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
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
World Wide Web
Erschienen in: World Wide Web 2/2020

03.01.2020

A relationship extraction method for domain knowledge graph construction

verfasst von: Haoze Yu, Haisheng Li, Dianhui Mao, Qiang Cai

Erschienen in: World Wide Web | Ausgabe 2/2020

Einloggen

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

search-config
loading …

Abstract

As a semantic knowledge base, knowledge graph is a powerful tool for managing large-scale knowledge consists with instances, concepts and relationships between them. In view that the existing domain knowledge graphs can not obtain relationships in various structures through targeted approaches in the process of construction which resulting in insufficient knowledge utilization, this paper proposes a relationship extraction method for domain knowledge graph construction. We obtain upper and lower relationships from structured data in the classification system of network encyclopedia and semi-structured data in the classification labels of web pages, and non-superordinate relationships are extracted from unstructured text through the proposed convolution residual network based on improved cross-entropy loss function. We verify the effectiveness of the designed method by comparing with existing relationship extraction methods and constructing a food domain knowledge graph.
Vorheriger Artikel Detecting the evolving community structure in dynamic social networks
Nächster Artikel Cluster query: a new query pattern on temporal knowledge graph

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

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

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
Literatur
1.
Agichtein E, Gravano L, et al. Snowball: extracting relations from large plain-text collections[J]. 2000:85–94
2.
Amarilli A, Galárraga L, Preda N, et al. Recent Topics of Research around the YAGO Knowledge Base [M]. Web Technologies and Applications. Springer International Publishing, 2014: 1–12
3.
Cohen W, Cohen W. Personalized recommendations using knowledge graphs: a probabilistic logic programming approach[C]. ACM Conf. Recommend. Syst. ACM, 2016:325–332
4.
Gupta M, Bendersky M. Information Retrieval with Verbose Queries[C]. International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2015:1121–1124
5.
Hasegawa T, Sekine S, Grishman R. Discovering relations among named entities from large corpora[C]. Meeting on Association for Computational Linguistics. Association for Computational Linguistics. 2004:415–422
6.
Hoffmann R, Zhang C, Ling X, et al. Knowledge-based weak supervision for information extraction of overlapping relations[C]. The, Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 19–24 June, 2011, Portland, Oregon, USA. DBLP, 2011:541–550
7.
Huang Y Y, Wang W Y. Deep residual learning for weakly-supervised relation extraction[J]. arXiv preprint arXiv:1707.08866, 2017
8.
9.
Laina I, Rupprecht C, Belagiannis V, et al. Deeper depth prediction with fully convolutional residual networks[C]//2016 fourth international conference on 3D vision (3DV). IEEE, 2016: 239–248
10.
Lee Y H, Koh J L. Conditional relationship extraction for diseases and symptoms by a web search-based approach[C]//2018 IEEE/WIC/ACM international conference on web intelligence (WI). IEEE, 2018: 554–561
11.
Lehmann J. DBpedia: A Nucleus for a Web of Open Data[C]. The Semantic Web, International Semantic Web Conference, Asian Semantic Web Conference, ISWC 2007 + Aswc 2007, Busan, Korea, November. 2007:11--15
12.
Liang C, Ye J, Wu Z, et al. Recovering Concept Prerequisite Relations from University Course Dependencies[C]//Thirty-First AAAI Conference on Artificial Intelligence. 2017
13.
Lin Y, Liu Z, Zhu X, et al. Learning entity and relation embeddings for knowledge graph completion[C]. Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI Press, 2015:2181–2187
14.
Lin Y, Shen S, Liu Z, et al. Neural relation extraction with selective attention over instances[C]// ACL 2016. Berlin: [s. n.]. 2016: 2124–2133
15.
Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proc. IEEE Int. Conf. Comput. Vision. 2017: 2980–2988
16.
Lv Q, Xu L, Yu J, et al. Research on domain knowledge graph based on the large scale online knowledge fragment[C]. Adv. Res. Technol. Indust. Appl. IEEE, 2014:312–315
17.
18.
