Investigating antiquities trafficking with generative pre-trained transformer (GPT)-3 enabled knowledge graphs: A case study
Background: There is a wide variety of potential sources from which insight into the antiquities trade could be culled, from newspaper articles to auction catalogues, to court dockets, to personal archives, if it could all be systematically examined. We explore the use of a large language model, GPT...
Saved in:
| Published in: | Open research Europe Vol. 3; p. 100 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London, UK
F1000 Research Limited
2023
F1000 Research Ltd |
| Subjects: | |
| ISSN: | 2732-5121, 2732-5121 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Background:
There is a wide variety of potential sources from which insight into the antiquities trade could be culled, from newspaper articles to auction catalogues, to court dockets, to personal archives, if it could all be systematically examined. We explore the use of a large language model, GPT-3, to semi-automate the creation of a knowledge graph of a body of scholarship concerning the antiquities trade.
Methods:
We give GPT-3 a prompt guiding it to identify knowledge statements around the trade. Given GPT-3’s understanding of the statistical properties of language, our prompt teaches GPT-3 to append text to each article we feed it where the appended text summarizes the knowledge in the article. The summary is in the form of a list of subject, predicate, and object relationships, representing a knowledge graph. Previously we created such lists by manually annotating the source articles. We compare the result of this automatic process with a knowledge graph created from the same sources via hand. When such knowledge graphs are projected into a multi-dimensional embedding model using a neural network (via the Ampligraph open-source Python library), the relative positioning of entities implies the probability of a connection; the direction of the positioning implies the
kind
of connection. Thus, we can interrogate the embedding model to discover new probable relationships. The results can generate new insight about the antiquity trade, suggesting possible avenues of research.
Results:
We find that our semi-automatic approach to generating the knowledge graph in the first place produces comparable results to our hand-made version, but at an enormous savings of time and a possible expansion of the amount of materials we can consider.
Conclusions:
These results have implications for working with other kinds of archaeological knowledge in grey literature, reports, articles, and other venues via computational means. |
|---|---|
| AbstractList | Background: There is a wide variety of potential sources from which insight into the antiquities trade could be culled, from newspaper articles to auction catalogues, to court dockets, to personal archives, if it could all be systematically examined. We explore the use of a large language model, GPT-3, to semi-automate the creation of a knowledge graph of a body of scholarship concerning the antiquities trade. Methods: We give GPT-3 a prompt guiding it to identify knowledge statements around the trade. Given GPT-3's understanding of the statistical properties of language, our prompt teaches GPT-3 to append text to each article we feed it where the appended text summarizes the knowledge in the article. The summary is in the form of a list of subject, predicate, and object relationships, representing a knowledge graph. Previously we created such lists by manually annotating the source articles. We compare the result of this automatic process with a knowledge graph created from the same sources via hand. When such knowledge graphs are projected into a multi-dimensional embedding model using a neural network (via the Ampligraph open-source Python library), the relative positioning of entities implies the probability of a connection; the direction of the positioning implies the kind of connection. Thus, we can interrogate the embedding model to discover new probable relationships. The results can generate new insight about the antiquity trade, suggesting possible avenues of research. Results: We find that our semi-automatic approach to generating the knowledge graph in the first place produces comparable results to our hand-made version, but at an enormous savings of time and a possible expansion of the amount of materials we can