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/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Numerical and Computational Mathematics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Institute of Information and Communication Technology, Bulgarian Academy of Science, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.410344.6", 
          "name": [
            "Institute of Information and Communication Technology, Bulgarian Academy of Science, Sofia, Bulgaria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fidanova", 
        "givenName": "Stefka", 
        "id": "sg:person.011173106320.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011173106320.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Science, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.493309.4", 
          "name": [
            "Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Science, Sofia, Bulgaria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Roeva", 
        "givenName": "Olympia", 
        "id": "sg:person.015745057111.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015745057111.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "DLCS University of Mlaga, 29071, Mlaga, Spain", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "DLCS University of Mlaga, 29071, Mlaga, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Luque", 
        "givenName": "Gabriel", 
        "id": "sg:person.011334142553.21", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011334142553.21"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2018-09-28", 
    "datePublishedReg": "2018-09-28", 
    "description": "The workforce planning is a difficult optimization problem. It is important real life problem which helps organizations to determine workforce which they need. A workforce planning problem is very complex and needs special algorithms to be solved using reasonable computational resources. The problem consists to select set of employers from a set of available workers and to assign this staff to the tasks to be performed. The objective is to minimize the costs associated to the human resources needed to fulfil the work requirements. A good workforce planing is important for an organization to accomplish its objectives. The complexity of this problem does not allow the application of exact methods for instances of realistic size. Therefore we will apply Ant Colony Optimization (ACO) method which is a stochastic method for solving combinatorial optimization problems. On this paper we focus on influence of the parameters on ACO algorithm performance.", 
    "editor": [
      {
        "familyName": "Georgiev", 
        "givenName": "Krassimir", 
        "type": "Person"
      }, 
      {
        "familyName": "Todorov", 
        "givenName": "Michail", 
        "type": "Person"
      }, 
      {
        "familyName": "Georgiev", 
        "givenName": "Ivan", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-97277-0_10", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-97276-3", 
        "978-3-319-97277-0"
      ], 
      "name": "Advanced Computing in Industrial Mathematics", 
      "type": "Book"
    }, 
    "keywords": [
      "optimization problem", 
      "difficult optimization problems", 
      "combinatorial optimization problems", 
      "ACO algorithm performance", 
      "ant colony optimization method", 
      "ant colony optimization algorithm", 
      "reasonable computational resources", 
      "colony optimization algorithm", 
      "important real-life problem", 
      "stochastic method", 
      "workforce planning problem", 
      "real-life problems", 
      "optimization method", 
      "set of employers", 
      "optimization algorithm", 
      "exact method", 
      "planning problem", 
      "realistic size", 
      "computational resources", 
      "algorithm parameters", 
      "special algorithm", 
      "algorithm performance", 
      "available workers", 
      "problem", 
      "life problems", 
      "algorithm", 
      "parameters", 
      "set", 
      "resources", 
      "planning", 
      "task", 
      "complexity", 
      "human resources", 
      "requirements", 
      "method", 
      "instances", 
      "applications", 
      "organization", 
      "performance", 
      "cost", 
      "work requirements", 
      "objective", 
      "workforce planning", 
      "size", 
      "influence", 
      "staff", 
      "planing", 
      "workforce", 
      "workers", 
      "employers", 
      "paper"
    ], 
    "name": "Ant Colony Optimization Algorithm for Workforce Planning: Influence of the Algorithm Parameters", 
    "pagination": "119-128", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1107272525"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-97277-0_10"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-97277-0_10", 
      "https://app.dimensions.ai/details/publication/pub.1107272525"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-05-10T10:47", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/chapter/chapter_322.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-319-97277-0_10"
  }
]

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.

141 TRIPLES      23 PREDICATES      76 URIs      69 LITERALS      7 BLANK NODES