nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

A Knowledge Selective Adversarial Network for Link Prediction in Knowledge Graph

verfasst von : Kairong Hu, Hai Liu, Tianyong Hao

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Knowledge Graphs (KGs) contain rich semantic information and are of importance to many downstream tasks. In order to enhance practical utilization of KGs, KG completion task, which is also called link prediction, is a newly emerging hot research topic. During KG embedding model training, negative sampling is a fundamental method for obtaining negative samples. Inspired by an adversarial learning framework KBGAN, this paper proposes a new knowledge selective adversarial network, named as KSGAN, using a knowledge selector for high-quality negative sampling to benefit link prediction. The performances of our model KSGAN are evaluated on three standard knowledge completion datasets: FB15k-237, WN18 and WN18RR. The results show that KSGAN outperforms a list of baseline models on all the datasets, demonstrating the effectiveness of the proposed model.

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 Many vs. Many Query Matching with Hierarchical BERT and Transformer

Nächstes Kapitel Feature-Level Attention Based Sentence Encoding for Neural Relation Extraction

https://github.com/cai-lw/KBGAN.

Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. ACM SIGMOD International Conference on Management of Data, pp. 1247–1250 (2008)

Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRef

Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: International conference on World Wide Web, pp. 697–706 (2007)

Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)CrossRef

Min, B., Grishman, R., Wan, L., Wang, C., Gondek, D.: Distant supervision for relation extraction with an incomplete knowledge base. In: NAACL, pp. 777–782 (2013)

Bordes, A., Usunier, N., Garcia-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)

Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: ACL and IJCNLP, pp. 687–696 (2015)

Yang, B., Yih, W.T., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014)

Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML, pp. 2071–2080 (2016)

10.

Cai, L., Wang, W.Y.: KBGAN: adversarial learning for knowledge graph embeddings. In: NAACL, pp. 1470–1480 (2018)

11.

Wang, P., Li, S., Pan, R.: Incorporating GAN for negative sampling in knowledge representation learning. In: AAAI, pp. 2005–2012 (2018)

12.

Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI, pp. 1112–1119 (2014)

13.

Fan, M., Zhou, Q., Chang, E., Zheng, T.F.: Transition-based knowledge graph embedding with relational mapping properties. In: PACLIC, pp. 328–337 (2014)

14.

Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI, pp. 2181–2187 (2015)

15.

Feng, J., Huang, M., Wang, M., Zhou, M., Hao, Y., Zhu, X.: Knowledge graph embedding by flexible translation. In: KR, pp. 557–560 (2016)

16.

Xiao, H., Huang, M., Zhu, X.: From one point to a manifold: knowledge graph embedding for precise link prediction. In: IJCAI, pp. 1315–1321 (2016)

17.

Tan, Z., Zhao, X., Fang, Y., Xiao, W.: GTrans: generic knowledge graph embedding via multi-state entities and dynamic relation spaces. IEEE Access 6, 8232–8244 (2018)CrossRef

18.

Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: ICML, pp. 809–816 (2011)

19.

Kazemi, S.M., Poole, D.: SimplE embedding for link prediction in knowledge graphs. In: NIPS, pp. 4284–4295 (2018)

20.

Balažević, I., Allen, C., Hospedales, T.M.: TuckER: tensor factorization for knowledge graph completion. arXiv preprint arXiv:1901.09590 (2019)

21.

Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. In: NIPS, pp. 926–934 (2013)

22.

Dong, X., Gabrilovich, E., Heitz, G., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: ACM SIGKDD, pp. 601–610 (2014)

23.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)

24.

Sutton, R.S., McAllester, D.A., Singh, S.P., Mansour, Y.: Policy gradient methods for reinforcement learning with function approximation. In: NIPS, pp. 1057–1063 (2000)

25.

Toutanova, K., Chen, D., Pantel, P., et al.: Representing text for joint embedding of text and knowledge bases. In: EMNLP, pp. 1499–1509 (2015)

26.

Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: AAAI, pp. 1811–1818 (2018)

27.

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

Titel: A Knowledge Selective Adversarial Network for Link Prediction in Knowledge Graph
verfasst von: Kairong Hu
Hai Liu
Tianyong Hao
Verlag: Springer International Publishing
Buch: Natural Language Processing and Chinese Computing
Print ISBN: 978-3-030-32232-8

Electronic ISBN: 978-3-030-32233-5

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-32233-5_14

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"