Efficiency and precision trade-offs in graph summary algorithms

Campinas, Stéphane; Delbru, Renaud; Tummarello, Giovanni

doi:10.1145/2513591.2513654

Cited by 21 publications

(35 citation statements)

References 18 publications

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“…Typically, the RDF summarization methods proposed so far do not address in depth the problem of the quality of the produced RDF summaries. A noticeable exception is the work in [11], which proposes a model for evaluating the precision of the graph summary, compared to a gold standard summary. The main idea of the precision model is based on counting the edges or paths that exist in the summary and/or in the data graph.…”

Section: Structural Extraction Approachesmentioning

confidence: 99%

Quality metrics for RDF graph summarization

Zneika

Vodislav

Kotzinos

2019

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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.

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Section: Structural Extraction Approachesmentioning

confidence: 99%

Quality metrics for RDF graph summarization

Zneika

Vodislav

Kotzinos

2019

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“…in [20], that RDF graphs exhibit so much structural heterogeneity that bisimulationbased summaries are very large, almost of the size of G, thus not very useful. In contrast, [11], [17] introduced ≡ relations which lead to compact summaries, many orders of magnitude smaller than the original graphs.…”

Section: B Quotient Rdf Summariesmentioning

confidence: 99%

“…Given an equivalence relation ≡, for each equivalence class C (that is, maximal set of graph nodes comprising nodes all equivalent to each other), the summary has exactly one node n C in the summary. Example of quotient-based summaries include [4], [5], [6], [7], [17], [11], [10], [12]; other summaries (including Dataguides) are not quotient-based.…”

Section: Introductionmentioning

confidence: 99%

Quotient RDF Summaries Based on Type Hierarchies

Guzewicz

Manolescu

2018

2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)

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Abstract-Summarization has been applied to RDF graphs to obtain a compact representation thereof, easier to grasp by human users. We present a new brand of quotient-based RDF graph summaries, whose main novelty is to summarize together RDF nodes belonging to the same type hierarchy. We argue that such summaries bring more useful information to users about the structure and semantics of an RDF graph.

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“…Other approaches try to create mainly instance summaries, by exploiting the instances' semantic associations, by proposing different algorithms that do not take into consideration the schemata of the graphs. To this end, Campinas et al [29] present several different summary graphs with different instance equivalence criteria for each algorithm. Jiang et.al.…”

Section: Related Workmentioning

confidence: 99%

Ontology understanding without tears: The summarization approach

Troullinou

Kondylakis

Daskalaki

et al. 2017

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Given the explosive growth in both data size and schema complexity, data sources are becoming increasingly difficult to use and comprehend. Summarization aspires to produce an abridged version of the original data source highlighting its most representative concepts. In this paper, we present an advanced version of the RDF Digest, a novel platform that automatically produces and visualizes high quality summaries of RDF/S Knowledge Bases (KBs). A summary is a valid RDFS graph that includes the most representative concepts of the schema, adapted to the corresponding instances. To construct this graph our algorithm exploits the semantics, the structure of the schema and the distribution of the corresponding data/instances to initially identify the most important nodes. Then we explore how to select the edges connecting these nodes by maximizing either locally or globally the importance of the selected edges. The performed evaluation demonstrates the benefits of our approach and the considerable advantages gained. Furthermore, we present our first steps into enabling summary exploration through extensible summaries.

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Efficiency and precision trade-offs in graph summary algorithms

Cited by 21 publications

References 18 publications

Quality metrics for RDF graph summarization

Quality metrics for RDF graph summarization

Quotient RDF Summaries Based on Type Hierarchies

Ontology understanding without tears: The summarization approach

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