# Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review
## 5 Results
### Adoption
#### 5.2.1 Publishing and Exploitation  
A SPARQL endpoint is the most used publication method, but we also see that the majority of the publications provide a front-end (see Figure 4). However, an interesting observation is that even though knowledge graphs do provide a unique way for data aggregation and analysis, very few of the papers provide autogenerated analytics for common metrics and data views. Even fewer provide any type of tutorial to utilise the knowledge graph. These aspects do limit the utilisation of the potential of knowledge graph features. In Figure 5, we have an overview of some of the most used external knowledge graphs and types of knowledge graphs that have been linked to (not including the ones with the smallest count). DBpedia appears the most popular knowledge graph. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Tool** & **Total** & **Explanation** & **Papers** \\ \hline Jena TDB and Fuseki & 9 & Storage, publication & \#2, \#4, \#8, \#10, \#21, \#22, \#27, \#32, \#33 \\ OpenLink Virtuoso & 8 & Storage, publication, RDF virtualisation & \#3, \#4, \#6, \#24, \#25, \#26, \#28, \#35 \\ XSLT & 4 & Data mapping, transformation & \#18, \#23, \#25, \#32 \\ SILK & 4 & Reconciliation, graph linking & \#5, \#11, \#28, \#32 \\ Pubby & 4 & KG publication (front-end interface) & \#19, \#24, \#31,\#32 \\ OpenRefine & 3 & Reconciliation, mapping, transformation & \#5, \#8, \#24 \\ RML & 2 & Mapping, transformation & \#8, \#12 \\ ELDA & 2 & KG publication (API) & \#25, \#28 \\ D2 RQ & 2 & Data mapping, virtualisation (relational) & \#25, \#29 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Most used tools. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Ontology/Vocabulary** & **Total** & **Explanation** & **Papers** \\ \hline VoID & 11 & Expressing metadata about RDF datasets & \#1, \#4, \#14, \#15, \#16, \#19, \#20, \#25, \#30, \#32, \#36 \\ SKOS & 11 & Representing knowledge organization systems & \#2, \#4, \#8, \#9, \#19, \#25, \#27, \#28, \#29, \#32, \#33 \\ Dublin Core & 11 & Describing metadata about resources & \#2, \#5, \#8, \#20, \#23, \#24, \#27, \#28, \#32, \#32, \#35, \#36 \\ PROV-O & 9 & Representing provenance information & \#7, \#15, \#16, \#19, \#29, \#30, \#31, \#35, \#36 \\ DCAT-AP & 6 & Describing public sector datasets in Europe & \#3, \#4, \#16, \#24, \#30, \#31 \\ CIDOC CRM & 5 & Describing cultural heritage domain & \#2, \#11, \#13, \#28, \#32 \\ FOAF & 5 & Describing individuals and social networks & \#8, \#27, \#28, \#33, \#36 \\ RDF DATA Cube & 4 & Publishing multi-dimensional data, such as statistics & \#3, \#4, \#26, \#31 \\ FRBR & 4 & Describing bibliographic domain & \#9, \#12, \#13, \#20 \\ DDLCE+DrS & 4 & Describing natural language and human common sense & \#5, \#13, \#25, \#28 \\ Schema.org & 3 & Describing common entities, such as people & \#5, \#8, \#27 \\ DBPedia & 3 & Describing common entities, such as people & \#20, \#26, \#33 \\ BIBO & 3 & Describing bibliographic things & \#7, \#27, \#32 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Most used ontologies and vocabularies. Figure 6 provides an overview on how knowledge graphs are evaluated in the reviewed works. We see that linking quality to other graphs is of high interest, together with usability. Based on the technical and semantic capabilities of knowledge graphs, it is interesting to see that metrics such as correctness, graph coverage, and ontology coverage are infrequently mentioned. Key observations regarding the employed use cases include:  
* Only two studies [77; 78] (#8 and #9) provide descriptions of use cases with advanced analytics, utilising the semantic and technical potential of knowledge graphs. * Six studies provide sample queries for the respective knowledge graph. * Twelve studies mention a use case from an external party. * Seven studies mention use cases done by the authors themselves. Figure 4: Publication methods. Figure 5: Links to other graphs. * Four studies mention potential use cases, without having an actual performed use case presented.