A Novel Online Encyclopedia-Oriented Approach for Large-Scale Knowledge Base Construction

2014 ◽  
Vol 9 (2) ◽  
Author(s):  
Ting Wang ◽  
Ruihua Di ◽  
Jicheng Song
Keyword(s):  
Knowledge Base ◽  
Large Scale ◽  
Knowledge Base Construction ◽  
Online Encyclopedia ◽  
Oriented Approach

Research on Measuring Semantic Correlation Based on the Wikipedia Hyperlink Network

2013 ◽  
Vol 1 (3) ◽  
pp. 1-11 ◽  
Author(s):  
Feiyue Ye ◽  
Feng Zhang
Keyword(s):  
Knowledge Base ◽  
Large Scale ◽  
Semantic Information ◽  
Semantic Relatedness ◽  
New Method ◽  
Test Set ◽  
Semantic Correlation ◽  
Calculation Results ◽  
New Ideas ◽  
Online Encyclopedia

As a free online encyclopedia with a large-scale of knowledge coverage, rich semantic information and quick update speed, Wikipedia brings new ideas to measure semantic correlation. In this paper, the authors present a new method for measuring the semantic correlation between words by mining rich semantic information that exists in Wikipedia. Unlike the previous methods that calculate semantic relatedness merely based on the page network or the category network, the authors' method not only takes into account the semantic information of the page network, it also combines the semantic information of the category network and it improves the accuracy of the results. Besides this, the authors analyze and evaluate the algorithm by comparing the calculation results with famous knowledge base (e.g., Hownet) and traditional methods based on Wikipedia on the same test set and prove its superiority.

scholarly journals Taxonomy Extraction for Customer Service Knowledge Base Construction

2019 ◽  
pp. 175-190
Author(s):  
Bianca Pereira ◽  
Cecile Robin ◽  
Tobias Daudert ◽  
John P. McCrae ◽  
Pranab Mohanty ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Customer Service ◽  
Knowledge Base Construction

Constructing and Utilizing Video Ontology for Accurate and Fast Retrieval

2011 ◽  
Vol 2 (4) ◽  
pp. 59-75 ◽  
Author(s):  
Kimiaki Shirahama ◽  
Kuniaki Uehara
Keyword(s):  
Knowledge Base ◽  
Large Scale ◽  
Video Retrieval ◽  
Computational Cost ◽  
Semantic Content ◽  
Video Data ◽  
Experimental Results ◽  
Huge Number ◽  
Dempster Shafer Theory ◽  
Shafer Theory

This paper examines video retrieval based on Query-By-Example (QBE) approach, where shots relevant to a query are retrieved from large-scale video data based on their similarity to example shots. This involves two crucial problems: The first is that similarity in features does not necessarily imply similarity in semantic content. The second problem is an expensive computational cost to compute the similarity of a huge number of shots to example shots. The authors have developed a method that can filter a large number of shots irrelevant to a query, based on a video ontology that is knowledge base about concepts displayed in a shot. The method utilizes various concept relationships (e.g., generalization/specialization, sibling, part-of, and co-occurrence) defined in the video ontology. In addition, although the video ontology assumes that shots are accurately annotated with concepts, accurate annotation is difficult due to the diversity of forms and appearances of the concepts. Dempster-Shafer theory is used to account the uncertainty in determining the relevance of a shot based on inaccurate annotation of this shot. Experimental results on TRECVID 2009 video data validate the effectiveness of the method.

Predicting Lexical Answer Types in Open Domain QA

2012 ◽  
Vol 8 (3) ◽  
pp. 74-88 ◽  
Author(s):  
Alfio Massimiliano Gliozzo ◽  
Aditya Kalyanpur
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Large Scale ◽  
Question Answering ◽  
General Purpose ◽  
Open Domain ◽  
Lexical Knowledge ◽  
Research Challenge ◽  
Lexical Knowledge Base

Automatic open-domain Question Answering has been a long standing research challenge in the AI community. IBM Research undertook this challenge with the design of the DeepQA architecture and the implementation of Watson. This paper addresses a specific subtask of Deep QA, consisting of predicting the Lexical Answer Type (LAT) of a question. Our approach is completely unsupervised and is based on PRISMATIC, a large-scale lexical knowledge base automatically extracted from a Web corpus. Experiments on the Jeopardy! data shows that it is possible to correctly predict the LAT in a substantial number of questions. This approach can be used for general purpose knowledge acquisition tasks such as frame induction from text.

scholarly journals Effective supra-classifiers for knowledge base construction

1999 ◽  
Vol 20 (11-13) ◽  
pp. 1347-1352 ◽  
Author(s):  
Kurt D Bollacker ◽  
Joydeep Ghosh
Keyword(s):  
Knowledge Base ◽  
Knowledge Base Construction

COMBINING DEFAULT LOGIC DATABASES

1994 ◽  
Vol 03 (03) ◽  
pp. 319-348 ◽  
Author(s):  
CHITTA BARAL ◽  
SARIT KRAUS ◽  
JACK MINKER ◽  
V. S. SUBRAHMANIAN
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Large Scale ◽  
Future Generation ◽  
Knowledge Bases ◽  
Default Logic ◽  
Integrity Constraints ◽  
Specialized Knowledge ◽  
The Past ◽  
The Future

During the past decade, it has become increasingly clear that the future generation of large-scale knowledge bases will consist, not of one single isolated knowledge base, but a multiplicity of specialized knowledge bases that contain knowledge about different domains of expertise. These knowledge bases will work cooperatively, pooling together their varied bodies of knowledge, so as to be able to solve complex problems that no single knowledge base, by itself, would have been able to address successfully. In any such situation, inconsistencies are bound to arise. In this paper, we address the question: "Suppose we have a set of knowledge bases, KB1, …, KBn, each of which uses default logic as the formalism for knowledge representation, and a set of integrity constraints IC. What knowledge base constitutes an acceptable combination of KB1, …, KBn?"

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