturierte, flexible, transparente, system\u00fcbergreifende und kollaborative M\u00f6glichkeit, unser Wissen und unsere Daten \u00fcber verschiedene Bereiche der Gesellschaft und der industriellen sowie wissenschaftlichen Disziplinen hinweg zu organisieren. KGs \u00fcbertreffen jede andere Form der Repr\u00e4sentation in Bezug auf die Effektivit\u00e4t. Die Entwicklung von Wissensgraphen (englisch <jats:italic>Knowledge Graph Engineering<\/jats:italic>, KGE) erfordert jedoch fundierte Erfahrungen mit Graphstrukturen, Webtechnologien, bestehenden Modellen und Vokabularen, Regelwerken, Logik sowie Best Practices. Es erfordert auch einen erheblichen Arbeitsaufwand.<\/jats:p><jats:p>In Anbetracht der Fortschritte bei gro\u00dfen Sprachmodellen (englisch <jats:italic>Large Language Modells<\/jats:italic>, LLMs) und ihren Schnittstellen und Anwendungen in den letzten Jahren haben wir umfassende Experimente mit ChatGPT durchgef\u00fchrt, um sein Potenzial zur Unterst\u00fctzung von KGE zu untersuchen. In diesem Artikel stellen wir eine Auswahl dieser Experimente und ihre Ergebnisse vor, um zu zeigen, wie ChatGPT uns bei der Entwicklung und Verwaltung von KGs unterst\u00fctzen kann.<\/jats:p>","DOI":"10.1007\/978-3-658-43705-3_8","type":"book-chapter","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T11:20:25Z","timestamp":1713266425000},"page":"103-115","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5260-5181","authenticated-orcid":false,"given":"Lars-Peter","family":"Meyer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9948-6458","authenticated-orcid":false,"given":"Claus","family":"Stadler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3127-0815","authenticated-orcid":false,"given":"Johannes","family":"Frey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9155-8920","authenticated-orcid":false,"given":"Norman","family":"Radtke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1337-2770","authenticated-orcid":false,"given":"Kurt","family":"Junghanns","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4193-8209","authenticated-orcid":false,"given":"Roy","family":"Meissner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9592-418X","authenticated-orcid":false,"given":"Gordian","family":"Dziwis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1459-3754","authenticated-orcid":false,"given":"Kirill","family":"Bulert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0762-8688","authenticated-orcid":false,"given":"Michael","family":"Martin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,17]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","unstructured":"Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., ... & Zhang, Y. (Mar 2023). Sparks of artificial general intelligence: Early experiments with gpt-4. https:\/\/doi.org\/10.48550\/ARXIV.2303.12712.","DOI":"10.48550\/ARXIV.2303.12712"},{"key":"8_CR2","unstructured":"Cagle, K. (Mar 2023). Nine chatgpt tricks for knowledge graph workers. https:\/\/thecaglereport.com\/2023\/03\/16\/nine-chatgpt-tricks-for-knowledge-graph-workers\/, Accessed: 14. Apr. 2023."},{"key":"8_CR3","doi-asserted-by":"publisher","unstructured":"Ekaputra, F. J., Llugiqi, M., Sabou, M., Ekelhart, A., Paulheim, H., Breit, A., ... & Auer, S. (Mar 2023). Describing and organizing semantic web and machine learning systems in the swemls-kg. https:\/\/doi.org\/10.48550\/ARXIV.2303.15113.","DOI":"10.48550\/ARXIV.2303.15113"},{"key":"8_CR4","doi-asserted-by":"publisher","unstructured":"Haque, M. U., Dharmadasa, I., Sworna, Z. T., Rajapakse, R. N., & Ahmad, H. (Dec 2022). \u201ci think this is the most disruptive technology\u201d: Exploring sentiments of chatgpt early adopters using twitter data. https:\/\/doi.org\/10.48550\/ARXIV.2212.05856.","DOI":"10.48550\/ARXIV.2212.05856"},{"key":"8_CR5","unstructured":"Kalici\u0144ski, K. (Jan 2023). Create neo4j database model with chatgpt. https:\/\/neo4j.com\/developer-blog\/create-neo4j-database-model-with-chatgpt\/, Accessed: 14. Apr. 2023."},{"key":"8_CR6","doi-asserted-by":"publisher","unstructured":"Lin, W., Babyn, P., yan, Y., & Zhang, W. (Mar 2023). Context-based ontology modelling for database: Enabling chatgpt for semantic database management. https:\/\/doi.org\/10.48550\/ARXIV.2303.07351.","DOI":"10.48550\/ARXIV.2303.07351"},{"key":"8_CR7","doi-asserted-by":"publisher","unstructured":"Omar, R., Mangukiya, O., Kalnis, P., Mansour, & E. (Feb 2023). Chatgpt versus traditional question answering for knowledge graphs: Current status and future directions towards knowledge graph chatbots. https:\/\/doi.org\/10.48550\/ARXIV.2302.06466.","DOI":"10.48550\/ARXIV.2302.06466"}],"container-title":["Informatik aktuell","First Working Conference on Artificial Intelligence Development for a Resilient and Sustainable Tomorrow"],"original-title":[],"language":"de","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-658-43705-3_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T11:21:39Z","timestamp":1713266499000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-658-43705-3_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783658437046","9783658437053"],"references-count":7,"URL":"https:\/\/doi.org\/10.1007\/978-3-658-43705-3_8","relation":{},"ISSN":["1431-472X","2628-8958"],"issn-type":[{"value":"1431-472X","type":"print"},{"value":"2628-8958","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"17 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIDRST","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Working conference on Artificial Intelligence Development for a Resilient and Sustainable Tomorrow","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leipzig","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Deutschland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aidrst2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/kmi-leipzig.de\/kmi-projekt\/ai-tomorrow\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}