# SumMER: Structural Summarization for RDF/S KGs
## 6 Conclusions  
In this paper, we present SumMER focusing on answering two key questions for constructing structural semantic summaries: (a) how to identify the most important nodes and (b) how to link the selected nodes in order to create a sub-graph of the original graph and return it to the user. For answering the first question we explore eight centrality measures for capturing nodes' importance, and then, we effectively combine multiple of them, using machine learning techniques, transforming the selection of the k most important nodes out of an RDF/S KG into a regression problem. Subsequently, we implement three approximations of the graph Steiner-Tree problem for linking the selected nodes.  
For our experimental evaluation we exploit query logs with numerous user queries, showing that machine learning has the potential to improve the quality of the generated summaries. Further after training, testing is practically rapidly fast. Overall, the Decision Tree regressor is the best performing algorithm showing a stability over the different KGs used. Further, the implemented Steiner-Tree approximations, introduce minimal nodes to the result schema graph. The best choice among the approximate algorithms is CHINS offering an optimal trade-off between quality and execution time.  
**Limitations.** Although we have shown that SUMMER is able to generalize on another KG, further experiments are required for other KGs to identify that the results hold indeed in all other KGs. Unfortunately, KGs with available workloads are hard to find online. Another limitation of the current work is that our algorithms are focusing on the schema graph. Applying however our approach to the entire graph would require parallel implementation of the algorithms in this paper in order to finish in a reasonable time.  
**Future Work.** As future work, our approach will be extended to handle constructs from OWL KGs such as class restrictions, disjointness and equivalences and explore other ML methods such as deep networks and methods for learning to rank [30]. Further as KGs are not static but subject to continuous evolution summarizing different versions of the KGs and comparing those summaries would provide useful insights on how graphs evolve. Finally, it would be interesting to explore how Shapes Constraint Language (SHACL) [31] expressions could be used instead our first level schema graph, in order to identify the structure of the underlying KG, and whether an additional abstraction step on top would lead to high-quality summaries.  
Methodology, A.P. and H.K.; Software, G.E.T., A.P. and G.T.; Validation, L.K.; Resources, G.T.; Writing--original draft, G.E.T., L.K. and H.K.; Writing--review & editing, N.P. and H.K.; Supervision, H.K. and N.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I) under the \"2 nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers\" (iQARuS Project No 1147).  
Figure 11: The average execution times for the various algorithms for linking the selected top-k nodes on DBpedia v3.8 (ms).