# Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review
## 7 Conclusions  
In this paper, we reported a systematic review, focusing on four selected venues, namely _Extended Semantic Web Conference_, _International Semantic Web Conference_, _Journal of Web Semantics_, and _Semantic Web Journal_. The goal was to obtain an overview over publication statistics, technical details, and issues and limitations within knowledge graph creation for the Semantic Web. Regarding the validity of results, the arguably smaller scope of venues can be considered a limitation. The venues were chosen based on general citation count and the relevancy of publications within the scope. We see that the count of studies, within the given time frame, was as much as 36 studies. Given the fact that the chosen venues are of high relevance within the Semantic Web domain and have strict review processes, the sample of studies should give a relevant and valid view for the addressed research questions.  
From the research statistics, we see that the interest in knowledge graph publication in the Semantic Web domain, throughout the four years reported, had a decrease from the first two years to the last two years, and had its peak in 2017. From the technical details, the main observations were that:  
* The main phases in the construction process reported were ontology development, the RDF mapping process, and publication.
* There is a big diversity in sources, tools, and phases reported, with 11 data source types, more than 20 sub-phases, and more than 40 tools and languages used.
* There was a big focus on publication; 24 out of 36 studies reported a front end for the exploration of the knowledge graph.
* Few studies reported the use of advanced knowledge processing, i.e., inductive knowledge such as embedding, analytics, etc.
* There was little focus on the evaluation phase; most of the evaluation tasks described were qualitative and not within a dedicated phase.  
Regarding the reported issues, limitations, and lessons learned, a great variety was reported, but many aspects were within the same category. The main findings were:  
* There were frequent mentions of limitations related to tools.
* Many of the lessons learned were related to making knowledge graph technologies more approachable to non-experts.
* Poor data quality is often reported to be an issue.  
Regarding the future work, literature reviews focusing explicitly on the tools and methodologies for creating knowledge graphs with the goal of consolidating the landscape would be one direction. This is because the findings from the review showed that knowledge graph construction in the Semantic Web domain is of high complexity with a very fragmented tool portfolio. The scope of this review was on the creation of knowledge graphs from (semi-)structured data sources; there is still room for reviews with a larger scope. Apart from extending the selected venues, another interesting direction would be to do a similar review for knowledge graph construction from unstructured data sources.  
Conceptualization, V.R., A.S. and D.R.; methodology, V.R., A.S. and D.R.; formal analysis, V.R.; investigation, V.R.; data curation, V.R.; writing--original draft preparation, V.R.; writing--review and editing, V.R., A.S. and D.R.; supervision, A.S. and D.R. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the European Commission Horizon 2020 DataCloud project (grant number 101016835), the NFR BigDataMine project (grant number 309691), and the SINTEF internally funded SEP DataPipes project.  
Not applicable, the study does not report any data.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
RDF Resource Description Framework  
RDFS Resource Description Framework Schema  
OWL Web Ontology Language  
CSV Comma-Separated Values  
XML Extensible Markup Language  
JSON JavaScript Object Notation