Feature


Bringing people


There is a continuing need for the sorts of insights and judgements that only a person can bring, writes David Stuart


An inconvenient truth of the data age is that so much of the value in the data we are capturing is just going to waste. For the value of data to be realised it must be FAIR (Findable, Accessible, Interoperable and Reusable) and too often it just isn’t. Data is being captured but it’s not in a machine-readable format, or when it is in a machine-readable format it’s not making use of common standards. The waste is unnecessary; solutions are readily available that just need to be implemented. Semantic enrichment is one such


solution that has been around for a while. It tackles the problem of data wastage by reducing the ambiguity in the language used. The language people use every day is naturally rich and ambiguous, containing both synonyms and homonyms, and over


together for semantic enrichment


time meanings change. This ambiguity often goes unnoticed by people, as there’s generally sufficient context for understanding or the opportunity for clarification. It can quickly cause difficulties, however, when machines are used to find and reuse data. Does mercury refer to the element, the planet, the god, the space programme, or one of the countless individuals or organisations that have it as a name?


Semantic enrichment is the process of adding a machine-readable layer of metadata that makes things findable through the disambiguation of concepts with controlled vocabularies. These vocabularies may have different amounts of relational richness; from simple subject headings and authority files, through


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