Quality metrics for RDF graph summarization

Abstract

RDF Graph Summarization pertains to the process of extracting concise but meaningful summaries from RDF Knowledge Bases (KBs) representing as close as possible the actual contents of the KB. RDF Summarization allows for better exploration and visualization of the underlying RDF graphs, optimizstion of queries or query evaluation in multiple steps, better understanding of connections in Linked Datasets and many other applications. In the literature, there are efforts reported presenting algorithms for extracting summaries from RDF KBs. These efforts though provide different results while applied on the same KB, thus a way to compare the produced summaries and decide on their quality, in the form of a quality framework, is necessary. So in this work, we propose a comprehensive Quality Framework for RDF Graph Summarization that would allow a better, deeper and more complete understanding of the quality of the different summaries and facilitate their comparison. We work at two levels: the level of the ideal summary (or ideal schema) of the KB that could be provided by an expert user and the level of the instances contained by the KB. For the first level, we are computing how close the proposed summary is to the ideal solution (when this is available) by computing its precision and recall against the ideal solution. For the second level, we are computing if the existing instances are covered (i.e. can be retrieved) and in what degree by the proposed summary. We use our quality framework to test the results of three of the best RDF Graph Summarization algorithms, when summarizing different (in terms of content) and diverse (in terms of total size and number of instances, classes and predicates) KBs and we present comparative results for them. We conclude this work by discussing these results and the suitability of the proposed quality framework in order to get useful insights for the quality of the presented results.

RDF graph summarization for first-sight structure discovery

“…Quality metrics for RDF graph summaries have been proposed in [40]. Along these metrics, our summaries score high for preserving all classes and properties from G; the price they pay for compactness is to sometimes show instances that do not exist in G (as our precision loss experiments show).…”

ABSTAT-HD: a scalable tool for profiling very large knowledge graphs

“…RDF graph profiling has been intensively studied, and various approaches and techniques have been proposed to provide a concise and meaningful representation of the RDF KGs. There are different recent surveys that discuss some of the approaches to profile knowledge graphs such as [9,44,54]. Most of the work on KGs profiling has been done in the field of KG summarization, which has been extensively surveyed in [9].…”

Charaterizing RDF graphs through graph-based measures – framework and assessment

“…Thus, some of the works mentioned appear in research activities in this domain of research [5,37]. Creating an RDF summary graph aims at building concise overviews of the data in RDF knowledge bases [37], in order to optimize, for example, querying and processing times for SPARQL engines [7,8], rather than aiming at extracting information about its topology. 3 https://doi.org/10.5281/zenodo.2109469 4 https://data.gesis.org/lodcc/2017-08/…”