Mintz M, Bills S, Snow R, et al. Distant supervision for relation extraction without labeled data[C]. Joint Conference of the, Meeting of the ACL and the, International Joint Conference on Natural Language Processing of the Afnlp: Volume. Association for Computational Linguistics, 2009:1003–1011
19.
Miwa M, Bansal M. End-to-end relation extraction using LSTMs on sequences and tree structures[C]. Meeting of the Association for Computational Linguistics. 2016:1105–1116
20.
Necesal P, Pospıšil J. Experience with teaching mathematics for engineers with the aid of Wolfram Alpha[C]//Proc. World Cong. Eng. Comput. Sci. 2012, 1: 271–274
21.
Ningthoujam D, Yadav S, Bhattacharyya P, et al. Relation extraction between the clinical entities based on the shortest dependency path based LSTM[J]. arXiv preprint arXiv:1903.09941, 2019
22.
Pan L, Li C, Li J, et al. Prerequisite relation learning for concepts in moocs[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017: 1447–1456
23.
Pantel P, Pennacchiotti M. Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations.[C]. The, International Conference on Computational Linguistics and, Meeting of the ACL. 2006:113–120
24.
Pennington J, Socher R, Manning C. Glove: Global vectors for word representation[C]//Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014: 1532–1543
25.
Ravichandran D, Hovy E. Lerning surface text patterns for a question answering system[C]. Meeting of the Association for Computational Linguistics, Proceedings of the Conference. 2013:41–47
26.
Riedel S, Yao L, McCallum A. Modeling Relations and their Mentions without Labeled Text[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, Heidelberg, 2010: 148–163CrossRef
27.
Sanderson M, Croft B. Deriving Concept Hierarchies from Text[R]. Massachusetts Univ Amherst Dept of Computer Science, 2005
28.
Socher R, Huval B, Manning CD, et al. Semantic compositionality through recursive matrix-vector spaces[C]. Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2012:1201–1211
29.
Srivastava, N., Hinton, G., Krizhevsky, A., et al.: Dropout: a simple way to prevent neural networks from overfitting[J]. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetMATH
30.
Sudo K, Sekine S, Grishman R. An improved extraction pattern representation model for automatic IE pattern acquisition[C]. Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2003:224–231
31.
Surdeanu M, Tibshirani J, Nallapati R, et al. Multi-instance multi-label learning for relation extraction[C]. Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2012:455–465
32.
Wang Z, Huang J, Li H, et al. Probase: a Universal Knowledge Base for Semantic Search[J]. 2004
33.
Wang S, Ororbia A, Wu Z, et al. Using prerequisites to extract concept maps fromtextbooks[C]//proceedings of the 25th acm international on conference on information and knowledge management. ACM, 2016: 317–326
34.
Yih W T, Chang M W, He X, et al. Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base[C]. Meeting of the Association for Computational Linguistics and the, International Joint Conference on Natural Language Processing. 2015:1321–1331
35.
Zeng D, Liu K, Lai S, et al. Relation classification via convolutional deep neural network[C]. Int. Conf. Comput. Linguist. 2014:2335–2344
36.
Zeng D, Liu K, Chen Y, et al. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks[C]// EMNLP 2015. Lisbon: [s. n.]. 2015: 1753-1762
37.
Zhang M, Su J, Wang D, et al. Discovering Relations Between Named Entities from a Large Raw Corpus Using Tree Similarity-Based Clustering[M]. Natural Language Processing – IJCNLP 2005. Springer Berlin Heidelberg, 2005:378–389
Metadaten
Titel
A relationship extraction method for domain knowledge graph construction
verfasst von
Haoze Yu
Haisheng Li
Dianhui Mao
Qiang Cai
Publikationsdatum
03.01.2020
Verlag
Springer US
Erschienen in
World Wide Web / Ausgabe 2/2020
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-019-00765-y

Weitere Artikel der Ausgabe 2/2020

World Wide Web 2/2020 Zur Ausgabe

OriginalPaper

A multiview learning method for malware threat hunting: windows, IoT and android as case studies

OriginalPaper

Matching of social events and users: a two-way selection perspective

OriginalPaper

TBI2Flow: Travel behavioral inertia based long-term taxi passenger flow prediction

OriginalPaper

Privacy-preserving conjunctive keyword search on encrypted data with enhanced fine-grained access control

EditorialNotes

Editorial: Special issue on security and privacy in network computing

OriginalPaper

Towards k-vertex connected component discovery from large networks

Premium Partner

    Bildnachweise