JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Sorbonne Universit\u00e9- LIP6, 4 place Jussieu, 75005, Paris, France", 
          "id": "http://www.grid.ac/institutes/grid.462844.8", 
          "name": [
            "Sorbonne Universit\u00e9- LIP6, 4 place Jussieu, 75005, Paris, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lasserre", 
        "givenName": "Marvin", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Airbus AI Research, 22 rue du Gouverneur G\u00e9n\u00e9ral Ebou\u00e9, 92130, Issy les Moulineaux, France", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Airbus AI Research, 22 rue du Gouverneur G\u00e9n\u00e9ral Ebou\u00e9, 92130, Issy les Moulineaux, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lebrun", 
        "givenName": "R\u00e9gis", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Sorbonne Universit\u00e9- LIP6, 4 place Jussieu, 75005, Paris, France", 
          "id": "http://www.grid.ac/institutes/grid.462844.8", 
          "name": [
            "Sorbonne Universit\u00e9- LIP6, 4 place Jussieu, 75005, Paris, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wuillemin", 
        "givenName": "Pierre-Henri", 
        "id": "sg:person.010323340640.84", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010323340640.84"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-3-642-12465-5_4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031567327", 
          "https://doi.org/10.1007/978-3-642-12465-5_4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-36127-8_35", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023803717", 
          "https://doi.org/10.1007/3-540-36127-8_35"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-11259-6_64-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084933692", 
          "https://doi.org/10.1007/978-3-319-11259-6_64-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-59365-9_8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023644261", 
          "https://doi.org/10.1007/978-3-642-59365-9_8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-93437-7_28", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012429812", 
          "https://doi.org/10.1007/978-3-642-93437-7_28"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4613-0303-9_33", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008061296", 
          "https://doi.org/10.1007/978-1-4613-0303-9_33"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2021-06-26", 
    "datePublishedReg": "2021-06-26", 
    "description": "Modeling high-dimensional multivariate distributions is a computationally challenging task. In the discrete case, Bayesian networks have been successfully used to reduce the complexity and to simplify the problem. However, they lack of a general model for continuous variables. In order to overcome this problem, Elidan (2010) proposed the model of copula Bayesian networks that parametrizes Bayesian networks using copula functions. We propose a new learning algorithm for this model based on a PC algorithm and a conditional independence test proposed by Bouezmarni et al. (2009). This test being non-parametric, no model assumptions are made allowing it to be as general as possible. This algorithm is compared on generated data with the parametric method proposed by Elidan (2010) and proves to have better results.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s10472-021-09754-2", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1043955", 
        "issn": [
          "1012-2443", 
          "1573-7470"
        ], 
        "name": "Annals of Mathematics and Artificial Intelligence", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "10-11", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "89"
      }
    ], 
    "keywords": [
      "Bayesian network", 
      "new learning algorithm", 
      "conditional independence tests", 
      "copula Bayesian network", 
      "learning algorithm", 
      "PC algorithm", 
      "continuous Bayesian networks", 
      "network", 
      "algorithm", 
      "independence test", 
      "better results", 
      "general model", 
      "task", 
      "learning", 
      "complexity", 
      "discrete case", 
      "parametric methods", 
      "model", 
      "constraints", 
      "multivariate distributions", 
      "copula function", 
      "order", 
      "model assumptions", 
      "data", 
      "method", 
      "et al", 
      "continuous variables", 
      "assumption", 
      "results", 
      "function", 
      "cases", 
      "variables", 
      "test", 
      "distribution", 
      "al", 
      "problem", 
      "high-dimensional multivariate distributions", 
      "Elidan", 
      "Bouezmarni et al", 
      "non-parametric continuous bayesian networks"
    ], 
    "name": "Constraint-based learning for non-parametric continuous bayesian networks", 
    "pagination": "1035-1052", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1139150590"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10472-021-09754-2"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10472-021-09754-2", 
      "https://app.dimensions.ai/details/publication/pub.1139150590"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-01-01T19:02", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_909.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s10472-021-09754-2"
  }
]

Download the RDF metadata as:  json-ld nt turtle xml License info

This table displays all metadata directly associated to this object as RDF triples.

137 TRIPLES      22 PREDICATES      71 URIs      57 LITERALS      6 BLANK NODES