X-Git-Url: https://code.communitydata.science/covid19.git/blobdiff_plain/4fd516a700501e0b934ddc7d90c27dfa5b87e7f4..ff5521d44bf2d680e089b1fba7d92a134c98a7e6:/keywords/README.md?ds=inline

diff --git a/keywords/README.md b/keywords/README.md
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--- a/keywords/README.md
+++ b/keywords/README.md
@@ -5,3 +5,10 @@ This code finds trending web searches related to the COVID-19 pandemic using Goo
 We search the Wikidata API for entities in `src/wikidata_search.py` and then we make simple SPARQL queries in `src/wikidata_translations.py` to collect labels and aliases the entities.  The labels come with language metadata.  This seems to provide a decent initial list of relevant terms across multiple languages. 
 
 The output data lives at [covid19.communitydata.science](https://covid19.communitydata.science/datasets/keywords).
+
+The output files have 4 colums: 
+
+- `itemid` links to the wikidata entity
+- `label` is the translation of the relevant keyword
+- `langcode` is the [iso 639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) code corresponding the language of the label. 
+- `is_alt` indicates whether the label is an [alias](https://www.wikidata.org/wiki/Help:Aliases).