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

ter Horst H, Hartung M, Cimiano P (2018)
In: Reasoning Web. Learning, Uncertainty, Streaming, and Scalability. d'Amato C, Theobald M (Eds); Lecture Notes in Computer Science, 11078. Springer: 78-109.

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Sammelwerksbeitrag | Veröffentlicht | Englisch
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Abstract / Bemerkung
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.
Erscheinungsjahr
Buchtitel
Reasoning Web. Learning, Uncertainty, Streaming, and Scalability.
Band
11078
Seite
78-109
Konferenz
14th International Reasoning Web Summer School 2018
Konferenzort
Luxembourg
Konferenzdatum
2018-09-22 – 2018-09-26
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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.
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.
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.
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.
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.
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).
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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