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/2008/17", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology and Cognitive 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"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1701", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Sapporo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.39158.36", 
          "name": [
            "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Sapporo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ogawa", 
        "givenName": "Akitoshi", 
        "id": "sg:person.016531377655.12", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016531377655.12"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Sapporo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.39158.36", 
          "name": [
            "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Sapporo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Omori", 
        "givenName": "Takashi", 
        "id": "sg:person.01263557346.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01263557346.07"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2004", 
    "datePublishedReg": "2004-01-01", 
    "description": "A characteristic feature of conventional intelligent agents is the amount of trials that are required for them to learn. Since the tasks they encounter change depending on the environment, it is difficult for a learning system to compress the learning time using a priori knowledge. In the real world, however, agents confront a whole range of new tasks one by one, and have to solve them one by one without consuming learning time. A serious problem for the real-world agent is the amount of learning time needed. We suppose that one reason for a long learning time is the nonuse of prior knowledge. It is natural to expect that a kind of fast adaptation to tasks would be possible when we reuse knowledge that is acquired from similar past experiences. For this problem, we propose a neural-network based learning system that can immediately or quickly solve new tasks by reusing knowledge already acquired. We adopt a navigation task as an example and show the effectiveness of our method in variations on the task by comparing its performance with other methods.", 
    "editor": [
      {
        "familyName": "Pal", 
        "givenName": "Nikhil Ranjan", 
        "type": "Person"
      }, 
      {
        "familyName": "Kasabov", 
        "givenName": "Nik", 
        "type": "Person"
      }, 
      {
        "familyName": "Mudi", 
        "givenName": "Rajani K.", 
        "type": "Person"
      }, 
      {
        "familyName": "Pal", 
        "givenName": "Srimanta", 
        "type": "Person"
      }, 
      {
        "familyName": "Parui", 
        "givenName": "Swapan Kumar", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-30499-9_62", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-23931-4", 
        "978-3-540-30499-9"
      ], 
      "name": "Neural Information Processing", 
      "type": "Book"
    }, 
    "keywords": [
      "learning system", 
      "learning time", 
      "navigation task", 
      "long learning time", 
      "neural learning systems", 
      "real-world agents", 
      "intelligent agents", 
      "amount of trial", 
      "real world", 
      "new tasks", 
      "similar past experiences", 
      "prior knowledge", 
      "task", 
      "task one", 
      "fast adaptation", 
      "system", 
      "past experience", 
      "immediate adaptation", 
      "knowledge", 
      "serious problem", 
      "environment", 
      "method", 
      "performance", 
      "effectiveness", 
      "time", 
      "features", 
      "adaptation", 
      "kind", 
      "example", 
      "amount", 
      "world", 
      "agents", 
      "one", 
      "experience", 
      "reasons", 
      "characteristic features", 
      "whole range", 
      "problem", 
      "nonuse", 
      "range", 
      "trials", 
      "variation", 
      "changes"
    ], 
    "name": "Knowledge Reusing Neural Learning System for Immediate Adaptation in Navigation Tasks", 
    "pagination": "409-415", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1000979818"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-30499-9_62"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-30499-9_62", 
      "https://app.dimensions.ai/details/publication/pub.1000979818"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-05-20T07:42", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/chapter/chapter_168.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-540-30499-9_62"
  }
]

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.

138 TRIPLES      23 PREDICATES      71 URIs      62 LITERALS      7 BLANK NODES