Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Entity-Centric Topic Extraction and Exploration: A Network-Based Approach Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Discriminative Path-Based Knowledge Graph Embedding for Precise Link Prediction

verfasst von : Maoyuan Zhang, Qi Wang, Wukui Xu, Wei Li, Shuyuan Sun

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

Representation learning of knowledge graph aims to transform both the entities and relations into continuous low-dimensional vector space. Though there have been a variety of models for knowledge graph embedding, most existing latent-based models merely explain triples via latent features, while supplementary rich inference patterns hidden in the observed graph features have not been fully employed. For this reason, in this paper we propose the discriminative path-based embedding model (DPTransE) which jointly learns from the latent features and graph features. Our model builds interactions between these two features, and uses the graph features as the crucial prior to offer precise and discriminative embedding. Experimental results demonstrate that our method outperforms other baselines on the task of link prediction and entity classification.

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 On the Reproducibility and Generalisation of the Linear Transformation of Word Embeddings
Nächstes Kapitel Spherical Paragraph Model
Fußnoten
1
The original relations are
(1) \(/film/film\_crewmember/films\_crewed./film/film\_crew\_gig/film\),
(2) \(/award/award\_nominee/award\_nominations./award/award\_nomination/nominated\_for\), here we use a wildcard \( * \) to reduce occupation without ambiguous expression.
 
2
The original relation path is \(/award/award\_winner/awards\_won./award/award\_honor/award\) \( -> \) \(/award/award\_category/nominees./award/award\_nomination/nominated\_for\).
 
Literatur
1.
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modelling multi-relational data. In: Proceedings of NIPS, pp. 2787–2795 (2013)
2.
Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Proceedings of AAAI (2011)
3.
García-Durán, A., Bordes, A., Usunier, N.: Composing relationships with translations. In: Proceedings of EMNLP, pp. 286–290 (2015)
4.
Guo, S., Wang, Q., Wang, B., Wang, L., Guo, L.: Semantically smooth knowledge graph embedding. In: Proceedings of ACL, pp. 84–94 (2015)
5.
He, S., Liu, K., Ji, G., Zhao, J.: Learning to represent knowledge graphs with Gaussian embedding. In: Proceedings of CIKM, pp. 623–632 (2015)
6.
Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of ACL, pp. 687–696 (2015)
7.
Fan, M., Zhou, Q., Chang, E., Zheng, T.F.: Transition-based knowledge graph embedding with relational mapping properties. In: Proceedings of PACLIC, pp. 328–337 (2014)
8.
Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn. 81, 53–67 (2010)MathSciNetCrossRef
9.
Lao, N., Mitchell, T., Cohen, W.W.: Random walk inference and learning in a large scale knowledge base. In: Proceedings of EMNLP, pp. 529–539 (2011)
10.
Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. In: Proceedings of EMNLP, pp. 705–714 (2011)
11.
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI, pp. 2181–2187 (2015)
12.
Neelakantan, A.: Compositional vector space models for knowledge base inference. In: Proceedings of AAAI, pp. 1–16 (2015)
13.
Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. In: Proceedings of the IEEE, vol. 104, pp. 11–33 (2015)
14.
Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs Nickel. In: Proceedings of AAAI, pp. 1955–1961 (2016)
15.
Shi, B., Weninger, T.: Fact checking in large knowledge graphs - a discriminative predicate path mining approach. Knowl.-Based Syst. 104, 123–133 (2015)CrossRef
16.
Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: Proceedings of International Conference on Intelligent Control & Information Processing, pp. 464–469 (2015)
17.
Szumlanski, S., Gomez, F.: Automatically acquiring a semantic network of related concepts. In: Proceedings of CIKM, pp. 19–28 (2015)
18.
Toutanova, K., Chen, D.: Observed versus latent features for knowledge base and text inference. In: The Workshop on Continuous Vector Space Models and their Compositionality (2015)
19.
Wang, Q., Liu, J., Luo, Y., Wang, B., Lin, C.Y.: Knowledge base completion via coupled path ranking. In: Proceedings of ACL, pp. 1308–1318 (2016)
20.
Wang, Z., Zhang, J., Feng, J., and Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of AAAI (2014)
21.
Xiao, H., Huang, M., Zhu, X.: From one point to a manifold: knowledge graph embedding for precise link prediction. In: Proceedings of IJCAI, pp. 1315–1321 (2016)
22.
Xiao, H., Huang, M., Zhu, X.: TransG: a generative model for knowledge graph embedding. In: Proceedings of ACL, pp. 2316–2325 (2016)
23.
Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71, 623–630 (2009)CrossRefMATH
24.
Cai, H., Zheng, V.W., Chang, C.C.: A comprehensive survey of graph embedding: problems, techniques and applications. In: IEEE Transactions on Knowledge and Data Engineering (2017)
25.
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of SIGMOD, pp. 1247–1250 (2008)
26.
Mille, G.A.: Wordnet: a lexical database for English. In: Communications of the ACM, vol. 38(11), pp. 39–41 (1995)
Metadaten
Titel
Discriminative Path-Based Knowledge Graph Embedding for Precise Link Prediction
verfasst von
Maoyuan Zhang
Qi Wang
Wukui Xu
Wei Li
Shuyuan Sun
Copyright-Jahr
2018
Buch
Advances in Information Retrieval

Print ISBN: 978-3-319-76940-0

Electronic ISBN: 978-3-319-76941-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-76941-7_21

Neuer Inhalt

    Bildnachweise