LEVERAGING BIBLIOGRAPHIC RDF DATA FOR KEYWORD PREDICTION WITH ASSOCIATION RULE MINING (ARM)

Authors

Nidhi Kushwaha, Indian Institute of Information Technology
O P. Vyas, Indian Institute of Information Technology

Document Type

Article

Date of this Version

11-14-2014

Citation

Data Science Journal (2014) 13: 119-126 doi: 10.2481/dsj.14-033

Comments

License: CC BY 3.0

Abstract

The Semantic Web ( Web 3.03.0) has been proposed as an efficient way to access the increasingly large amounts of data on the internet. The Linked Open Data Cloud project at present is the major effort to implement the concepts of the Seamtic Web, addressing the problems of in homogeneity and large data volumes. RKBExplorer is one of many repositories implementing Open Data and contains considerable bibliographic information. Th is paper discusses bibliographic data data, an important part of cloud data. Effective searching of bibliographic datasets can be a challenge as many of the papers residing in these databases do not have sufficient or comprehensive keyword information. In these cases however, a search engine based on RKBExplorer is only able to use information to retrieve papers based on author names and title of papers without keywords keywords. In this paper we attempt to address this problem by using the data mining algorithm Association Rule Mining (ARM ) to develop keywords based on features retrieved from Resource Description Framework (RDF) data within a bibliographic citation. We have demonstrate the applicability of this method for predicting missing keywords for bibliographic entries in several typical databases.