Measuring structural similarity between RDF graphs

Maillot, Pierre; Bobed, Carlos

doi:10.1145/3167132.3167342

Cited by 14 publications

(15 citation statements)

References 20 publications

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“…As in the previous level, evaluating with the GS is the ideal approach, but in case it is not available as an ontology, structure-based evaluation methods are also appropriate, such as Graph Similarity or RDF Similarity (Jian et al, 2005;Maillot and Bobed, 2018). The comparison is performed by triples and the similarity comes from the accumulation of similarities of entities involved in the same role (subject, predicate, object) in the two triples being compared.…”

Section: Structural Levelmentioning

confidence: 99%

Application of a methodological approach to compare ontologies

Cardinale

Cornejo-Lupa

Gala

et al. 2021

IJWIS

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Purpose This study aims to the OQuaRE quality model to the developed methodology. Design/methodology/approach Ontologies are formal, well-defined and flexible representations of knowledge related to a specific domain. They provide the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. Then, it is necessary to define how to compare such ontologies to decide which one is the most suitable for the specific needs of users/developers. As the emerging development of ontologies, several studies have proposed criteria to evaluate them. Findings In a previous study, the authors propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural and Domain Knowledge levels, considering correctness and quality perspectives. As the evaluation methods of the proposal are based on a golden-standard, it can be customized to compare ontologies in any domain. Practical implications To show the suitability of the proposal, the authors apply the methodological approach to conduct comparative studies of ontologies in two different domains, one in the robotic area, in particular for the simultaneous localization and mapping (SLAM) problem; and the other one, in the cultural heritage domain. With these cases of study, the authors demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domains. Originality/value Using these metrics and the quality model from OQuaRE, the authors are incorporating a standard of software engineering at the quality validation into the Semantic Web.

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Section: Structural Levelmentioning

confidence: 99%

Application of a methodological approach to compare ontologies

Cardinale

Cornejo-Lupa

Gala

et al. 2021

IJWIS

View full text Add to dashboard Cite

show abstract

“…If one actually wanted to perform category prediction [2,26] or measure the structural similarity between RDF datasets [27], we could ask if the graph measures presented in this paper are appropriate and sufficient. As discussed earlier, vocabulary usage and the way how publishers, data extraction tools, and researchers describe data, has an impact on the graph's topology.…”

Section: Limited Set Of Featuresmentioning

confidence: 99%

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

Zloch

Acosta

Hienert

et al. 2021

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The topological structure of RDF graphs inherently differs from other types of graphs, like social graphs, due to the pervasive existence of hierarchical relations (TBox), which complement transversal relations (ABox). Graph measures capture such particularities through descriptive statistics. Besides the classical set of measures established in the field of network analysis, such as size and volume of the graph or the type of degree distribution of its vertices, there has been some effort to define measures that capture some of the aforementioned particularities RDF graphs adhere to. However, some of them are redundant, computationally expensive, and not meaningful enough to describe RDF graphs. In particular, it is not clear which of them are efficient metrics to capture specific distinguishing characteristics of datasets in different knowledge domains (e.g., Cross Domain vs. Linguistics). In this work, we address the problem of identifying a minimal set of measures that is efficient, essential (non-redundant), and meaningful. Based on 54 measures and a sample of 280 graphs of nine knowledge domains from the Linked Open Data Cloud, we identify an essential set of 13 measures, having the capacity to describe graphs concisely. These measures have the capacity to present the topological structures and differences of datasets in established knowledge domains.

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“…Finally, the application of our foundations (i.e., heuristics) to uncover redundancies that can be further captured by RDF compression techniques sets the stage for the application of further uncovered transformations. Our future work considers both using other implicit structural similarity patterns (e.g., looking at the structure of adjacent nodes in the RDF graph [27]), as well as making use of explicitly declared constraints or regularities in the data (e.g., expressed with SHACL [26] or ShEx [4]).…”

Section: Reorganizationmentioning

confidence: 99%