# SumMER: Structural Summarization for RDF/S KGs
## 2 State of the Art on Structural Summarization  
The rapid increase of the available RDF data and the need to effectively and efficiently explore and understand such data has sparked many contributions in the semantic summarization field. Interested readers are forwarded to our recent work (a survey [1] and a tutorial [9]), for an overview of the works in the area. Overall, the research in the area can be classified according to the summarization method used in structural, statistical, pattern-mining, and hybrid. Structural methods exploit the graph structure, and works in this category can be further classified into quotient and non-quotient methods. The former considers some notion of \"equivalence\" for identifying node's representatives whereas the latter mostly select individual nodes out of the RDF graph. Pattern-mining methods, exploit pattern discovery techniques, providing a set of patterns as the result summary. Statistical methods quantitatively summarize the contents of a KG, counting occurrences, computing histograms, etc. Finally, hybrid methods combine multiple techniques for summarization. In the remaining of this section, we will only refer to structural non-quotient methods as our line of work falls also there. [PERSON] et al. [2] presented the first non-quotient structural summarization method along with [PERSON] et al. [3]. [PERSON] et al. focuses on automatically identifying the key concepts in an KG combining cognitive, lexical, and topological measurements, whereas [PERSON] et al. presents similar algorithms to identify the most important concepts and relations in an iterative manner. Unfortunately, both approaches only identify the top-k most important noted without linking them for returning a sub-graph of the original graph to the end user. [PERSON] et al. [4] focuses on identifying the most important sentences in a KG and exploit measures such as the degree-centrality, the betweenness and the eigenvector centrality. [PERSON] et al. [5] combines user preferences with the degree centrality and the closeness in order to calculate the importance of the various nodes. Then for connecting the selected nodes they employ an algorithm that includes the most important nodes in the final graph. However, the produced algorithm prioritizes direct neighbors ignoring that the selection of other paths that could maximize the importance of the selected summary. 1-LSP-DisC is another approach that focuses on constructing structural summaries reusing ideas from result diversification. The key idea there is that instead of focusing only to the \"central\" nodes to push node selection also to the perimeter of the graph. However although such summaries are able to focus on specific parts of interest of the graph they fail to provide high-quality summaries of the entire KG. WBSum [10] exploits query workloads in order to identify the node that appear more frequently in user queries as the more important ones and then use a Steiner-Tree approximation for linking the selected nodes. GLIMPSE [11] focuses on constructing personalized summaries of KG containing only the facts most relevant to individuals'interests. However, they require from the user to provide a set of relevant queries that would like relevant information to be included in the summary. However, both WBSum and GLIMPSE require an existing workload for constructing the summary which limits their applicability. Finally, RDFDigest+ [6; 7] exploits the betweenness centrality along with the number of instances for selecting schema nodes linking them as well with various Steiner Tree approximations, dominate other approaches in the area of semantic schema summarization. However, we argue that multiple centrality measures can offer a complementary view of a node's importance. In this direction, as we showed, machine learning significantly improves the quality of the generated summaries.