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/2008/15", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Commerce, Management, Tourism and Services", 
        "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/2008/1503", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Business and Management", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 21250, Baltimore, MD, USA", 
          "id": "http://www.grid.ac/institutes/grid.266673.0", 
          "name": [
            "Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 21250, Baltimore, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Ye", 
        "id": "sg:person.016231355743.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016231355743.14"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 21250, Baltimore, MD, USA", 
          "id": "http://www.grid.ac/institutes/grid.266673.0", 
          "name": [
            "Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 21250, Baltimore, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Peng", 
        "givenName": "Yun", 
        "id": "sg:person.01136741416.72", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136741416.72"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2003", 
    "datePublishedReg": "2003-01-01", 
    "description": "In this paper, we describe our on-going research on uncertainty analysis in Multi-agent Systems for Supply Chain Management (MASCM). In a MASCM, an agent consists of automation processes within a legal entity in the specific supply chain network. It conducts supply chain planning, execution and cooperation on behalf of its owner. Each day these agents have to process a large volume of data from different sources with mixed signals not to be anticipated in advance. Thus, one challenge every agent has to face in this volatile environment is to quickly identify the impact of unexpected events, and take proper adjustments in both local procedures and related cross-boundary interactions. To facilitate the study of uncertainty in the complex system of MASCM, we model agent system behaviors by abstracting its significant operational aspects as observation, propagation and update of uncertainty ifnromation. The resulting theoretical model, called an extended Bayesian Belief Network (eBBN), may serve as the basis for developing an uncertainty management component for a large-scale electronic supply chain system. We also briefly describe ways this model can be used to solve different supply chain tasks and some simulation results that demonstrate the power of this model in improving the system performance.", 
    "editor": [
      {
        "familyName": "Truszkowski", 
        "givenName": "Walt", 
        "type": "Person"
      }, 
      {
        "familyName": "Hinchey", 
        "givenName": "Mike", 
        "type": "Person"
      }, 
      {
        "familyName": "Rouff", 
        "givenName": "Chris", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-45173-0_25", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-40725-6", 
        "978-3-540-45173-0"
      ], 
      "name": "Innovative Concepts for Agent-Based Systems", 
      "type": "Book"
    }, 
    "keywords": [
      "multi-agent systems", 
      "supply chain management", 
      "agent system behaviors", 
      "chain management", 
      "supply chain tasks", 
      "supply chain planning", 
      "belief network model", 
      "Bayesian belief networks", 
      "belief network", 
      "automation process", 
      "supply chain network", 
      "supply chain system", 
      "chain planning", 
      "network model", 
      "management components", 
      "Bayesian belief network model", 
      "system behavior", 
      "complex systems", 
      "chain network", 
      "volatile environment", 
      "system performance", 
      "unexpected events", 
      "simulation results", 
      "large volumes", 
      "network", 
      "chain system", 
      "operational aspects", 
      "execution", 
      "different sources", 
      "system", 
      "study of uncertainty", 
      "cross-boundary interactions", 
      "task", 
      "model", 
      "mixed signals", 
      "update", 
      "environment", 
      "planning", 
      "management", 
      "local procedures", 
      "performance", 
      "uncertainty analysis", 
      "owners", 
      "challenges", 
      "entities", 
      "legal entities", 
      "cooperation", 
      "behalf", 
      "way", 
      "uncertainty", 
      "data", 
      "advances", 
      "research", 
      "aspects", 
      "agents", 
      "power", 
      "process", 
      "signals", 
      "proper adjustment", 
      "components", 
      "results", 
      "basis", 
      "source", 
      "propagation", 
      "behavior", 
      "analysis", 
      "interaction", 
      "procedure", 
      "theoretical model", 
      "volume", 
      "impact", 
      "events", 
      "adjustment", 
      "observations", 
      "study", 
      "paper", 
      "days"
    ], 
    "name": "An Extended Bayesian Belief Network Model of Multi-agent Systems for Supply Chain Managements", 
    "pagination": "335-346", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1018663987"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-45173-0_25"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-45173-0_25", 
      "https://app.dimensions.ai/details/publication/pub.1018663987"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-08-04T17:20", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/chapter/chapter_385.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-540-45173-0_25"
  }
]

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.

161 TRIPLES      22 PREDICATES      104 URIs      95 LITERALS      7 BLANK NODES