consider. Conclusions: These results have implications for working with other kinds of archaeological knowledge in grey literature, reports, articles, and other venues via computational means.Background: There is a wide variety of potential sources from which insight into the antiquities trade could be culled, from newspaper articles to auction catalogues, to court dockets, to personal archives, if it could all be systematically examined. We explore the use of a large language model, GPT-3, to semi-automate the creation of a knowledge graph of a body of scholarship concerning the antiquities trade. Methods: We give GPT-3 a prompt guiding it to identify knowledge statements around the trade. Given GPT-3's understanding of the statistical properties of language, our prompt teaches GPT-3 to append text to each article we feed it where the appended text summarizes the knowledge in the article. The summary is in the form of a list of subject, predicate, and object relationships, representing a knowledge graph. Previously we created such lists by manually annotating the source articles. We compare the result of this automatic process with a knowledge graph created from the same sources via hand. When such knowledge graphs are projected into a multi-dimensional embedding model using a neural network (via the Ampligraph open-source Python library), the relative positioning of entities implies the probability of a connection; the direction of the positioning implies the kind of connection. Thus, we can interrogate the embedding model to discover new probable relationships. The results can generate new insight about the antiquity trade, suggesting possible avenues of research. Results: We find that our semi-automatic approach to generating the knowledge graph in the first place produces comparable results to our hand-made version, but at an enormous savings of time and a possible expansion of the amount of materials we can consider. Conclusions: These results have implications for working with other kinds of archaeological knowledge in grey literature, reports, articles, and other venues via computational means. Background: There is a wide variety of potential sources from which insight into the antiquities trade could be culled, from newspaper articles to auction catalogues, to court dockets, to personal archives, if it could all be systematically examined. We explore the use of a large language model, GPT-3, to semi-automate the creation of a knowledge graph of a body of scholarship concerning the antiquities trade. Methods: We give GPT-3 a prompt guiding it to identify knowledge statements around the trade. Given GPT-3’s understanding of the statistical properties of language, our prompt teaches GPT-3 to append text to each article we feed it where the appended text summarizes the knowledge in the article. The summary is in the form of a list of subject, predicate, and object relationships, representing a knowledge graph. Previously we created such lists by manually annotating the source articles. We compare the result of this automatic process with a knowledge graph created from the same sources via hand. When such knowledge graphs are projected into a multi-dimensional embedding model using a neural network (via the Ampligraph open-source Python library), the relative positioning of entities implies the probability of a connection; the direction of the positioning implies the kind of connection. Thus, we can interrogate the embedding model to discover new probable relationships. The results can generate new insight about the antiquity trade, suggesting possible avenues of research. Results: We find that our semi-automatic approach to generating the knowledge graph in the first place produces comparable results to our hand-made version, but at an enormous savings of time and a possible expansion of the amount of materials we can consider. Conclusions: These results have implications for working with other kinds of archaeological knowledge in grey literature, reports, articles, and other venues via computational means. Background: There is a wide variety of potential sources from which insight into the antiquities trade could be culled, from newspaper articles to auction catalogues, to court dockets, to personal archives, if it could all be systematically examined. We explore the use of a large language model, GPT-3, to semi-automate the creation of a knowledge graph of