nach oben

Erschienen in:

2025 | OriginalPaper | Buchkapitel

DISCIE–Discriminative Closed Information Extraction

verfasst von : Cedric Möller, Ricardo Usbeck

Erschienen in: The Semantic Web – ISWC 2024

Verlag: Springer Nature Switzerland

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This paper introduces a novel method for closed information extraction. The method employs a discriminative approach that incorporates type and entity-specific information to improve relation extraction accuracy, particularly benefiting long-tail relations. Notably, this method demonstrates superior performance compared to state-of-the-art end-to-end generative models. This is especially evident for the problem of large-scale closed information extraction where we are confronted with millions of entities and hundreds of relations. Furthermore, we emphasize the efficiency aspect by leveraging smaller models. In particular, the integration of type-information proves instrumental in achieving performance levels on par with or surpassing those of a larger generative model. This advancement holds promise for more accurate and efficient information extraction techniques.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel AdaptLIL: A Real-Time Adaptive Linked Indented List Visualization for Ontology Mapping

Nächstes Kapitel Expanding the Scope: Inductive Knowledge Graph Reasoning with Multi-starting Progressive Propagation

Using QIDs and PIDs from www.wikidata.org. QIDs are the identifiers of entities and PIDs are the identifiers of relations.

Relations rarely occurring.

The code can be found in: https://github.com/semantic-systems/discie.

Usually, mention recognition is solved by applying BIO sequence tagging. We trained and evaluated such a method but achieved a lower performance in comparison to the token-pair-based approach described above.

This could be replaced with any other KG containing descriptions.

https://faiss.ai.

This was also observable in our use case.

930 types are used in total. They were filtered by exploring how useful they are for disambiguating between different entities.

When evaluating on GeoNRE or WikipediaNRE, we limited the set of available predictable relations and entities to the same set as used in the work by Josifoski et al. [12]. Therefore, we set prediction scores for out-of-scope relations to 0.0.

We did not compare to SCICERO [8] as we were not able to adapt their code to our datasets.

Hence putting more emphasis on recall.

They occur only rarely in the training data.

GenIE takes a long time to evaluate on the other datasets on a single GPU. Therefore we opted for only running the efficiency tests on the smallest dataset. While the average speed differs between the datasets, DISCIE was considerably faster for all of them.

Angeli, G., et al.: Bootstrapped self training for knowledge base population. In: Proceedings of the 2015 Text Analysis Conference, TAC 2015, Gaithersburg, Maryland, USA, November 16-17, 2015, 2015. NIST (2015). https://tac.nist.gov/publications/2015/participant.papers/TAC2015.Stanford.proceedings.pdf

Ayoola, T., Tyagi, S., Fisher, J., Christodoulopoulos, C., Pierleoni, A.: Refined: An efficient zero-shot-capable approach to end-to-end entity linking. In: Loukina, A., Gangadharaiah, R., Min, B. (eds.) Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, NAACL 2022, Hybrid: Seattle, Washington, USA + Online, C41555 July 2022, pp. 209–220. Association for Computational Linguistics (2022). https://doi.org/10.18653/v1/2022.naacl-industry.24

Cabot, P.H., Navigli, R.: REBEL: relation extraction by end-to-end language generation. In: Moens, M., Huang, X., Specia, L., Yih, S.W. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 16-20 November 2021, pp. 2370–2381. Association for Computational Linguistics (2021). https://doi.org/10.18653/V1/2021.FINDINGS-EMNLP.204

Cao, N.D., Izacard, G., Riedel, S., Petroni, F.: Autoregressive entity retrieval. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3-7 May 2021. OpenReview.net (2021). https://openreview.net/forum?id=5k8F6UU39V

Cao, N.D., et al.: Multilingual autoregressive entity linking. Trans. Assoc. Comput. Linguistics 10, 274–290 (2022). https://doi.org/10.1162/tacl_a_00460

Chaganty, A.T., Paranjape, A., Liang, P., Manning, C.D.: Importance sampling for unbiased on-demand evaluation of knowledge base population. In: Palmer, M., Hwa, R., Riedel, S. (eds.) Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9-11 September 2017. pp. 1038–1048. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/d17-1109

Chicco, D.: Siamese neural networks: an overview. Artifi. Neural Netw., 73–94 (2021)

Dessì, D., Osborne, F., Recupero, D.R., Buscaldi, D., Motta, E.: SCICERO: a deep learning and NLP approach for generating scientific knowledge graphs in the computer science domain. Knowl. Based Syst. 258, 109945 (2022). https://doi.org/10.1016/J.KNOSYS.2022.109945

Galárraga, L., Heitz, G., Murphy, K., Suchanek, F.M.: Canonicalizing open knowledge bases. In: Li, J., Wang, X.S., Garofalakis, M.N., Soboroff, I., Suel, T., Wang, M. (eds.) Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, 3-7 November 2014, pp. 1679–1688. ACM (2014). https://doi.org/10.1145/2661829.2662073

10.

