Leveraging Bibliographic RDF Data for Keyword Prediction with Association Rule Mining (ARM)

Abstract

The Semantic Web (Web 3.0) 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 inhomogeneity and large data volumes. RKBExplorer is one of many repositories implementing Open Data and contains considerable bibliographic information. This paper discusses bibliographic data, an important part of cloud data. Effective searching of bibiographic 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. 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. −−−−− Paper presented at 1st International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2014) March 27-28, 2014. Organized by VIT University, Chennai, India. Sponsored by BRNS.

Keywords

• Keyword generation
• Linked Open Data Cloud
• Data mining
• Query-answering system
• RDF
• Association rule mining