a body of scholarship concerning the antiquities trade. Methods: We give GPT-3 a prompt guiding it to identify knowledge statements around the trade. Given GPT-3’s understanding of the statistical properties of language, our prompt teaches GPT-3 to append text to each article we feed it where the appended text summarizes the knowledge in the article. The summary is in the form of a list of subject, predicate, and object relationships, representing a knowledge graph. Previously we created such lists by manually annotating the source articles. We compare the result of this automatic process with a knowledge graph created from the same sources via hand. When such knowledge graphs are projected into a multi-dimensional embedding model using a neural network (via the Ampligraph open-source Python library), the relative positioning of entities implies the probability of a connection; the direction of the positioning implies the kind of connection. Thus, we can interrogate the embedding model to discover new probable relationships. The results can generate new insight about the antiquity trade, suggesting possible avenues of research. Results: We find that our semi-automatic approach to generating the knowledge graph in the first place produces comparable results to our hand-made version, but at an enormous savings of time and a possible expansion of the amount of materials we can consider. Conclusions: These results have implications for working with other kinds of archaeological knowledge in grey literature, reports, articles, and other venues via computational means. Background: There is a wide variety of potential sources from which insight into the antiquities trade could be culled, from newspaper articles to auction catalogues, to court dockets, to personal archives, if it could all be systematically examined. We explore the use of a large language model, GPT-3, to semi-automate the creation of a knowledge graph of a body of scholarship concerning the antiquities trade. Methods: We give GPT-3 a prompt guiding it to identify knowledge statements around the trade. Given GPT-3’s understanding of the statistical properties of language, our prompt teaches GPT-3 to append text to each article we feed it where the appended text summarizes the knowledge in the article. The summary is in the form of a list of subject, predicate, and object relationships, representing a knowledge graph. Previously we created such lists by manually annotating the source articles. We compare the result of this automatic process with a knowledge graph created from the same sources via hand. When such knowledge graphs are projected into a multi-dimensional embedding model using a neural network (via the Ampligraph open-source Python library), the relative positioning of entities implies the probability of a connection; the direction of the positioning implies the kind of connection. Thus, we can interrogate the embedding model to discover new probable relationships. The results can generate new insight about the antiquity trade, suggesting possible avenues of research. Results: We find that our semi-automatic approach to generating the knowledge graph in the first place produces comparable results to our hand-made version, but at an enormous savings of time and a possible expansion of the amount of materials we can consider. Conclusions: These results have implications for working with other kinds of archaeological knowledge in grey literature, reports, articles, and other venues via computational means. |
| Author | Yates, Donna Graham, Shawn El-Roby, Ahmed |
| Author_xml | – sequence: 1 givenname: Shawn surname: Graham fullname: Graham, Shawn – sequence: 2 givenname: Donna orcidid: 0000-0002-9936-6461 surname: Yates fullname: Yates, Donna – sequence: 3 givenname: Ahmed surname: El-Roby fullname: El-Roby, Ahmed |
| BookMark | eNpVkU9r3DAQxUVJoWmar1B0TA_e6p9luZcSQpsuBNpDehayPPIq8UqOZG8I9MNXuxtK9zTDvMdvmHnv0VmIARD6SMmKMqnU5zhBSJBhSaVbUUkIX9E36Jw1nFU1ZfTsv_4dusz5gRDCasolbc_Rn3XYQZ79YGYfBmzC7J8WP3vIeE7GOW8f9_NnP2_wAAFS8e0ATwmqovsA_d4XsotpCwlf3f66_1RxDMF0Y9EeQ3wudQA8JDNt8hd8ja3JgPO89C8f0FtnxgyXr_UC_f7-7f7mR3X383Z9c31XWc4IrQCUcrZhRrSyBV4zW5OOMicaxazreQeNYL2Uoq876lrHhKydU0xQyxvoLL9A6yO3j-ZBT8lvTXrR0Xh9GMQ0aJNmb0fQ1vSMWWFaLp0ASZXowSkB0DVKde2e9fXImpZuC72FUO4fT6CnSvAbPcSdpkSIWhFRCFevhBSflvJ8vfXZwjiaAHHJmqlata2gqi1WebTaFHNO4P7toUQf8tcn-etD_pryv1J8rYw |
