The Future of FAIR: Machine Learning Will Take Us Beyond Data Integration

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The following is an excerpt from Phill Jones’ recent white paper The Top Trends in Knowledge and Information Management. Download the full paper here

Data integration is increasingly important. The increase in momentum behind it mirrors a broader movement in science generally to make data findable, accessible, interoperable and reusable (FAIR).

FAIR data owes its origins to open science trends in academia. The concept is gaining traction and has been expanded upon by commercial companies to include internally FAIR data as a guiding principle to ensure that all results are adequately documented. As a researcher that I spoke to said:

“…in our electronic lab notebook, the guidelines of how we’re supposed to keep records is that anybody with a similar skill set and equally qualified should be able to follow the steps and obtain the same results.”

The philosophy behind FAIR has deeper implications than good data stewardship and reproducibility. The concept of interoperability can extend beyond data and include any class of information object. As people are becoming increasingly skilled in making data consistent, it naturally becomes easier for machines to read it. As machines become increasingly better at reading natural language and unstructured data sources, automated processing of information becomes increasingly powerful. In the long term, these two approaches will converge and there will be no meaningful distinction between the way we handle data and documents. It will all just be information.

For the immediate future, knowledge and information managers should keep a close eye on semantic search approaches such as topic extraction and assertion mapping. While those approaches continue to mature, the use of curated taxonomies, full text, and fully aggregated search that cuts across databases of all kinds, literature, and document stores can significantly decrease the amount time spent searching for the right piece of information.

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Ray Gilmartin

Author: Ray Gilmartin

Ray Gilmartin is Director of Corporate Solutions for Copyright Clearance Center. He is responsible for knowledge management products within the Corporate Business Unit including RightFind Navigate, RightFind Professional, and XML for Mining. Ray has diverse experience in providing innovative tools for the management and distribution of information across multiple industries. Before joining CCC, he served in several leadership roles at Akamai, Avid Technology, and HP after beginning his career in TV journalism roles at Hearst Broadcasting and the Christian Science Monitor. Ray holds an MBA from Boston University and resides in Massachusetts with his wife and two young children.

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