# SumMER: Structural Summarization for RDF/S KGs
## 1 Introduction  
The Linked Open Data (LOD) and the Data Web initiatives have created an enormous amount of freely available knowledge graphs (KGs) that are either manually or automatically generated. The complexity of the schema of those KGs makes it rather difficult to be fully understood and explored, limiting their exploitation potential. In addition, users, in order to formulate queries and to publish the graph with additional instances, have to examine and comprehend the entire schema in order to identify the elements of interest. It is therefore more important than ever to develop methods and tools to facilitate the quick understanding and exploration of these data sources. Semantic summarization is recognized as a powerful tool for understanding KGs, as well as supporting the exploration and reuse of the information available them. A semantic summary, according to our recent survey [1] is \"a compact information extracted from the original graph, offering a way to extract meaning from data while reducing its size, and/or a graph, which some application can exploit instead of the original graph for performing certain tasks more efficiently like query answering, source selection, etc.\"  
In this paper, we focus on methods for structural semantic summarization, which primarily focus on the graph structure for summary generation. State-of-the-art methods in the domain [2, 3] focus on selecting only the most important nodes of an RDF/S graph for constructing a summary. This selection is based on specific important measures orcentrality measures [4; 5] in order to identify the most important nodes and then introduce heuristics to link them. RDFDigest+ [6], one of the most recent works in the area, adapts the betweenness centrality for node selection and Steiner Tree approximations for linking the selected nodes, constructing high-quality summaries, dominating previous approaches in the area in terms of quality for query answering. **The problem. ** Over the years, multiple centrality measures have been proposed (degree, betweenness, HITS, PageRank, etc. ), considering diverse criteria for node importance. However, still, no centrality measure dominates them all, and each one is appropriate for different notions of importance over different types of graphs. To make things worse, several of these centralities interrelate. Existing approaches on structural summarization, in most of the cases, select a single (or just a few) centrality measure(s) that produce the best results for selecting the most important nodes for a specific ontology. However, as already explained, centrality measures offer a complementary view on node's importance, and combining them appropriately has the potential to uplift the quality of the generated summaries. To the best of our knowledge, there is no other approach currently available, focusing on how to optimally combine multiple centrality measures for generating high-quality structural summaries. **Our solution. ** We argue that exploiting multiple such measures has the potential to provide a more objective view on which nodes should be selected as the most important ones. In this direction, we explore machine learning (ML) algorithms for learning how to optimally combine multiple importance measures for node selection. More specifically:  
* We explore eight centrality measures that have been proposed in the bibliography, i.e., the degree, the bridging, and the harmonic centrality, the radiality, the ego centrality, the betweenness, the PageRank and the HITS, each one of them perceiving differently the importance of the available nodes. * We model the problem of selecting the top-k most important nodes as a regression problem. For each schema node we construct a set of features based on multiple centrality measures and the corresponding number of instances available for that schema node. Then we search for their optimal combination to maximize the quality of the generated summary.