| Cites_doi | 10.37911/9781947864382 10.5281/zenodo.6567481 10.1179/0093469013Z.00000000053 10.1086/691725 10.1145/3442188.3445922 10.1017/aap.2023.1 10.1017/S094073911900016X 10.1515/opar-2015-0004 10.48550/arXiv.1810.04805 10.17863/CAM.15975 10.1057/978-1-137-54405-6_10 10.7183/0002-7316.79.1.5 10.48550/arXiv.2211.15661 10.5744/florida/9780813029726.003.0010 10.48550/arXiv.2106.07742 10.48550/arXiv.2302.07736 10.48550/arXiv.2212.03551 10.2307/507226 10.48550/arXiv.2210.13382 10.1007/978-3-030-84856-9_2 10.48550/arXiv.2303.15056 10.4324/9781351026826-8 10.1007/s10611-011-9321-6 |
| ContentType | Journal Article |
| Copyright | Copyright: © 2023 Graham S et al. Copyright: © 2023 Graham S et al. 2023 |
| Copyright_xml | – notice: Copyright: © 2023 Graham S et al. – notice: Copyright: © 2023 Graham S et al. 2023 |
| DBID | AAYXX CITATION 7X8 5PM DOA |
| DOI | 10.12688/openreseurope.16003.1 |
| DatabaseName | CrossRef MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2732-5121 |
| ExternalDocumentID | oai_doaj_org_article_cad22c4a936f4e6184def84eeb788b9c PMC10445804 10_12688_openreseurope_16003_1 |
| GrantInformation_xml | – fundername: Horizon 2020 Framework Programme grantid: 804851 – fundername: Social Sciences and Humanities Research Council of Canada |
| GroupedDBID | AAFWJ AAYXX AFPKN ALMA_UNASSIGNED_HOLDINGS CITATION GROUPED_DOAJ M~E OK1 PGMZT RPM 7X8 5PM |
| ID | FETCH-LOGICAL-c3201-ee88fc72a4969e352c50b12f4782cfd3be742d664d5b1f9f2465ff8241c37ebc3 |
| IEDL.DBID | DOA |
| ISSN | 2732-5121 |
| IngestDate | Fri Oct 03 12:50:55 EDT 2025 Tue Sep 30 17:13:05 EDT 2025 Fri Jul 11 07:20:44 EDT 2025 Sat Nov 29 05:59:19 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3201-ee88fc72a4969e352c50b12f4782cfd3be742d664d5b1f9f2465ff8241c37ebc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 No competing interests were disclosed. |
| ORCID | 0000-0002-9936-6461 |
| OpenAccessLink | https://doaj.org/article/cad22c4a936f4e6184def84eeb788b9c |
| PQID | 2858994189 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_cad22c4a936f4e6184def84eeb788b9c pubmedcentral_primary_oai_pubmedcentral_nih_gov_10445804 proquest_miscellaneous_2858994189 crossref_primary_10_12688_openreseurope_16003_1 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-00-00 |
| PublicationDateYYYYMMDD | 2023-01-01 |
| PublicationDate_xml | – year: 2023 text: 2023-00-00 |
| PublicationDecade | 2020 |
| PublicationPlace | London, UK |
| PublicationPlace_xml | – name: London, UK |
| PublicationTitle | Open research Europe |
| PublicationYear | 2023 |
| Publisher | F1000 Research Limited F1000 Research Ltd |
| Publisher_xml | – name: F1000 Research Limited – name: F1000 Research Ltd |
| References | M Bogdanos (ref-4) 2021 E Gilgan (ref-14) 2001 K Kintigh (ref-24) 2014; 79 C Chippendale (ref-7) 2000; 104 A Radford (ref-36) 2018 G Jayaraman (ref-21) 2015 D Yates (ref-45) 2006 T Mashberg (ref-29) 2015 N Oosterman (ref-31) 2018 S Paredes Maury (ref-35) 1999 F Huang (ref-19) 2023 (ref-10) 2019 N Brodie (ref-6) 2019; 26 T Davis (ref-8) 2011; 56 J Felch (ref-12) 2011 M Kersel (ref-23) 2006b M Kersel (ref-22) 2006a L Weng (ref-44) 2023 (ref-33) 2018 A Sabar (ref-39) 2020 (ref-34) 2020 E Bender (ref-3) 2021 N Oosterman (ref-32) 2019 E Akyürek (ref-1) 2022 P Krishnankutty (ref-26) 2022 M Levine (ref-28) 2013; 38 S Graham (ref-17) 2023 S Beltrametti (ref-2) 2016; 59 S Gopinathan (ref-15) 2021 A Brandsen (ref-5) 2021 J Devlin (ref-9) 2019 K Li (ref-20) 2022 M Shanahan (ref-40) 2022 S Huang (ref-18) 2022 B Muller (ref-30) M Fabiani (ref-11) 2021 T Kuzman (ref-27) P Watson (ref-42) 2007 M Woolf (ref-43) 2023 F Gilardi (ref-13) 2023 K Kintigh (ref-25) 2015; 1 S Graham (ref-16) 2023; 11 I Romanowska (ref-38) 2021 T Underwood (ref-41) 2022 D Richman (ref-37) 2022 |
| References_xml | – year: 2001 ident: ref-14 article-title: Looting and the Market for Maya Objects: A Belizean Perspective. – year: 2021 ident: ref-38 article-title: Agent-based Modeling for Archaeology: Simulating the Complexity of Societies. doi: 10.37911/9781947864382 – year: 1999 ident: ref-35 article-title: Surviving in the Rainforest: The Realities of Looting in the Rural Villages of El Petén. – year: 2022 ident: ref-41 article-title: Mapping the Latent Spaces of Culture. publication-title: Startwords. doi: 10.5281/zenodo.6567481 – year: 2007 ident: ref-42 article-title: The Medici Conspiracy: The Illicit Journey of Looted Antiquities From Italy's Tomb Raiders to the Words Greatest Museums. – volume: 