XML version detection

Saccol, Deise de Brum; Edelweiss, Nina; Galante, Renata; Zaniolo, Carlo

doi:10.1145/1284420.1284441

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…A systematic approach to applying machine learning classifiers follows four phases: 1)Training; 2)Testing; 3)Validation; and 4)Classification [9,5]…”

Section: Methodsmentioning

confidence: 99%

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Automatic Identification of Ontology Versions Using Machine Learning Techniques

Allocca

2011

Lecture Notes in Computer Science

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When different versions of an ontology are published online, the links between them are often lost as the standard mechanisms (such as owl:versionInfo and owl:priorVersion) to expose these links are rarely used. This generates issues in scenarios where people or applications are required to make use of large scale, heterogenous ontology collections, implicitly containing multiple versions of ontologies. In this paper, we propose a method to detect automatically versioning links between ontologies which are available online through a Semantic Web search engine. Our approach is based on two main steps. The first step selects candidate pairs of ontologies by using versioning information expressed in their identifiers. In the second step, these candidate pairs are characterized through a set of features, including similarity measures, and classified by using Machine Learning Techniques, to distinguish the pairs that represent versions from the ones that do not. We discuss the features used, the methodology employed to train the classifiers and the precision obtained when applying this approach on the collection of ontologies of the Watson Semantic Web search engine.

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“…A systematic approach to applying machine learning classifiers follows four phases: 1)Training; 2)Testing; 3)Validation; and 4)Classification [9,5]…”

Section: Methodsmentioning

confidence: 99%

“…Closer to our work but applied on XML documents, [9] attempts to identify versions of documents using naive Bayes classifiers. The authors use a similarity measure dedicated to XML documents as input for the classifiers, and apply the approach on a set of automatically generated documents in a closed domain.…”

Section: Related Workmentioning

confidence: 99%

Automatic Identification of Ontology Versions Using Machine Learning Techniques

Allocca

2011

Lecture Notes in Computer Science

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“…operation : (type, position, v, v , fingerprint(position)) (8) We stored the delta itself within an XML file, using the root node <delta>. Edit operations are mapped to the XML domain as follows:…”

Section: Delta Formatmentioning

confidence: 99%

“…As the context of edit operations is quite small, this technique is not promising for our scenario. Another approach tailored for XML documents was presented in [8]. As our fingerprint technique maps to linear data, thus dissolving the tree structure, this approach is not applicable, either.…”

Section: Related Workmentioning

confidence: 99%

Merging changes in XML documents using reliable context fingerprints

Rönnau

Pauli

Borghoff

2008

Proceedings of the Eighth ACM Symposium on Document Engineering

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Different dialects of XML have emerged as ubiquitous document exchange formats. For effective collaboration based on such documents, the capability to propagate edit operations performed on a document is indispensable. In order to avoid the transmission of whole documents, deltas are used to describe these edit operations, allowing the construction of a new version of a document. However, patching a document with a delta it was not generated for is error-prone, and any insert or delete operations performed on the document are likely to affect all subsequent paths within that document.In this paper, we present a delta format for XML documents that uses context-aware fingerprints to identify edit operations. This allows our XML patch procedure to find the correct position of an edit operation, even if the document was updated in the meantime. Possible conflicts are detected. Experimental results show the reliability of the presented fingerprinting technique and prove the high quality of the resulting patched documents.

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Wikipedia Revision Graph Extraction Based on N-Gram Cover

Iwaihara

2012

Web-Age Information Management

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XML version detection

Cited by 6 publications

References 27 publications

Automatic Identification of Ontology Versions Using Machine Learning Techniques

Automatic Identification of Ontology Versions Using Machine Learning Techniques

Merging changes in XML documents using reliable context fingerprints

Wikipedia Revision Graph Extraction Based on N-Gram Cover

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