Using FRED for Named Entity Resolution, Linking and Typing for Knowledge Base Population

2015 ◽  
pp. 40-50 ◽  
Author(s):  
Sergio Consoli ◽  
Diego Reforgiato Recupero
Keyword(s):  
Knowledge Base ◽  
Entity Resolution ◽  
Base Population ◽  
Named Entity ◽  
Knowledge Base Population

WikiIdRank++: EXTENSIONS AND IMPROVEMENTS OF THE WikiIdRank SYSTEM FOR ENTITY LINKING

2013 ◽  
Vol 22 (03) ◽  
pp. 1350018 ◽  
Author(s):  
M. D. JIMÉNEZ ◽  
N. FERNÁNDEZ ◽  
J. ARIAS FISTEUS ◽  
L. SÁNCHEZ
Keyword(s):  
Knowledge Base ◽  
Text Analysis ◽  
Base Population ◽  
Entity Linking ◽  
Named Entities ◽  
Named Entity ◽  
Global Comparison ◽  
Knowledge Base Population ◽  
Reference Knowledge ◽  
Unstructured Information

The amount of information available on the Web has grown considerably in recent years, leading to the need to structure it in order to access it in a quick and accurate way. In order to develop techniques to automate the structuring process, the Knowledge Base Population (KBP) track of the Text Analysis Conference (TAC) was created. This forum aims to encourage research in automated systems capable of capturing knowledge in unstructured information. One of the tasks proposed in the context of the KBP track is named entity linking, and its goal is to link named entities mentioned in a document to instances in a reference knowledge base built from Wikipedia. This paper focuses on the entity linking task in the context of KBP 2010, where two different varieties of this task were considered, depending on whether the use of the text from Wikipedia was allowed or not. Specifically, the paper proposes a set of modifications to a system that participated in KBP 2010, named WikiIdRank, in order to improve its performance. The different modifications were evaluated in the official KBP 2010 corpus, showing that the best combination increases the accuracy of the initial system in a 7.04%. Though the resultant system, named WikiIdRank++, is unsupervised and does not take advantage of Wikipedia text, a comparison with other approaches in KBP indicates that the system would rank as 4th (out of 16) in the global comparison, outperforming other approaches that use human supervision and take advantage of Wikipedia textual contents. Furthermore, the system would rank as 1st in the category of systems that do not use Wikipedia text.

Tackling representation, annotation and classification challenges for temporal knowledge base population

2013 ◽  
Vol 41 (3) ◽  
pp. 611-646 ◽  
Author(s):  
Heng Ji ◽  
Taylor Cassidy ◽  
Qi Li ◽  
Suzanne Tamang
Keyword(s):  
Knowledge Base ◽  
Base Population ◽  
Knowledge Base Population

scholarly journals Distantly supervised Web relation extraction for knowledge base population

Semantic Web ◽  
2016 ◽  
Vol 7 (4) ◽  
pp. 335-349 ◽  
Author(s):  
Isabelle Augenstein ◽  
Diana Maynard ◽  
Fabio Ciravegna
Keyword(s):  
Knowledge Base ◽  
Relation Extraction ◽  
Base Population ◽  
Knowledge Base Population

scholarly journals Importance sampling for unbiased on-demand evaluation of knowledge base population

2017 ◽  
Author(s):  
Arun Chaganty ◽  
Ashwin Paranjape ◽  
Percy Liang ◽  
Christopher D. Manning
Keyword(s):  
Knowledge Base ◽  
Importance Sampling ◽  
Base Population ◽  
On Demand ◽  
Knowledge Base Population

Stacked Ensembles of Information Extractors for Knowledge-Base Population

2015 ◽  
Author(s):  
Vidhoon Viswanathan ◽  
Nazneen Fatema Rajani ◽  
Yinon Bentor ◽  
Raymond Mooney
Keyword(s):  
Knowledge Base ◽  
Base Population ◽  
Knowledge Base Population
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