# SumMER: Structural Summarization for RDF/S KGs
## 1 Introduction
We explore eight ML algorithms, (i.e., Adaboost, Gradient Boosting, Extra Tree, Random Forest, Linear, Decision Tree, Bayesian Ridge, and ElasticNet regressors) identifying the best performing one. * Having selected the most important nodes in the previous step, we try next to identify the optimal paths for connecting the selected nodes. We model the problem of connecting the selected nodes as a graph Steiner-Tree Problem trying to minimize the total number of the additional nodes introduced when linking the selected nodes. We show that the problem is NP-complete and explore three approximations, the SDIST, the CHINS and the HEUM which are trying to optimize either the insertion of a single component or the connection of the components using their shortest paths. A heuristic improvement procedure is implemented on top of these algorithms, ensuring that all leaves are terminal nodes, further optimizing algorithms efficiency. * Finally, we present a detailed experimental analysis using three real-world datasets, DBpedia v3.8 & v3.9 and SWDF and the corresponding query logs that we exploit in order to construct the \"golden standard\" summary in each case. More specifically, we use an existing query log from DBpedia v3.8, including more that 50K queries, to train the various models and we show experimentally the improvement on the quality of the produced summary in both DBpedia v3.9 and SWDF, demonstrating that our results generalize, not only in different versions of the same ontology -that differ to a large extent, but also to completely different ontologies. * Overall, we show that the Decision Tree regressor is the best performing algorithm showing a stability over the different KGs used. In addition, we show that Steiner-Tree approximations used for linking the nodes, introduce a minimal number of additional nodes to the result schema graph, in essence not affecting summary quality. The best choice among the approximate algorithms is CHINS offering an optimal trade-off between quality and execution time. To the best of our knowledge, this is the first approach exploiting machine learning for constructing high-quality structural semantic summaries. A preliminary version of this work has already been published in ESWC [7], exploring a single centrality measure for selecting the most important nodes. The current paper extends previous work in many ways: first by considering two additional centrality measures (HITS and PageRank) and most importantly by exploiting several machine learning techniques for optimally selecting and combining multiple centrality measures. In addition, we significantly extend the experimental evaluation, showing the high quality of the generated summary. A four pages poster, sketching the main ideas behind them was also presented in ISWC [8]. The remaining of the paper is structured as follows: Section 2 introduces preliminaries required for understanding the presented solution. Section 3 presents the methodology and the various algorithms created for constructing structural semantic summaries. In Section 4 we experimentally evaluate our solution. Finally, Section 5 presents the related work and Section 6 presents directions for future work and concludes this paper.