# Semantic Metadata Annotation Services in the Biomedical Domain--A Literature Review
## 3 Methods--Systematic Literature Review  
A literature search based on the PRISMA guidelines [16] was undertaken in PubMed and Google Scholar using the search phrases \"semantic metadata annotation\" and \"semantic metadata enrichment\" in PubMed and \"biomedical semantic metadata annotation\" and \"biomedical semantic metadata enrichment\" in Google Scholar. We added the term \"biomedical\" to explicitly add the context to the Google Scholar queries, which is implicit when using PubMed. Altogether, 244 articles were screened--all of the PubMed results (144) and the first 50 results of each Google Scholar search. The searches were performed without additional search criteria (for example, time or type of articles) for a broad literature \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**No.** & **Requirement** & **Description** & **Prioritization** \\ \hline
1 & Open accessible and/or Open Code & The software is available under an open-source license or the service is free to use & **MUST** \\ \hline
2 & Support of common data formats & The software supports common and domain specific import and export data formats & **MUST** \\ \hline
3 & FAIR Terminologies & Integrated terminologies comply with the FAIR principles & **MUST** \\ \hline
4 & Terminology Search & The software provides a possibility to search for concepts/classes within integrated terminologies/ontologies & **MUST** \\ \hline
5 & Annotation Suggestion & The software provides suggestions for semantic concept annotations for each data element (semi-autonomous process) & **SHOULD** \\ \hline
6 & Interface to external terminology/ontology services & The software offers the possibility to connect to an external terminology/ontology/SPARQL service & **SHOULD** \\ \hline
7 & Extension of Terminologies/Ontologies & The software allows one to extend the default set of integrated terminologies/ontologies & **MAY** \\ \hline \end{tabular}
\end{table} 
Table 1: Requirements and their prioritization for a semantic metadata annotation service based on current use cases in the NFD4 Health project. analysis. We limited the result set of Google Scholar due to a large amount of search results (in total 33.770 results at the time of search) without relevancy. Even the first 50 results added no significant new finding. The number of duplicates increased and the number of titles and abstracts to include decreased drastically. Search results between 50 and 80 accounted for no new items; thus, the search was limited to 50 records of Google Scholar. In the next step, all duplicate articles were removed. To further remove irrelevant articles the titles were analyzed. Titles that contained the key words \"semantic,\" \"metadata\" or \"annotation\" in combination or alone were included. However, the topic indicated by the title had to be in the context of metadata or text annotation. As a result, the majority (170 of 211) of articles were excluded. Further, articles (22) were excluded in the abstract screening. The exclusion criterion for this step was metadata annotation not being the main topic of the article. For example, the report \"The Semantic Data Dictionary--An Approach for Describing and Annotating Data\" by [PERSON] et al. 2020 [17], fulfills the inclusion criteria in the title screening with the key words \"semantic data\" and \"annotating data,\" but the focus lies in the specification of a data dictionary for a formalized assignment of a semantic representation of data [17]. Screening of titles and abstracts was done non-restrictively with regard to the kind of data that were annotated. Articles about biomedical text annotation, such as \"SIFR annotator: ontology-based semantic annotation of French biomedical text and clinical notes\" from [PERSON] et al.