Ontology type: schema:ScholarlyArticle
2020-04-12
AUTHORSBhatawdekar Ramesh Murlidhar, Deepak Kumar, Danial Jahed Armaghani, Edy Tonnizam Mohamad, Bishwajit Roy, Binh Thai Pham
ABSTRACTBlasting is an economical technique for rock breaking in hard rock excavation. One of its complex undesired environmental effects is flyrock, which may result in human injuries, fatalities and property damage. Because previously developed techniques for predicting flyrock are having less accuracy, this paper develops a new hybrid intelligent system of extreme learning machine (ELM) optimized by biogeography-based optimization (BBO) for prediction of flyrock distance resulting from blasting in a mine. In the BBO-ELM system, the role of BBO is to optimize the weights and biases of ELM. For comparison purposes, another hybrid model, i.e., particle swarm optimization (PSO)-ELM and a pre-developed ELM model were also applied and proposed. To do so, 262 datasets including burden to spacing ratio, hole diameter, powder factor, stemming, maximum charge per delay and hole depth as input variables and flyrock distance as system output were considered and used. Many models with different combinations of training and testing datasets have been constructed to identify the best predictive model in estimating flyrock. The results indicate capability of the newly developed BBO-ELM model for predicting flyrock distance. The coefficient of determination, coefficient of persistence and root mean square error values of (0.93, 0.93 and 21.51), (0.94, 0.95 and 18.84) and (0.79, 0.85 and 32.29) were obtained for testing datasets of PSO-ELM, BBO-ELM and ELM model, respectively, which reveal that the BBO-ELM is a powerful model for predicting flyrock induced by blasting. The developed BBO-ELM model can be introduced as a new, capable and applicable model for solving engineering problems. More... »
PAGES4103-4120
http://scigraph.springernature.com/pub.10.1007/s11053-020-09676-6
DOIhttp://dx.doi.org/10.1007/s11053-020-09676-6
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1126634256
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/2008/05",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Environmental Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Engineering",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0502",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Environmental Science and Management",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0914",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Resources Engineering and Extractive Metallurgy",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Geotropik - Centre of Tropical Geoengineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia",
"id": "http://www.grid.ac/institutes/grid.410877.d",
"name": [
"Geotropik - Centre of Tropical Geoengineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia"
],
"type": "Organization"
},
"familyName": "Murlidhar",
"givenName": "Bhatawdekar Ramesh",
"id": "sg:person.016001401373.08",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016001401373.08"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Civil Engineering, National Institute of Technology Patna, Ashok Raj Path, 800005, Patna, India",
"id": "http://www.grid.ac/institutes/grid.444650.7",
"name": [
"Department of Civil Engineering, National Institute of Technology Patna, Ashok Raj Path, 800005, Patna, India"
],
"type": "Organization"
},
"familyName": "Kumar",
"givenName": "Deepak",
"id": "sg:person.012055724152.78",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012055724152.78"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam",
"id": "http://www.grid.ac/institutes/grid.444812.f",
"name": [
"Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam"
],
"type": "Organization"
},
"familyName": "Jahed Armaghani",
"givenName": "Danial",
"id": "sg:person.012214152011.74",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012214152011.74"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Geotropik - Centre of Tropical Geoengineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia",
"id": "http://www.grid.ac/institutes/grid.410877.d",
"name": [
"Geotropik - Centre of Tropical Geoengineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia"
],
"type": "Organization"
},
"familyName": "Mohamad",
"givenName": "Edy Tonnizam",
"id": "sg:person.010264523752.19",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010264523752.19"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, India",
"id": "http://www.grid.ac/institutes/grid.444650.7",
"name": [
"Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, India"
],
"type": "Organization"
},
"familyName": "Roy",
"givenName": "Bishwajit",
"id": "sg:person.010264367327.40",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010264367327.40"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Institute of Research and Development, Duy Tan University, 550000, Da Nang, Vietnam",
"id": "http://www.grid.ac/institutes/grid.444918.4",
"name": [
"Institute of Research and Development, Duy Tan University, 550000, Da Nang, Vietnam"
],
"type": "Organization"
},
"familyName": "Pham",
"givenName": "Binh Thai",
"id": "sg:person.016647207523.54",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016647207523.54"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s11053-018-9424-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1109759308",
"https://doi.org/10.1007/s11053-018-9424-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11053-019-09611-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1123791357",
"https://doi.org/10.1007/s11053-019-09611-4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10064-014-0657-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016555821",
"https://doi.org/10.1007/s10064-014-0657-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12665-015-4983-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041196690",
"https://doi.org/10.1007/s12665-015-4983-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12517-013-1174-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038954491",
