http://scigraph.springernature.com/journal.1125588
ISSN0885-6125 | 1573-0565
DIMENSIONShttps://app.dimensions.ai/discover/publication?and_facet_source_title=jour.1125588
SCOPUS
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://scigraph.springernature.com/ontologies/product-market-codes/I21000",
"inDefinedTermSet": "http://scigraph.springernature.com/ontologies/product-market-codes/",
"name": "Artificial Intelligence",
"type": "DefinedTerm"
},
{
"id": "http://scigraph.springernature.com/ontologies/product-market-codes/T19000",
"inDefinedTermSet": "http://scigraph.springernature.com/ontologies/product-market-codes/",
"name": "Control, Robotics, Mechatronics",
"type": "DefinedTerm"
},
{
"id": "http://scigraph.springernature.com/ontologies/product-market-codes/I21000",
"inDefinedTermSet": "http://scigraph.springernature.com/ontologies/product-market-codes/",
"name": "Artificial Intelligence",
"type": "DefinedTerm"
},
{
"id": "http://scigraph.springernature.com/ontologies/product-market-codes/I19000",
"inDefinedTermSet": "http://scigraph.springernature.com/ontologies/product-market-codes/",
"name": "Simulation and Modeling ",
"type": "DefinedTerm"
},
{
"id": "http://scigraph.springernature.com/ontologies/product-market-codes/I21040",
"inDefinedTermSet": "http://scigraph.springernature.com/ontologies/product-market-codes/",
"name": "Natural Language Processing (NLP)",
"type": "DefinedTerm"
}
],
"contentRating": [
{
"author": "snip",
"ratingValue": "1.757",
"type": "Rating"
},
{
"author": "sjr",
"ratingValue": "0.695",
"type": "Rating"
},
{
"author": "impact_factor_wos",
"dateCreated": "2017",
"ratingValue": "1.855",
"type": "Rating"
},
{
"author": "impact_factor_wos",
"dateCreated": "2016",
"ratingValue": "1.848",
"type": "Rating"
},
{
"author": "impact_factor_wos",
"dateCreated": "2015",
"ratingValue": "1.719",
"type": "Rating"
},
{
"author": "impact_factor_wos",
"dateCreated": "2014",
"ratingValue": "1.889",
"type": "Rating"
},
{
"author": "impact_factor_wos",
"dateCreated": "2013",
"ratingValue": "1.689",
"type": "Rating"
},
{
"author": "impact_factor_wos",
"dateCreated": "2009",
"ratingValue": "1.663",
"type": "Rating"
},
{
"author": "impact_factor_wos",
"dateCreated": "2012",
"ratingValue": "1.454",
"type": "Rating"
},
{
"author": "impact_factor_wos",
"dateCreated": "2011",
"ratingValue": "1.587",
"type": "Rating"
},
{
"author": "impact_factor_wos",
"dateCreated": "2010",
"ratingValue": "1.967",
"type": "Rating"
},
{
"author": "impact_factor_wos",
"dateCreated": "2009",
"ratingValue": "1.663",
"type": "Rating"
},
{
"author": "impact_factor_wos",
"dateCreated": "2008",
"ratingValue": "2.326",
"type": "Rating"
},
{
"author": "impact_factor_wos",
"dateCreated": "2007",
"ratingValue": "1.742",
"type": "Rating"
}
],
"description": "Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems.
\nThe journal features papers that describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems or methods provide solid support via empirical studies, theoretical analysis, or comparison to psychological phenomena. Applications papers show how to apply learning methods to solve important applications problems. Research methodology papers improve how machine learning research is conducted.
\nAll papers describe the supporting evidence in ways that can be verified or replicated by other researchers. The papers also detail the learning component clearly and discuss assumptions regarding knowledge representation and the performance task.
",
"editor": [
{
"familyName": "Flach",
"givenName": "Peter A.",
"type": "Person"
}
],
"id": "sg:journal.1125588",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"issn": [
"0885-6125",
"1573-0565"
],
"license": "Hybrid (Open Choice)",
"name": "Machine Learning",
"productId": [
{
"name": "scopus_id",
"type": "PropertyValue",
"value": [
"24775"
]
},
{
"name": "wos_id",
"type": "PropertyValue",
"value": [
"0885-6125/MACHINE LEARNING"
]
},
{
"name": "nlm_unique_id",
"type": "PropertyValue",
"value": [
"9881780"
]
},
{
"name": "nsd_ids_id",
"type": "PropertyValue",
"value": [
"444497"
]
},
{
"name": "springer_id",
"type": "PropertyValue",
"value": [
"10994"
]
},
{
"name": "lccn_id",
"type": "PropertyValue",
"value": [
"86655869"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"125588"
]
},
{
"name": "era_ids_id",
"type": "PropertyValue",
"value": [
"18066"
]
}
],
"publisher": {
"name": "Springer US",
"type": "Organization"
},
"publisherImprint": "Springer",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_source_title=jour.1125588"
],
"sdDataset": "journals",
"sdDatePublished": "2019-03-18T11:05",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "file:///home/ubuntu/piotr/scigraph_export/journals_20190313_sn_only.jsonl",
"startYear": "1986",
"type": "Periodical",
"url": "http://link.springer.com/journal/10994"
}
]
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/journal.1125588'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/journal.1125588'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/journal.1125588'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/journal.1125588'
This table displays all metadata directly associated to this object as RDF triples.
172 TRIPLES
21 PREDICATES
46 URIs
38 LITERALS
25 BLANK NODES