Han, X., et al.: Fewrel: a large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31 October - 4 November 2018, pp. 4803–4809. Association for Computational Linguistics (2018). https://doi.org/10.18653/V1/D18-1514, https://doi.org/10.18653/v1/d18-1514

11.

Ji, S., Pan, S., Cambria, E., Marttinen, P., Yu, P.S.: A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Networks Learn. Syst. 33(2), 494–514 (2022). https://doi.org/10.1109/TNNLS.2021.3070843

12.

Josifoski, M., Cao, N.D., Peyrard, M., Petroni, F., West, R.: Genie: Generative information extraction. In: Carpuat, M., de Marneffe, M., Ruíz, I.V.M. (eds.) Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, 10-15 July 2022, pp. 4626–4643. Association for Computational Linguistics (2022). https://doi.org/10.18653/v1/2022.naacl-main.342

13.

Josifoski, M., Sakota, M., Peyrard, M., West, R.: Exploiting asymmetry for synthetic training data generation: Synthie and the case of information extraction. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, 6-10 December 2023. pp. 1555–1574. Association for Computational Linguistics (2023). https://doi.org/10.18653/V1/2023.EMNLP-MAIN.96

14.

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., Zettlemoyer, L.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, J5-10 July 2020, pp. 7871–7880. Association for Computational Linguistics (2020). https://doi.org/10.18653/V1/2020.ACL-MAIN.703

15.

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., Zettlemoyer, L.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J.R. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, 5-10 July 2020, pp. 7871–7880. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.acl-main.703

16.

Liu, Y., Zhang, T., Liang, Z., Ji, H., McGuinness, D.L.: Seq2rdf: an end-to-end application for deriving triples from natural language text. In: van Erp, M., Atre, M., López, V., Srinivas, K., Fortuna, C. (eds.) Proceedings of the ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, USA, 8th - to -12th October 2018. CEUR Workshop Proceedings, vol. 2180. CEUR-WS.org (2018). https://ceur-ws.org/Vol-2180/paper-37.pdf

17.

Logeswaran, L., Chang, M., Lee, K., Toutanova, K., Devlin, J., Lee, H.: Zero-shot entity linking by reading entity descriptions. In: Korhonen, A., Traum, D.R., Màrquez, L. (eds.) Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, 28 July - 2 August 2019, Volume 1: Long Papers, pp. 3449–3460. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/p19-1335

18.

Ma, Y., Wang, A., Okazaki, N.: DREEAM: guiding attention with evidence for improving document-level relation extraction. In: Vlachos, A., Augenstein, I. (eds.) Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023, Dubrovnik, Croatia, 2-6 May 2023, pp. 1963–1975. Association for Computational Linguistics (2023). https://doi.org/10.18653/V1/2023.EACL-MAIN.145

19.

Miwa, M., Bansal, M.: End-to-end relation extraction using lstms on sequences and tree structures. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, 7-12 August 2016, Berlin, Germany, Volume 1: Long Papers. The Association for Computer Linguistics (2016). https://doi.org/10.18653/v1/p16-1105

20.

Möller, C., Lehmann, J., Usbeck, R.: Survey on english entity linking on wikidata: datasets and approaches. Semantic Web 13(6), 925–966 (2022). https://doi.org/10.3233/SW-212865

21.

Nguyen, T.H., Grishman, R.: Relation extraction: perspective from convolutional neural networks. In: Blunsom, P., Cohen, S.B., Dhillon, P.S., Liang, P. (eds.) Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, VS@NAACL-HLT 2015, June 5, 2015, Denver, Colorado, USA. pp. 39–48. The Association for Computational Linguistics (2015). https://doi.org/10.3115/v1/w15-1506

22.