"https://doi.org/10.1007/s12517-013-1174-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10064-019-01626-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1123268773",
"https://doi.org/10.1007/s10064-019-01626-8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11053-019-09605-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1123594088",
"https://doi.org/10.1007/s11053-019-09605-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-019-00726-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1112634042",
"https://doi.org/10.1007/s00366-019-00726-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10706-015-9869-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008803471",
"https://doi.org/10.1007/s10706-015-9869-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-017-0568-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1092907006",
"https://doi.org/10.1007/s00366-017-0568-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11053-020-09616-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1124194648",
"https://doi.org/10.1007/s11053-020-09616-4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11053-019-09515-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1117740481",
"https://doi.org/10.1007/s11053-019-09515-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-015-0400-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003281017",
"https://doi.org/10.1007/s00366-015-0400-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12517-015-1908-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025927733",
"https://doi.org/10.1007/s12517-015-1908-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-019-04663-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1123266113",
"https://doi.org/10.1007/s00521-019-04663-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-018-3717-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1107055053",
"https://doi.org/10.1007/s00521-018-3717-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12559-014-9255-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005939490",
"https://doi.org/10.1007/s12559-014-9255-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-019-00849-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1120475015",
"https://doi.org/10.1007/s00366-019-00849-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12517-009-0091-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003408679",
"https://doi.org/10.1007/s12517-009-0091-8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11600-019-00304-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1114502390",
"https://doi.org/10.1007/s11600-019-00304-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-019-00816-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1117731285",
"https://doi.org/10.1007/s00366-019-00816-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10064-010-0298-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010242509",
"https://doi.org/10.1007/s10064-010-0298-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12665-016-5524-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039856740",
"https://doi.org/10.1007/s12665-016-5524-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-019-00711-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1111778658",
"https://doi.org/10.1007/s00366-019-00711-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-016-0497-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053003292",
"https://doi.org/10.1007/s00366-016-0497-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-018-0596-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1101330868",
"https://doi.org/10.1007/s00366-018-0596-4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1008306431147",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009040383",
"https://doi.org/10.1023/a:1008306431147"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10898-016-0455-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028035110",
"https://doi.org/10.1007/s10898-016-0455-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11053-019-09519-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1118016732",
"https://doi.org/10.1007/s11053-019-09519-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00603-016-1015-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033227644",
"https://doi.org/10.1007/s00603-016-1015-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11053-019-09532-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1120092086",
"https://doi.org/10.1007/s11053-019-09532-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-019-00808-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1117495481",
"https://doi.org/10.1007/s00366-019-00808-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-016-2434-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009507452",
"https://doi.org/10.1007/s00521-016-2434-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s13762-016-0979-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033172041",
"https://doi.org/10.1007/s13762-016-0979-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-016-0463-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027620432",
"https://doi.org/10.1007/s00366-016-0463-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1012771025575",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036497930",
"https://doi.org/10.1023/a:1012771025575"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10706-017-0356-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1091477574",
"https://doi.org/10.1007/s10706-017-0356-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11053-019-09470-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1112475471",
"https://doi.org/10.1007/s11053-019-09470-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-018-03965-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1111154918",
"https://doi.org/10.1007/s00521-018-03965-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-015-0402-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022416262",
"https://doi.org/10.1007/s00366-015-0402-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-015-0415-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006981814",
