Automating Property Binding into Informational Aspects from Linked Data

Abstract

Semantic Web is a collaborative effort to bootstrap the Conventional Web (Document Web) content by adding meaningful data structure to it. This transition is to make conventional web’s content more valuable by providing machine understandable and semantically rich structured data. Linked Open Data project is seeding the Semantic Web Vision by publishing and interlinking related structured data. This project resulted in a heap of Linked Open Data which ranges from geographic to cross-domain datasets which provide huge opportunities for knowledge discovery and mash up application development. However to make full use of these datasets, there still lies many challenges which are needed to be trailed. One of the major bottlenecks in Linked Open Data is the extraction and then presentation of semantic data to the naive users. In past many efforts has been made to develop semantic applications which can search the required information from linked data sources accurately, further on can hide the complexities of data, organize and importantly convert the data in a readable format before presenting it to the users. “Concept Aggregation Framework” is one of these applications. According to this framework, the similar type of data is grouped into different aspects and sub-aspects before displaying it. In first step, this framework was practiced in an application known as “CAFSIAL”. However, till now the grouping of the properties into aspects and sub-aspects is being done manually which need to be automated for new resource type alignment in CAFSIAL. This paper presents an automated way of grouping related properties into informational aspects using ontology structure. The evaluation has proved that the grouping of related properties can be automated with good accuracy.