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": "Institute AIFB, University of Karlsruhe, Germany", 
          "id": "http://www.grid.ac/institutes/grid.7892.4", 
          "name": [
            "Institute AIFB, University of Karlsruhe, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stojanovic", 
        "givenName": "Nenad", 
        "id": "sg:person.011564632227.91", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011564632227.91"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute AIFB, University of Karlsruhe, Germany", 
          "id": "http://www.grid.ac/institutes/grid.7892.4", 
          "name": [
            "Institute AIFB, University of Karlsruhe, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Studer", 
        "givenName": "Rudi", 
        "id": "sg:person.01024266115.06", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01024266115.06"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2004", 
    "datePublishedReg": "2004-01-01", 
    "description": "Although several approaches have been proposed for modelling an on-line shop assistant, recent customer\u2019s analyses show that they miss some assistance in the buying process. The common problem is that the behaviour of an on-line shop assistant is modelled on the procedural level, i.e. like a workflow. In this paper we present an approach that models this behaviour on the knowledge level, i.e. it takes into account not only which actions (questions) a shop assistant will perform, but also which goals he wants to achieve by taking an action. As a generic reasoning pattern of such an e-shop agent we use the cover-and-differentiate problem-solving method, a method very successfully applied in various diagnosis and classification tasks. In that way, we can (i) model the question-answering process such that the minimal set of useful questions will be provided to a user, (ii) easily reinterpret and fine-tune shopping strategies that exist in other e-shop portals and (iii) design and integrate new methods into generic reasoning pattern. We present an evaluation study which illustrates these benefits.", 
    "editor": [
      {
        "familyName": "Motta", 
        "givenName": "Enrico", 
        "type": "Person"
      }, 
      {
        "familyName": "Shadbolt", 
        "givenName": "Nigel R", 
        "type": "Person"
      }, 
      {
        "familyName": "Stutt", 
        "givenName": "Arthur", 
        "type": "Person"
      }, 
      {
        "familyName": "Gibbins", 
        "givenName": "Nick", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-30202-5_24", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-23340-4", 
        "978-3-540-30202-5"
      ], 
      "name": "Engineering Knowledge in the Age of the Semantic Web", 
      "type": "Book"
    }, 
    "keywords": [
      "problem-solving methods", 
      "question-answering process", 
      "shop agents", 
      "reasoning patterns", 
      "classification task", 
      "customer analysis", 
      "minimal set", 
      "knowledge level", 
      "assistants", 
      "buying process", 
      "procedural level", 
      "shop assistants", 
      "users", 
      "workflow", 
      "new method", 
      "evaluation study", 
      "portal", 
      "task", 
      "common problem", 
      "method", 
      "set", 
      "useful questions", 
      "goal", 
      "design", 
      "assistance", 
      "process", 
      "way", 
      "shopping strategies", 
      "benefits", 
      "strategies", 
      "analysis", 
      "patterns", 
      "behavior", 
      "account", 
      "action", 
      "agents", 
      "questions", 
      "levels", 
      "diagnosis", 
      "cover", 
      "study", 
      "approach", 
      "paper", 
      "problem", 
      "line shop assistant", 
      "recent customer\u2019s analyses", 
      "generic reasoning pattern", 
      "differentiate problem-solving method", 
      "fine-tune shopping strategies", 
      "shop portals"
    ], 
    "name": "On the Knowledge Level of an On-line Shop Assistant", 
    "pagination": "354-370", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1013898550"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-30202-5_24"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-30202-5_24", 
      "https://app.dimensions.ai/details/publication/pub.1013898550"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-01-01T19:09", 
    "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/chapter/chapter_162.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-540-30202-5_24"
  }
]

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.

132 TRIPLES      23 PREDICATES      76 URIs      69 LITERALS      7 BLANK NODES