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/2008/3468", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "name": "Vinay Deolalikar", 
        "type": "Person"
      }, 
      {
        "name": "Hernan Laffitte", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf00058655", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002929950", 
          "https://doi.org/10.1007/bf00058655"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00058655", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002929950", 
          "https://doi.org/10.1007/bf00058655"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/505282.505283", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023316280"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1613/jair.953", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105579550"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2015-10-20T00:00", 
    "description": "
A method of generating training documents for training a classifying device comprises, with a processor, sampling from a distribution of words in a number of original documents, and creating a number of pseudo-documents from the distribution of words, the pseudo-documents comprising a similar distribution of words as the original documents. A device for classifying textual documents comprises a processor; and a memory communicatively coupled to the processor, the memory comprising a sampling module to, when executed by the processor, determine the distribution of words in a number of original documents, a pseudo-document creation module to, when executed by the processor, create a number of pseudo-documents from the distribution of words, the pseudo-documents comprising a similar distribution of words as the original documents, and a training module to, when executed by the processor, train the device to classify textual documents based on the pseudo-documents.
", 
    "id": "sg:patent.US-9165258-B2", 
    "keywords": [
      "method", 
      "classifying", 
      "processor", 
      "sampling", 
      "distribution", 
      "document", 
      "similar distribution", 
      "Equipment and Supply", 
      "memory", 
      "training module", 
      "train"
    ], 
    "name": "Generating training documents", 
    "recipient": [
      {
        "id": "https://www.grid.ac/institutes/grid.418547.b", 
        "type": "Organization"
      }
    ], 
    "sameAs": [
      "https://app.dimensions.ai/details/patent/US-9165258-B2"
    ], 
    "sdDataset": "patents", 
    "sdDatePublished": "2019-04-18T10:16", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-patents-target-20190320-rc/data/sn-export/402f166718b70575fb5d4ffe01f064d1/0000100128-0000352499/json_export_01090.jsonl", 
    "type": "Patent"
  }
]

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.

47 TRIPLES      15 PREDICATES      28 URIs      19 LITERALS      2 BLANK NODES