"https://doi.org/10.1007/s00366-015-0415-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12665-016-6335-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009284080",
"https://doi.org/10.1007/s12665-016-6335-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-011-0631-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039536792",
"https://doi.org/10.1007/s00521-011-0631-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-016-2746-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030713151",
"https://doi.org/10.1007/s00521-016-2746-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11053-019-09503-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1116997542",
"https://doi.org/10.1007/s11053-019-09503-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12517-012-0703-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032640964",
"https://doi.org/10.1007/s12517-012-0703-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-019-00770-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1114039927",
"https://doi.org/10.1007/s00366-019-00770-9"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11600-019-00268-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1112378322",
"https://doi.org/10.1007/s11600-019-00268-4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10064-016-0872-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006311390",
"https://doi.org/10.1007/s10064-016-0872-8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s006030050049",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044493601",
"https://doi.org/10.1007/s006030050049"
],
"type": "CreativeWork"
}
],
"datePublished": "2020-04-12",
"datePublishedReg": "2020-04-12",
"description": "Blasting is an economical technique for rock breaking in hard rock excavation. One of its complex undesired environmental effects is flyrock, which may result in human injuries, fatalities and property damage. Because previously developed techniques for predicting flyrock are having less accuracy, this paper develops a new hybrid intelligent system of extreme learning machine (ELM) optimized by biogeography-based optimization (BBO) for prediction of flyrock distance resulting from blasting in a mine. In the BBO-ELM system, the role of BBO is to optimize the weights and biases of ELM. For comparison purposes, another hybrid model, i.e., particle swarm optimization (PSO)-ELM and a pre-developed ELM model were also applied and proposed. To do so, 262 datasets including burden to spacing ratio, hole diameter, powder factor, stemming, maximum charge per delay and hole depth as input variables and flyrock distance as system output were considered and used. Many models with different combinations of training and testing datasets have been constructed to identify the best predictive model in estimating flyrock. The results indicate capability of the newly developed BBO-ELM model for predicting flyrock distance. The coefficient of determination, coefficient of persistence and root mean square error values of (0.93, 0.93 and 21.51), (0.94, 0.95 and 18.84) and (0.79, 0.85 and 32.29) were obtained for testing datasets of PSO-ELM, BBO-ELM and ELM model, respectively, which reveal that the BBO-ELM is a powerful model for predicting flyrock induced by blasting. The developed BBO-ELM model can be introduced as a new, capable and applicable model for solving engineering problems.",
"genre": "article",
"id": "sg:pub.10.1007/s11053-020-09676-6",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1136218",
"issn": [
"0961-1444",
"1520-7439"
],
"name": "Natural Resources Research",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "6",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "29"
}
],
"keywords": [
"flyrock distance",
"hard rock excavation",
"extreme learning machine",
"ELM model",
"powder factor",
"rock excavation",
"mine blasting",
"biogeography-based optimization",
"flyrock",
"particle swarm optimization",
"hole diameter",
"root mean square error (RMSE) values",
"hole depth",
"maximum charge",
"engineering problems",
"undesired environmental effects",
"system output",
"economical technique",
"biases of ELM",
"blasting",
"square error values",
"PSO-ELM",
"mean square error values",
"swarm optimization",
"comparison purposes",
"hybrid intelligent system",
"input variables",
"error values",
"property damage",
"coefficient of determination",
"human injuries",
"hybrid model",
"learning machine",
"optimization",
"intelligent systems",
"testing dataset",
"coefficient",
"different combinations",
"less accuracy",
"environmental effects",
"technique",
"new hybrid intelligent system",
"model",
"predictive model",
"mine",
"system",
"applicable model",
"capability",
"diameter",
"distance",
"machine",
"excavation",
"coefficient of persistence",
"depth",
"accuracy",
"output",
"prediction",
"charge",
"ratio",
"rocks",
"results",
"problem",
"delay",
"damage",
"values",
"combination",
"determination",
"effect",
"best predictive model",
"dataset",
"purpose",
"variables",
"weight",
"biases",
"factors",
"fatalities",
"stemming",
"role",
"powerful model",
"burden",
"training",
"persistence",
"injury",
"paper"
],
"name": "A Novel Intelligent ELM-BBO Technique for Predicting Distance of Mine Blasting-Induced Flyrock",
"pagination": "4103-4120",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1126634256"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s11053-020-09676-6"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s11053-020-09676-6",
"https://app.dimensions.ai/details/publication/pub.1126634256"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-20T07:38",
"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/article/article_861.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s11053-020-09676-6"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11053-020-09676-6'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11053-020-09676-6'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11053-020-09676-6'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11053-020-09676-6'
This table displays all metadata directly associated to this object as RDF triples.
396 TRIPLES
22 PREDICATES
161 URIs
101 LITERALS
6 BLANK NODES