| AndrewSu added a comment. |
My initial thought is that there will be two types of metrics. First, we want to look at statement-level metrics. For all the statements that our team has loaded into Wikidata, we have been referencing specific resources that assert that statement. For example, see the human gene reelin (Q414043). This gene has a genetic association (P2293) with the disease Alzheimer's disease (Q11081), as stated in (P248) a database called Phenocarta (Q22330995). We would like to provide the Phenocarta team statistics on how often Phenocarta-referenced statements are used in SPARQL queries. Those statements might be part of the output of the SPARQL query, or they might simply be structural intermediates.
Second, we might also want to look at item-level metrics. See for example visual agnosia (Q18742). This item is mapped to the Disease Ontology (Q5282129) through the Disease Ontology ID (P699) (and one intermediate item for the specific release of the ontology). Again, we would want to provide the Disease Ontology team metrics on how often DO-linked items were utilized (either directly or indirectly) in SPARQL queries. (Note also that ontologies that are referenced as external identifiers in Wikidata items will very often also be referenced in support of instance of (P31) or subclass of (P279) statements, which may fall under the previous category.)
Computing one or both of these metrics in my mind would be good first steps, though I'm guessing there would need to be further iteration once we examine the results. Hope this is helpful...
Cc: Nuria, Lydia_Pintscher, mkroetzsch, leila, debt, thiemowmde, Jonas, Smalyshev, AndrewSu, Aklapper, I9606, GoranSMilovanovic, QZanden, EBjune, merbst, Avner, Gehel, FloNight, Xmlizer, Izno, JAllemandou, jkroll, Wikidata-bugs, Jdouglas, aude, Tobias1984, Manybubbles, Mbch331, jeremyb
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