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Motif based Hyponym Relation Extraction from Wikipedia Hyperlinks
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Motif - based Hyponym Relation Extraction from Wikipedia Hyperlinks

Category : Data Mining


Sub Category : DOTNET


Project Code : ITDDM20


Project Abstract

MOTIF-BASED HYPONYM RELATION EXTRACTION

FROM WIKIPEDIA HYPERLINKS

 

 

 

ABSTRACT

 

Discovering hyponym relations among domain-specific terms is a fundamental task in taxonomy learning and knowledge acquisition. However, the great diversity of various domain corpora and the lack of labeled training sets make this task very challenging for conventional methods that are based on text content. The hyperlink structure of Wikipedia article pages was found to contain recurring network motifs in this study, indicating the probability of a hyperlink being a hyponym hyperlink. Hence, a novel hyponym relation extraction approach based on the network motifs of Wikipedia hyperlinks was proposed. This approach automatically constructs motif-based features from the hyperlink structure of a domain; every hyperlink is mapped to a 13-dimensional feature vector based o the 13 types of three-node motifs. The approach extracts structural Information from Wikipedia and heuristically creates a labeled training set. Classification models were determined from the training sets for hyponym relation extraction. Two experiments were conducted to validate our approach based on seven domain-specific datasets obtained from Wikipedia. The first experiment, which utilized manually labeled data, verified the effectiveness of the motif-based features. The second experiment, which utilized an automatically labeled training set of different domains, showed that the proposed approach performs better than the approach based on lexico-syntactic patterns and achieves comparable result to the approach based on textual features. Experimental results show the practicability and fairly good domain scalability of the proposed approach.

 

 

 

 

 

 

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

Wikipedia has become a popular data source in hyponym relation extraction research. Several such studies adopted the syntactic-pattern-based methods or textural feature- based machine learning methods to extract hyponym relations from Wikipedia.

These methods rely mainly on features extracted from the text content of Wikipedia. When shifting to a new domain, these methods require new syntactic patterns to be learned or new training samples to be manually constructed, which usually entail high labor costs.

PROPOSED CONCEPT:-

In this approach to develop Wikipedia with relation extraction approach without navigating different domains. It has two approaches to check our mechanism first, manually labeled data and automatic labeled data sets of different domains.

We found that the Wikipedia navigation box, a type of structural information in Wikipedia article pages, contains plenty of domain- specific hyponym relations that can be automatically extracted. This type of structural information is suitable for automatic labeling of training data with weakly supervised learning because of its high quality.

EXISTING TECHNIQUE:-

Information Extraction.

PROPOSED TECHNIQUE:-

Motif based hyponym relation Extraction.

TECHNIQUE DEFINITION:-

Considerable knowledge is necessary to accurately extract these tuples from a broad range of text. Existing techniques obtain it in ways ranging from direct knowledge-based encoding to supervised learning to self-supervised learning.

TECHNIQUE DEFINITION:-

Automatically construct motif-based features from the WAG of a domain. Extract structural information from Wikipedia and heuristically label the training set of the domain based on the extracted structural information.

DRAWBACKS:-

Labor cost is high for generating manual labeling.

Domain independency.

ADVANTAGES:-

Labor cost is low for generating automatic labeling.

Domain dependency.

 
 
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