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Version: 5.1.0

# Cleaning

The aggregation processes run independently one from another and continuously. Each aggregation process, depending on the characteristics of the records exposed by the data source, makes use of one or more vocabularies to harmonise the values available in a given field. In this page, we describe the vocabulary-based cleaning operation performed to harmonise the data of the different data sources. A vocabulary is a data structure that defines a list of terms, and for each term defines a list of synonyms:

    <TERMS>
        <TERM native_name="Annotation" code="0018" english_name="Annotation" encoding="OPENAIRE">
            <SYNONYMS>
                <SYNONYM term="Comentario" encoding="CSIC"/>
                <SYNONYM term="Comment/debate" encoding="Aaltodoc Publication Archive"/>
                <SYNONYM term="annotation" encoding="OPENAIRE-PR202112"/>
                [...]
            </SYNONYMS>
            <RELATIONS/>
        </TERM>
        <TERM native_name="Article" code="0001" english_name="Article" encoding="OPENAIRE">
            <SYNONYMS>
                <SYNONYM term="A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä" encoding="Aaltodoc Publication Archive"/>
                <SYNONYM term="A4 Artikkeli konferenssijulkaisussa" encoding="Aaltodoc Publication Archive"/>
                <SYNONYM term="Article" encoding="OTHER"/>
                <SYNONYM term="Article (author)" encoding="OTHER"/>
                [...]

Each vocabulary is typically used to control and harmonise the values available in a specific field characterising the bibliographic records. The example above provides a preview of the vocabulary used to clean the result's instance typology.

The content of the vocabularies can be accessed on api.openaire.eu/vocabularies.

Given a value provided in the original records, the cleaning process looks for a synonym and, when found, resolves the corresponding term which is used in turn to build the cleaned record. Each aggregation process applies vocabularies according to their definitions in a given moment of time, however, it could be the case that a vocabulary changes after the aggregation of one data source has finished, thus the aggregated content does not reflect the current status of the controlled vocabularies.

In addition, the integration of ScholeXplorer and DOIBoost and some enrichment processes applied on the raw and on the de-duplicated graph may introduce values that do not comply with the current status of the OpenAIRE controlled vocabularies. For these reasons, we included a final step of cleansing at the end of the workflow materialisation.