Ni, J., Florian, R.: Neural cross-lingual relation extraction based on bilingual word embedding mapping. In: Inui, K., Jiang, J., Ng, V., Wan, X. (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 2019, Hong Kong, China, 3-7 November 2019, pp. 399–409. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/D19-1038

23.

Ni, J., Rossiello, G., Gliozzo, A., Florian, R.: A generative model for relation extraction and classification. CoRR abs/ arXiv: 2202.13229 (2022)

24.

OpenAI: GPT-4 technical report. CoRR abs/ arXiv: 2303.08774 (2023)

25.

Paolini, G., et al.: Structured prediction as translation between augmented natural languages. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3-7 May 2021. OpenReview.net (2021). https://openreview.net/forum?id=US-TP-xnXI

26.

Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 140:1–140:67 (2020). http://jmlr.org/papers/v21/20-074.html

27.

Raiman, J.: Deeptype 2: Superhuman entity linking, all you need is type interactions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 8028–8035 (2022)

28.

Raiman, J., Raiman, O.: Deeptype: multilingual entity linking by neural type system evolution. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

29.

dos Santos, C.N., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, 26-31 July 2015, Beijing, China, Volume 1: Long Papers, pp. 626–634. The Association for Computer Linguistics (2015). https://doi.org/10.3115/v1/p15-1061

30.

Shavarani, H., Sarkar, A.: Spel: Structured prediction for entity linking. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, 6-10 December 2023, pp. 11123–11137. Association for Computational Linguistics (2023). https://doi.org/10.18653/V1/2023.EMNLP-MAIN.686

31.

Soares, L.B., FitzGerald, N., Ling, J., Kwiatkowski, T.: Matching the blanks: Distributional similarity for relation learning. In: Korhonen, A., Traum, D.R., Màrquez, L. (eds.) Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, 28 July - 2 August 2019, Volume 1: Long Papers, pp. 2895–2905. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/p19-1279

32.

Sui, D., Wang, C., Chen, Y., Liu, K., Zhao, J., Bi, W.: Set generation networks for end-to-end knowledge base population. In: Moens, M., Huang, X., Specia, L., Yih, S.W. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, pp. 9650–9660. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.emnlp-main.760

33.

Touvron, H., et al.: Llama: Open and efficient foundation language models. CoRR abs/ arXiv: 2302.13971 (2023)

34.

Trisedya, B.D., Weikum, G., Qi, J., Zhang, R.: Neural relation extraction for knowledge base enrichment. In: Korhonen, A., Traum, D.R., Màrquez, L. (eds.) Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, 28 July - 2 August 2019, Volume 1: Long Papers, pp. 229–240. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/p19-1023

35.

Wu, L., Petroni, F., Josifoski, M., Riedel, S., Zettlemoyer, L.: 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 2020, Online, 16-20 November 2020. pp. 6397–6407. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.emnlp-main.519

36.

Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: Hajic, J., Tsujii, J. (eds.) COLING 2014, 25th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 23-29 August 2014, Dublin, Ireland, pp. 2335–2344. ACL (2014), https://aclanthology.org/C14-1220/

37.

Zhang, R.H., Liu, Q., Fan, A.X., Ji, H., Zeng, D., Cheng, F., Kawahara, D., Kurohashi, S.: Minimize exposure bias of seq2seq models in joint entity and relation extraction. In: Cohn, T., He, Y., Liu, Y. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020. Findings of ACL, vol. EMNLP 2020, pp. 236–246. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.findings-emnlp.23

38.

Zhang, S., Ng, P., Wang, Z., Xiang, B.: Reknow: Enhanced knowledge for joint entity and relation extraction. CoRR abs/ arXiv: 2206.05123 (2022)

39.

Zhong, Z., Chen, D.: A frustratingly easy approach for entity and relation extraction. In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tür, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y. (eds.) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, 6-11 June 2021, pp. 50–61. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.naacl-main.5

Titel: DISCIE–Discriminative Closed Information Extraction
verfasst von: Cedric Möller
Ricardo Usbeck
Verlag: Springer Nature Switzerland
Buch: The Semantic Web – ISWC 2024
Print ISBN: 978-3-031-77849-0

Electronic ISBN: 978-3-031-77850-6

Copyright-Jahr: 2025
DOI: https://doi.org/10.1007/978-3-031-77850-6_2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"