Data Science Ontology

A Data Science Ontology is a data science KB that is a domain-specific ontology for the field of data science.

• AKA: Formal Machine-Readable Data Science KB.
• Context:
  • It can (typically) include Data Science Concepts, such as: Hypothesis Testing Concepts, Data Engineering Concepts, etc.
  • It can (typically) include Data Science Relations.
  • It can be associated to a Machine Learning Ontology or a Statistics Ontology.
  • …
• Example:
  • kddo1 Ontology.
  • OntoDM Ontology.
  • …
• Counter-Example(s)
  • a Informal Database Mining KB.
  • a Biomedical Ontology, such as a GO ontology.
  • a Software Engineering Ontology.
• See: Data Mining Textbook, Computer Science Ontology.

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References

2016

• (Melli, 2016) ⇒ Gabor Melli. (2016). “Semantically Annotated Concepts in KDD's 2009-2015 Abstracts.” In: Proceedings of LangOnto2-TermiKS (LO2TKS) 2016 Workshop.
  • QUOTE: Each abstract was internally annotated to identify the concepts mentioned within the text. Then, where possible, each mention was linked to the appropriate concept node in the ontology focused on data science topics.

2010

• (Melli, 2010a) ⇒ Gabor Melli. (2010). “Concept Mentions within KDD-2009 Abstracts (kdd09cma1) Linked to a KDD Ontology (kddo1).” In: Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC 2010).

2008

• (Panov et al., 2008) ⇒ Pance Panov, Sašo Džeroski, and Larisa Soldatova. (2008). “OntoDM: An Ontology of Data Mining.” In: IEEE International Conference on Data Mining Workshops (ICDMW 2008). doi:10.1109/ICDMW.2008.62

2003

• (Cannataro & Comito, 2003) ⇒ Mario Cannataro, and Carmela Comito. (2003). “A Data Mining Ontology for Grid Programming.” In: Proceedings of the First International Workshop on Semantics in Peer-to-Peer and Grid Computing at WWW 2003.

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