38 start-page: 264-76 year: 2013 ident: ref-28 article-title: Museum Salvage: A Case Study of Mesoamerican Artifacts in Museum Collections and on the Antiquities Market. publication-title: J Field Archaeol. doi: 10.1179/0093469013Z.00000000053 – volume: 59 start-page: 913-44 year: 2016 ident: ref-2 article-title: Market Responses to Court Rulings: Evidence from Antiquities Auctions. publication-title: J Law Econ. doi: 10.1086/691725 – start-page: 610-23 year: 2021 ident: ref-3 article-title: On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? doi: 10.1145/3442188.3445922 – volume: 11 start-page: 126-138 year: 2023 ident: ref-16 article-title: Relationship Prediction in a Knolwedge Graph Embedding Model of the Illicit Antiquities Trade. publication-title: Advances in Archaeological Practice. doi: 10.1017/aap.2023.1 – volume: 26 start-page: 265-283 year: 2019 ident: ref-6 article-title: Through a Glass, Darkly: Long-Term Antiquities Auction Data in Context. publication-title: Int J Cult Prop. doi: 10.1017/S094073911900016X – year: 2023 ident: ref-43 article-title: ChatGPT's API is So Good and Cheap, It Makes Most Text Generating AI Obsolete – year: 2018 ident: ref-33 article-title: Language Models Are Few-Shot Learners – ident: ref-27 article-title: ChatGPT: Beginning of an End of Manual Linguistic Data Annotation? Use Case of Automatic Genre Identification. – year: 2020 ident: ref-34 article-title: Improving Language Understanding with Unsupervised Learning – year: 2021 ident: ref-15 article-title: Meet the Amateur Art Sleuths Fighting To Bring Back India’s Looted Cultural Heritage. publication-title: Vice. – volume: 1 start-page: 96-101 year: 2015 ident: ref-25 article-title: Extracting Information from Archaeological Texts. publication-title: Open Archaeology. doi: 10.1515/opar-2015-0004 – year: 2019 ident: ref-9 article-title: BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. doi: 10.48550/arXiv.1810.04805 – year: 2006b ident: ref-23 article-title: License to Sell: The Legal Trade of Antiquities in Israel. doi: 10.17863/CAM.15975 – ident: ref-30 article-title: BERT 101 - State Of The Art NLP Model Explained. – year: 2023 ident: ref-44 article-title: Prompt Engineering – year: 2011 ident: ref-12 article-title: Chasing Aphrodite: The Hunt for Looted Antiquities at the World’s Richest Museum. – year: 2006 ident: ref-45 article-title: South America on the Block: The Changing Face of Pre-Columbian Antiquities Auctions in Response to International Law. – year: 2022 ident: ref-18 article-title: Relationship Extraction with GPT-3. publication-title: Geek Culture. – start-page: 213-35 year: 2019 ident: ref-32 article-title: Regional Overviews of the Policing of Art Crime in the European Union. publication-title: The Palgrave Handbook on Art Crime. doi: 10.1057/978-1-137-54405-6_10 – year: 2020 ident: ref-39 article-title: Veritas: A Harvard Professor, a Con Man, and the Gospel of Jesuss Wife. – volume: 79 start-page: 5-24 year: 2014 ident: ref-24 article-title: Grand Challenges for Archaeology. publication-title: Am Antiq. doi: 10.7183/0002-7316.79.1.5 – year: 2022 ident: ref-1 article-title: What Learning Algorithm Is In-Context Learning? Investigations with Linear Models. doi: 10.48550/arXiv.2211.15661 – start-page: 188-205 year: 2006a ident: ref-22 article-title: From the Ground to the Buyer: A Market Analysis of the Trade in Illegal Antiquities. publication-title: Archaeology, Cultural Heritage, and the Antiquities Trade. doi: 10.5744/florida/9780813029726.003.0010 – year: 2022 ident: ref-37 article-title: How to create GPT-3 apps in Google Sheets – Free Tutorial. publication-title: Richman SEO Training. – year: 2021 ident: ref-5 article-title: Can BERT Dig It? -- Named Entity Recognition for Information Retrieval in the Archaeology Domain. doi: 10.48550/arXiv.2106.07742 – year: 2023 ident: ref-19 article-title: Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech. doi: 10.48550/arXiv.2302.07736 – year: 2023 ident: ref-17 article-title: XLabCU/gpt3-relationship-extraction-to-kg: first (0.0.1). publication-title: Zenodo – year: 2021 ident: ref-4 article-title: In the Matter of A Grand Jury Investigation into a Private New York Antiquities Collector. – year: 2022 ident: ref-40 article-title: Talking About Large Language Models. publication-title: arXiv. doi: 10.48550/arXiv.2212.03551 – year: 2019 ident: ref-10 article-title: Felony Arrest