XML clustering: a review of structural approaches

Piernik, Maciej; Brzeziński, Dariusz; Morzy, Tadeusz; Leśniewska, Anna

doi:10.1017/s0269888914000216

Cited by 12 publications

(7 citation statements)

References 85 publications

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“…In future works, we plan to apply our measure to di erent problems such as data integration [6] or document clustering as in [12]. Finally, we will work on formalizing the codi cation of the code tables to be included as metadata describing the di erent datasets.…”

Section: Discussionmentioning

confidence: 99%

“…Apart from adapting the algorithm and their measure to RDF graphs, we have shown the capacities of this approach regarding exible data models. In another domain, the notion of structural similarity appears in approaches for XML document clustering [12]. ese approaches use di erent ways to evaluate distances between XML documents, and use these distances in clustering algorithms.…”

Section: Related Workmentioning

confidence: 99%

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Measuring structural similarity between RDF graphs

Maillot¹,

Bobed²

2018

Proceedings of the 33rd Annual ACM Symposium on Applied Computing

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In the latest years, there has been a huge e ort to deploy large amounts of data, making it available in the form of RDF data thanks, among others, to the Linked Data initiative. In this context, using shared ontologies has been crucial to gain interoperability, and to be able to integrate and exploit third party datasets. However, using the same ontology does not su ce to successfully query or integrate external data within your own dataset: the actual usage of the vocabulary (e.g., which concepts have instances, which properties are actually populated and how, etc.) is crucial for these tasks. Being able to compare di erent RDF graphs at the actual usage level would indeed help in such situations. Unfortunately, the complexity of graph comparison is an obstacle to the scalability of many approaches. In this article, we present our structural similarity measure, designed to compare structural similarity of low-level data between two di erent RDF graphs according to the pa erns they share. To obtain such pa erns, we leverage a data mining method (KRIMP) which allows to extract the most descriptive pa erns appearing in a transactional database. We adapt this method to the particularities of RDF data, proposing two di erent conversions for an RDF graph. Once we have the descriptive pa erns, we evaluate how much two graphs can compress each other to give a numerical measure depending on the common data structures they share. We have carried out several experiments to show its ability to capture the structural di erences of actual vocabulary usage.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Measuring structural similarity between RDF graphs

Maillot¹,

Bobed²

2018

Proceedings of the 33rd Annual ACM Symposium on Applied Computing

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“…The requirement of knowing the number of clusters a priori is commonly assumed in most XML clustering algorithms. Recent XML clustering surveys [2,32] reveal that nearly all of the approaches proposed so far rely on this assumption. However, such a requirement may discredit the algorithm in many real-world applications.…”

Section: Parametrizationmentioning

confidence: 99%

Clustering XML documents by patterns

2015

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Now that the use of XML is prevalent, methods for mining semi-structured documents have become even more important. In particular, one of the areas that could greatly benefit from in-depth analysis of XML's semi-structured nature is cluster analysis. Most of the XML clustering approaches developed so far employ pairwise similarity measures. In this paper, we study clustering algorithms, which use patterns to cluster documents without the need for pairwise comparisons. We investigate the shortcomings of existing approaches and establish a new pattern-based clustering framework called XPattern, which tries to address these shortcomings. The proposed framework consists of four steps: choosing a pattern definition, pattern mining, pattern clustering, and document assignment. The framework's distinguishing feature is the combination of pattern clustering and document-cluster assignment, which allows to group documents according to their characteristic features rather than their direct similarity. We experimentally evaluate the proposed approach by implementing an algorithm called PathXP, which mines maximal frequent paths and groups them into profiles. PathXP was found to match, in terms of accuracy, other XML clustering approaches, while requiring less parametrization and providing easily interpretable cluster representatives. Additionally, the results of an in-depth experimental study lead to general suggestions concerning pattern-based XML clustering.

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“…As discussed in the overview paper by Piernik et. al., most clustering applications involve three phases [57]. First, a preprocessing stage occurs to transform the text-based XML documents into more computationally compatible formats.…”

Section: Related Work Involving Xmlmentioning

confidence: 99%

“…Second, a similarity calculation can be developed as a way of measuring how related two or more XML documents are to each other. Finally, a clustering algorithm is applied that uses this similarity calculation to group related documents [57].…”

Section: Related Work Involving Xmlmentioning

confidence: 99%

Schema decision trees for heterogeneous JSON arrays

Goulet¹

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Due to the popularity of the JavaScript Object Notation (JSON), a need has arisen for the creation of schema documents for the purpose of validating the content of other JSON documents. Existing automatic schema generation tools, however, have not adequately considered the scenario of an array of JSON objects with different types of structures. These tools work off the assumption that all objects have the same structure, and thus, only generate a single schema combining them together.To address this problem, this thesis looks to improve upon schema generation for heterogeneous JSON arrays. We develop an algorithm to determine a set of keys that identifies what type of structure each element has. These keys are then used as the basis for a schema decision tree.

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XML clustering: a review of structural approaches

Cited by 12 publications

References 85 publications

Measuring structural similarity between RDF graphs

Measuring structural similarity between RDF graphs

Clustering XML documents by patterns

Schema decision trees for heterogeneous JSON arrays

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