Towards Semantic Big Graph Analytics for Cross-Domain Knowledge Discovery

TL;DR

This paper proposes a semantic big graph analytics framework designed to manage large, cross-domain linked data, enabling more accurate and efficient discovery of knowledge across diverse fields.

Contribution

It introduces a novel framework that leverages semantic methods to handle big graph data from multiple domains for improved knowledge discovery.

Findings

Enhanced cross-domain knowledge discovery accuracy

Efficient management of large graph data

Framework applicability across various domains

Abstract

In recent years, the size of big linked data has grown rapidly and this number is still rising. Big linked data and knowledge bases come from different domains such as life sciences, publications, media, social web, and so on. However, with the rapid increasing of data, it is very challenging for people to acquire a comprehensive collection of cross domain knowledge to meet their needs. Under this circumstance, it is extremely difficult for people without expertise to extract knowledge from various domains. Therefore, nowadays human limited knowledge can't feed the high requirement for discovering large amount of cross domain knowledge. In this research, we present a big graph analytics framework aims at addressing this issue by providing semantic methods to facilitate the management of big graph data from close domains in order to discover cross domain knowledge in a more accurate and efficient way.