Warrant, People of New York v. Sanjeeve Asokan et Al. – year: 2015 ident: ref-21 article-title: Intrepid Bloggers Try to Retrieve Stolen Sacred Art from around the World. publication-title: India Today. – year: 2022 ident: ref-26 article-title: How Stolen Art Detectives India Pride Project Tracked down 265 Artefacts. publication-title: The Print. – volume: 104 start-page: 463-511 year: 2000 ident: ref-7 article-title: Material Consequences of Contemporary Classical Collecting. publication-title: Am J Archaeol. doi: 10.2307/507226 – year: 2022 ident: ref-20 article-title: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task. doi: 10.48550/arXiv.2210.13382 – start-page: 11-28 year: 2021 ident: ref-11 article-title: Transiting Through the Antiquities Market. doi: 10.1007/978-3-030-84856-9_2 – year: 2023 ident: ref-13 article-title: ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks. doi: 10.48550/arXiv.2303.15056 – year: 2015 ident: ref-29 article-title: Antiquities Dealer Leonardo Patterson Faces New Criminal Charges. publication-title: The New York Times. – year: 2018 ident: ref-31 article-title: From Canvas to Ashes: Understanding the Implications of the Westfries Museum and Kunsthal Thefts for the Dutch Art World. publication-title: Transnational Crime. doi: 10.4324/9781351026826-8 – year: 2018 ident: ref-36 article-title: Improving language understanding with unsupervised learning. – volume: 56 start-page: 155-74 year: 2011 ident: ref-8 article-title: Supply and Demand: Exposing the Illicit Trade in Cambodian Antiquities through a Study of Sotheby’s Auction House. publication-title: Crime Law Soc Change. doi: 10.1007/s10611-011-9321-6 |
| SSID | ssj0002513619 |
| Score | 2.2473717 |
| Snippet | Background:
There is a wide variety of potential sources from which insight into the antiquities trade could be culled, from newspaper articles to auction... Background: There is a wide variety of potential sources from which insight into the antiquities trade could be culled, from newspaper articles to auction... |
| SourceID | doaj pubmedcentral proquest crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database |
| StartPage | 100 |
| SubjectTerms | antiquities trade eng gpt3 illicit antiquities knowledge graph knowledge graph embedding model Large language models |
| Title | Investigating antiquities trafficking with generative pre-trained transformer (GPT)-3 enabled knowledge graphs: A case study |
| URI | https://www.proquest.com/docview/2858994189 https://pubmed.ncbi.nlm.nih.gov/PMC10445804 https://doaj.org/article/cad22c4a936f4e6184def84eeb788b9c |
| Volume | 3 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2732-5121 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002513619 issn: 2732-5121 databaseCode: DOA dateStart: 20210101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2732-5121 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002513619 issn: 2732-5121 databaseCode: M~E dateStart: 20210101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3BbtQwELWg4sAFtQLEAq2mEgc4hCa2k9jl1FYtHKDqoaCVELJie1z2kpbd7h75F_6UGWdbbU5cuOSQOInlmdjvTTxvhHiTvJayRFUoVKHQEpvCW5UK00WbUpLRZsWbb5_b83MzndqLjVJfvCdskAceBu4gdFHKoDurmqSRy5NETEYjeiJv3gaefcvWbpApnoNp1VZEDdYpwbIhmsfVqDijJwe5OahSqvfVaDXKov0jpDneJ7mx8JxtiydrxAhHQ093xAPsn4o_G_oY_RXQ8Mx-LbM4KtAzWBaCQ-DAUVa4ysrSPK0B7_nIRSEwcrsBsuIc3n68uHxXKMCcSRXhPtIGWdB6cQhHEGi9g6xGC99XQ5QNqg9wg3T_kABzCBKyRvkK449n4uvZ6eXJp2JdbaEIilBAgWhMCq3stG0sEi4LdekrmTRhiJCi8kgsOjaNjrWvkk1SN3VKhhBAUC36oJ6Lrf66xxcCpCISZcqkjO90LcuuKdtExJCgXES09UQc3I26uxlENRyTEbaTG9nJZTu5aiKO2Tj3rVkUO58gV3FrV3H_cpWJ2L8zraOPiP-MdD1eLxdOmpp4p66MnQgzsvnojeMr_exnluMmQqtrU-qX_6OPr8RjLmg_BHlei63b-RJ3xaOwup0t5nviYTs1e9nV6fjl9-lfwR8MxA |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Investigating+antiquities+trafficking+with+generative+pre-trained+transformer+%28GPT%29-3+enabled+knowledge+graphs%3A+A+case+study+%5Bversion+1%3B+peer+review%3A+2+approved%5D&rft.jtitle=Open+research+Europe&rft.au=Shawn+Graham&rft.au=Donna+Yates&rft.au=Ahmed+El-Roby&rft.date=2023&rft.pub=F1000+Research+Ltd&rft.eissn=2732-5121&rft.volume=3&rft_id=info:doi/10.12688%2Fopenreseurope.16003.1&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_cad22c4a936f4e6184def84eeb788b9c |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2732-5121&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2732-5121&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2732-5121&client=summon |