ReLink: Open Information Extraction by Linking Phrases and Its Applications

2020 ◽  
pp. 44-62
Author(s):  
Xuan-Chien Tran ◽  
Le-Minh Nguyen
Keyword(s):  
Information Extraction ◽  
Open Information Extraction

Co-Clustering Triples from Open Information Extraction

2020 ◽  
Author(s):  
Koninika Pal ◽  
Vinh Thinh Ho ◽  
Gerhard Weikum
Keyword(s):  
Information Extraction ◽  
Open Information Extraction

Open Information Extraction from Texts: Part II. Extraction of Semantic Relationships Using Unsupervised Machine Learning

2020 ◽  
Vol 47 (6) ◽  
pp. 340-347
Author(s):  
A. O. Shelmanov ◽  
D. A. Devyatkin ◽  
V. A. Isakov ◽  
I. V. Smirnov
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
Unsupervised Machine Learning ◽  
Semantic Relationships ◽  
Open Information Extraction

Discovering Relational Phrases for Qualia Roles Through Open Information Extraction

2017 ◽  
pp. 69-84
Author(s):  
Giovanni Siragusa ◽  
Valentina Leone ◽  
Luigi Di Caro ◽  
Claudio Schifanella
Keyword(s):  
Information Extraction ◽  
Open Information Extraction

InferPortOIE: A Portuguese Open Information Extraction system with inferences

2018 ◽  
Vol 25 (2) ◽  
pp. 287-306 ◽  
Author(s):  
Cleiton Fernando Lima Sena ◽  
Daniela Barreiro Claro
Keyword(s):  
Natural Language ◽  
Information Extraction ◽  
Digital Data ◽  
Extraction System ◽  
Open Information Extraction ◽  
The Web ◽  
Information Extraction System

AbstractNowadays, there is an increasing amount of digital data. In the case of the Web, daily, a vast collection of data is generated, whose contents are heterogeneous. A significant portion of this data is available in a natural language format. Open Information Extraction (Open IE) enables the extraction of facts from large quantities of texts written in natural language. In this work, we propose an Open IE method to extract facts from texts written in Portuguese. We developed two new rules that generalize the inference by transitivity and by symmetry. Consequently, this approach increases the number of implicit facts in a sentence. Our novel symmetric inference approach is based on a list of symmetric features. Our results confirmed that our method outstands close works both in precision and number of valid extractions. Considering the number of minimal facts, our approach is equivalent to the most relevant methods in the literature.

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