Knowledge Graph for Reusing Research Knowledge on Related Work in Data Analytics
Lecture Notes in Business Information Processing, ISSN: 1865-1356, Vol: 521, Page: 186-199
2024
- 2Citations
- 1Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85196193867&origin=inward; http://dx.doi.org/10.1007/978-3-031-61003-5_17; https://link.springer.com/10.1007/978-3-031-61003-5_17; https://dx.doi.org/10.1007/978-3-031-61003-5_17; https://link.springer.com/chapter/10.1007/978-3-031-61003-5_17
Springer Science and Business Media LLC
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