Sammelbandbeitrag - Artikel | 2018 | Peer reviewed

Cold-Start Knowledge Base Population Using Ontology-Based Information Extraction with Conditional Random Fields

Autor(en)

ter Horst, Hendrik ; Hartung, Matthias ; Cimiano, Philipp

Herausgeber*innen

d'Amato, Claudia ; Theobald, Martin

Titel des Sammelbandes

Reasoning Web. Learning, Uncertainty, Streaming, and Scalability.

Verlag

Springer

Name der Konferenz

14th International Reasoning Web Summer School 2018

ISBN-13

9783030003371

URN

urn:nbn:de:0070-pub-29309529

URL

https://pub.uni-bielefeld.de/record/2930952

Weitere Details

Sprache

englisch

Darstellungsform

Text

Serie

Lecture Notes in Computer Science

Band

11078

Seitenbereich

78 - 109

Schlagwörter

Cold-start Knowledge Base Population , Conditional Random Fields , Ontology-based Information Extraction , Slot Filling

Link zum Volltext (intern)

https://pub.uni-bielefeld.de/record/2930952

DDC

000

In this tutorial we discuss how Conditional Random Fields can be applied to knowledge base population tasks. We are in particular interested in the cold-start setting which assumes as given an ontology that models classes and properties relevant for the domain of interest, and an empty knowledge base that needs to be populated from unstructured
text. More specifically, cold-start knowledge base population consists in predicting semantic structures from an input document that instantiate classes and properties as defined in the ontology. Considering knowledge base population as structure prediction, we frame the task as a statistical inference problem which aims at predicting the most likely assignment to a set of ontologically grounded output variables given an input document. In order to model the conditional distribution of these output variables given the input variables derived from the text, we follow the approach adopted in Conditional Random Fields. We decompose the cold-start knowledge base population task into the specific problems of entity recognition, entity linking and slot-filling, and show how they can be modeled using Conditional Random Fields.

Zitierstile

ter Horst H, Hartung M, Cimiano P. Cold-Start Knowledge Base Population Using Ontology-Based Information Extraction with Conditional Random Fields. In: d'Amato C, Theobald M, eds. Reasoning Web. Learning, Uncertainty, Streaming, and Scalability. Lecture Notes in Computer Science. Vol 11078. Springer; 2018: 78-109.

In die Zwischenablage einfügen

ter Horst, H., Hartung, M., & Cimiano, P. (2018). Cold-Start Knowledge Base Population Using Ontology-Based Information Extraction with Conditional Random Fields. In C. d'Amato & M. Theobald (Eds.), Lecture Notes in Computer Science: Vol. 11078. Reasoning Web. Learning, Uncertainty, Streaming, and Scalability. (pp. 78-109). Springer.

In die Zwischenablage einfügen

ter Horst, Hendrik, Hartung, Matthias, and Cimiano, Philipp. 2018. “Cold-Start Knowledge Base Population Using Ontology-Based Information Extraction with Conditional Random Fields”. In Reasoning Web. Learning, Uncertainty, Streaming, and Scalability., ed. Claudia d'Amato and Martin Theobald, 11078:78-109. Lecture Notes in Computer Science. Springer.

In die Zwischenablage einfügen

ter Horst, H., Hartung, M., and Cimiano, P. (2018). “Cold-Start Knowledge Base Population Using Ontology-Based Information Extraction with Conditional Random Fields” in Reasoning Web. Learning, Uncertainty, Streaming, and Scalability., d'Amato, C., and Theobald, M. eds. Lecture Notes in Computer Science, vol. 11078, (Springer), 78-109.

In die Zwischenablage einfügen

ter Horst, H., Hartung, M., & Cimiano, P., 2018. Cold-Start Knowledge Base Population Using Ontology-Based Information Extraction with Conditional Random Fields. In C. d'Amato & M. Theobald, eds. Reasoning Web. Learning, Uncertainty, Streaming, and Scalability. Lecture Notes in Computer Science. no.11078 Springer, pp. 78-109.

In die Zwischenablage einfügen

H. ter Horst, M. Hartung, and P. Cimiano, “Cold-Start Knowledge Base Population Using Ontology-Based Information Extraction with Conditional Random Fields”, Reasoning Web. Learning, Uncertainty, Streaming, and Scalability., C. d'Amato and M. Theobald, eds., Lecture Notes in Computer Science, vol. 11078, Springer, 2018, pp.78-109.

In die Zwischenablage einfügen

ter Horst, H., Hartung, M., Cimiano, P.: Cold-Start Knowledge Base Population Using Ontology-Based Information Extraction with Conditional Random Fields. In: d'Amato, C. and Theobald, M. (eds.) Reasoning Web. Learning, Uncertainty, Streaming, and Scalability. Lecture Notes in Computer Science. 11078, p. 78-109. Springer (2018).

In die Zwischenablage einfügen

ter Horst, Hendrik, Hartung, Matthias, and Cimiano, Philipp. “Cold-Start Knowledge Base Population Using Ontology-Based Information Extraction with Conditional Random Fields”. Reasoning Web. Learning, Uncertainty, Streaming, and Scalability. Ed. Claudia d'Amato and Martin Theobald. Springer, 2018.Vol. 11078. Lecture Notes in Computer Science. 78-109.

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