Survey on Common Strategies of Vocabulary Reuse in Linked Open Data Modeling

Schaible, Johann; Gottron, Thomas; Scherp, Ansgar

doi:10.1007/978-3-319-07443-6_31

Cited by 25 publications

(30 citation statements)

References 11 publications

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“…The set of features that categorize each recommendation candidate x is enlisted in Table 2. This set of features was derived from [29], which illustrated that the most common strategies and influencing factors to choose a vocabulary terms for reuse is its popularity and whether or not it is from a vocabulary that is already used. Features f1 to f3 represent the popularity of a vocabulary terms whereas feature f4 specifies whether the recommended term is from a vocabulary that is is already used in the query-SLP.…”

Section: Feature Computationmentioning

confidence: 99%

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TermPicker: Enabling the Reuse of Vocabulary Terms by Exploiting Data from the Linked Open Data Cloud

Schaible

Gottron²,

Scherp

2016

The Semantic Web. Latest Advances and New Domains

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Deciding which vocabulary terms to use when modeling data as Linked Open Data (LOD) is far from trivial. Choosing too general vocabulary terms, or terms from vocabularies that are not used by other LOD datasets, is likely to lead to a data representation, which will be harder to understand by humans and to be consumed by Linked data applications. In this technical report, we propose TermPicker: a novel approach for vocabulary reuse by recommending RDF types and properties based on exploiting the information on how other data providers on the LOD cloud use RDF types and properties to describe their data. To this end, we introduce the notion of so-called schema-level patterns (SLPs). They capture how sets of RDF types are connected via sets of properties within some data collection, e.g., within a dataset on the LOD cloud. TermPicker uses such SLPs and generates a ranked list of vocabulary terms for reuse. The lists of recommended terms are ordered by a ranking model which is computed using the machine learning approach Learning To Rank (L2R). TermPicker is evaluated based on the recommendation quality that is measured using the Mean Average Precision (MAP) and the Mean Reciprocal Rank at the first five positions (MRR@5). Our results illustrate an improvement of the recommendation quality by 29 − 36% when using SLPs compared to the beforehand investigated baselines of recommending solely popular vocabulary terms or terms from the same vocabulary. The overall best results are achieved using SLPs in conjunction with the Learning To Rank algorithm Random Forests.

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Section: Feature Computationmentioning

confidence: 99%

“…(ii) Evaluation of the SLP-feature's impact on the recommendation quality by comparing its recommendations to the baselines of recommending solely popular vocabulary terms and recommending terms from an already used vocabulary [29,24].…”

Section: Introductionmentioning

confidence: 99%

TermPicker: Enabling the Reuse of Vocabulary Terms by Exploiting Data from the Linked Open Data Cloud

Schaible

Gottron²,

Scherp

2016

The Semantic Web. Latest Advances and New Domains

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“…Closer to our work, Schaible et al reported on an empirical study involving 75 linked data experts and practitioners assessing reuse strategies based on various ranking decisions [7]. The goal is to find objective criteria for choosing which vocabularies to reuse and how many can be combined.…”

Section: Related Work and Discussionmentioning

confidence: 93%

Information content based ranking metric for linked open vocabularies

Atemezing

Troncy

2014

Proceedings of the 10th International Conference on Semantic Systems

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It is widely accepted that by controlling metadata, it is easier to publish high quality data on the web. Metadata, in the context of Linked Data, refers to vocabularies and ontologies used for describing data. With more and more data published on the web, the need for reusing controlled taxonomies and vocabularies is becoming more and more a necessity. Catalogues of vocabularies are generally a starting point to search for vocabularies based on search terms. Some recent studies recommend that it is better to reuse terms from "popular" vocabularies [4]. However, there is not yet an agreement on what makes a popular vocabulary since it depends on diverse criteria such as the number of properties, the number of datasets using part or the whole vocabulary, etc. In this paper, we propose a method for ranking vocabularies based on an information content metric which combines three features: (i) the datasets using the vocabulary, (ii) the outlinks from the vocabulary and (iii) the inlinks to the vocabulary. We applied this method to 366 vocabularies described in the LOV catalogue. The results are then compared with other catalogues which provide alternative rankings.

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“…relatively different vocabulary reuse strategies [14]. The two basic forms are  reusing classes and properties from existing vocabularies directly,  establishing links on schema-level, which means defining new classes as either subclasses or equivalent classes and properties as subproperties or equivalent properties of the classes and properties of the reused ontology.…”

Section: B Object and Data Properties Of The Ontologymentioning

confidence: 99%