Benchmarking applied to semantic conceptual models of linked financial data

  1. Sánchez-Cervantes, J.L. 3
  2. Rodríguez-Mazahua, L. 3
  3. Alor-Hernández, G. 3
  4. Sánchez-Ramírez, C. 3
  5. García-Alcaráz, J.L. 2
  6. Jimenez-Macias, E. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  2. 2 Universidad Autónoma de Ciudad Juárez
    info

    Universidad Autónoma de Ciudad Juárez

    Ciudad Juárez, México

    ROR https://ror.org/05fj8cf83

  3. 3 Instituto Tecnologico de Orizaba
    info

    Instituto Tecnologico de Orizaba

    Orizaba, México

    ROR https://ror.org/05vpj2s72

Mostrar afiliacións +
Revista:
Lecture Notes in Computer Science

ISSN: 0302-9743

Ano de publicación: 2015

Volume: 9416

Páxinas: 289-298

Tipo: Artigo

beta Ver similares en nube de resultados
DOI: 10.1007/978-3-319-26138-6_32 SCOPUS: 2-s2.0-84951120411 WoS: WOS:000374099700032 GOOGLE SCHOLAR

Outras publicacións en: Lecture Notes in Computer Science

Obxectivos de Desenvolvemento Sustentable

Resumo

Semantic modeling plays a central role in knowledge-based systems where information sharing and integration is a primary objective. Ontology and metadata description languages such as OWL (Web Ontology Language) and RDF(S) (Resource Description Framework Schema) are commonly the most used for representing semantic models and data. The graph-like structure adopted for semantic metadata representation allows simple and expressive queries by using SPARQL-based subgraph matching. While performance of such knowledgebased systems depends on multiple factors, in this work we present a mechanism to properly choice a semantic modeling pattern in order to significantly reduce the data query execution time. Based on this understanding, this work proposes a comparative analysis of different conceptual modeling approaches on the basis of financial domain. In order to show the efficiency/accuracy of our approach, an evaluation of SPARQL-based queries was performed against different modeled datasets. © Springer International Publishing Switzerland 2015.