Knowledge Graph for Reusing Research Knowledge on Related Work in Data Analytics

Data analytics projects encompass a multitude of facets, including the types of analytics employed, algorithms utilized, and data sources scrutinized. Despite this wealth of information, there remains a challenge in effectively leveraging previous related work for future projects. Traditional approaches often lack mechanisms for preserving and repurposing the knowledge gained from the analysis of related works. In response, this paper introduces a novel method leveraging RDF triples to encapsulate attributes of analytics projects. These RDF triples are then integrated into a web-based knowledge graph, facilitating the exploration of related work within specific data analytics domains. By harnessing this method, researchers and practitioners can identify valuable resources, including data sources, tools, and algorithms, for future endeavors. To demonstrate its efficacy, we apply this method to the domain of real estate analytics, showcasing its potential to enhance project efficiency and innovation.