Matches in ScholarlyData for { ?s ?p ?o. }
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- 134 creator alejandro-vaisman.
- 134 creator lorena-etcheverry.
- 134 type InProceedings.
- 134 label "Enhancing OLAP Analysis with Web Cubes".
- 134 sameAs 134.
- 134 abstract "Traditional On-Line Analytical Processing (OLAP) tools have proven to be successful in analyzing large sets of enterprise data. For today's business dynamics, sometimes these highly curated data is not enough. External data (particularly web data), may be useful to enhance local analysis. In this paper we discuss the extraction of multidimensional data from web sources, and their representation in RDFS. We introduce Open Cubes, an RDFS vocabulary for the specification and publication of multidimensional cubes on the Semantic Web, and show how classical OLAP operations can be implemented over Open Cubes using SPARQL 1.1, without the need of mapping the multidimensional information to the local database (the usual approach to multidimensional analysis of Semantic Web data). We show that our approach is plausible for the data sizes that can usually be retrieved to enhance local data repositories.".
- 134 hasAuthorList authorList.
- 134 isPartOf proceedings.
- 134 keyword "OLAP".
- 134 keyword "data analysis".
- 134 keyword "web data".
- 134 title "Enhancing OLAP Analysis with Web Cubes".
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- item-1 hasContent lorena-etcheverry.
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- item-2 hasContent alejandro-vaisman.
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- 136 creator audun-stolpe.
- 136 creator martin-g.-skjaeveland.
- 136 type InProceedings.
- 136 label "Preserving Information Content in RDF using Bounded Homomorphisms".
- 136 sameAs 136.
- 136 abstract "The topic of study in the present paper is the class of RDF homomorphisms that substitute one predicate for another throughout an set of RDF triples, under the condition that the predicate in question is not also a subject or object. These maps turn out to be suitable for reasoning about similarities in information content between two or more RDF graphs. As such they are very useful e.g. for migrating data from one RDF vocabulary to another. In this paper we address a particular instance of this problem and try to provide an answer to the question of when we are licensed to say that data is being transformed, reused or merged in a non-distortive manner. We place this problem in the context of RDF and Linked Data, and study the problem in relation to SPARQL construct queries.".
- 136 hasAuthorList authorList.
- 136 isPartOf proceedings.
- 136 keyword "Data Transformation".
- 136 keyword "Homomorphisms".
- 136 keyword "RDF".
- 136 keyword "SPARQL".
- 136 title "Preserving Information Content in RDF using Bounded Homomorphisms".
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- item-1 hasContent audun-stolpe.
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- item-2 hasContent martin-g.-skjaeveland.
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- 139 creator nor-azlinayati-abdul-manaf.
- 139 creator robert-stevens.
- 139 creator sean-bechhofer.
- 139 type InProceedings.
- 139 label "The current state of SKOS vocabularies on the Web".
- 139 sameAs 139.
- 139 abstract "We present a survey of the current state of Simple Knowledge Organization System (SKOS) vocabularies on the Web. Candidate vocabularies were gathered through collections and web crawling, with 478 identified as complying to a given definition of a SKOS vocabulary. Analyses were then conducted included investigation of the use of SKOS constructs; the use of SKOS semantic relations and lexical labels; and the structure of vocabularies in terms of the hierarchical and associative relations, branching factors and the depth of the vocabularies. Even though SKOS concepts are considered to be the core of SKOS vocabularies, our findings were that not all SKOS vocabularies published explicitly declared SKOS concepts in the vocabularies. Almost one-third of the SKOS vocabularies collected fall into the category of "term lists", with no use of any SKOS semantic relations. As concept labelling is core to SKOS vocabularies a surprising find is that not all SKOS vocabularies use SKOS lexical labels, whether '{skos:prefLabel' or 'skos:altLabel', for their concepts. The branching factors and maximum depth of the vocabularies have no direct relationship to the size of the vocabularies. We also observed some common modelling slips found in SKOS vocabularies. The survey is useful when considering, for example, converting artefacts such as OWL ontologies into SKOS, where a definition of typicality of SKOS vocabularies could be used to guide the conversion. Moreover, the survey results can serve to provide a better understanding of the modelling styles of the SKOS vocabularies published on the Web, especially when considering the creation of applications that utilizes these vocabularies.".
- 139 hasAuthorList authorList.
- 139 isPartOf proceedings.
- 139 keyword "SKOS metric".
- 139 keyword "SKOS vocabularies".
- 139 keyword "branching factors".
- 139 keyword "modelling styles".
- 139 keyword "vocabulary structure".
- 139 title "The current state of SKOS vocabularies on the Web".
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- item-1 hasContent nor-azlinayati-abdul-manaf.
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- item-2 hasContent sean-bechhofer.
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- item-3 hasContent robert-stevens.
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- 154 creator andres-garcia-silva.
- 154 creator jeon-hyung-kang.
- 154 creator kristina-lerman.
- 154 creator oscar-corcho.
- 154 type InProceedings.
- 154 label "Characterising Emergent Semantics in Twitter Lists".
- 154 sameAs 154.
- 154 abstract "Twitter lists constitute a form of organising Twitter users into sets, and can be created and maintained by any user in Twitter. In this paper we describe a characterisation approach of the emergent semantics in these lists, which consists in deriving semantic relations between lists and users by analyzing the co-occurrence of keywords in list names. We use the vector space model and Latent Dirichlet Allocation to obtain similar keywords according to co-occurrence patterns. These results are then compared to similarity measures relying on the WordNet synset hierarchy and to existing Linked Data sets. Results show that co-occurrence of keywords based on members of the lists produce more synonyms and more correlated results to that of WordNet similarity measures.".
- 154 hasAuthorList authorList.
- 154 isPartOf proceedings.
- 154 keyword "Emergent semantics".
- 154 keyword "Similarity".
- 154 keyword "Twitter lists".
- 154 title "Characterising Emergent Semantics in Twitter Lists".
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