forked from D-Net/dnet-hadoop
merge with beta - resolved conflict in pom
This commit is contained in:
commit
ffb0ce1d59
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@ -70,7 +70,7 @@ case object Crossref2Oaf {
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"reference-book" -> "0002 Book",
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"monograph" -> "0002 Book",
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"journal-article" -> "0001 Article",
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"dissertation" -> "0006 Doctoral thesis",
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"dissertation" -> "0044 Thesis",
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"other" -> "0038 Other literature type",
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"peer-review" -> "0015 Review",
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"proceedings" -> "0004 Conference object",
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@ -206,11 +206,16 @@ case object Crossref2Oaf {
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else {
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instance.setDateofacceptance(asField(createdDate.getValue))
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}
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val s: String = (json \ "URL").extract[String]
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val links: List[String] = ((for {JString(url) <- json \ "link" \ "URL"} yield url) ::: List(s)).filter(p => p != null).distinct
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if (links.nonEmpty) {
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instance.setUrl(links.asJava)
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}
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val s: List[String] = List("https://doi.org/" + doi)
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// val links: List[String] = ((for {JString(url) <- json \ "link" \ "URL"} yield url) ::: List(s)).filter(p => p != null && p.toLowerCase().contains(doi.toLowerCase())).distinct
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// if (links.nonEmpty) {
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// instance.setUrl(links.asJava)
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// }
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if(s.nonEmpty)
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{
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instance.setUrl(s.asJava)
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}
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result.setInstance(List(instance).asJava)
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//IMPORTANT
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@ -111,26 +111,9 @@ object SparkProcessMAG {
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.map(item => ConversionUtil.updatePubsWithConferenceInfo(item))
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.write
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.mode(SaveMode.Overwrite)
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.save(s"$workingPath/merge_step_2_conference")
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magPubs= spark.read.load(s"$workingPath/merge_step_2_conference").as[Publication]
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.map(p => (ConversionUtil.extractMagIdentifier(p.getOriginalId.asScala), p)).as[(String, Publication)]
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val paperUrlDataset = spark.read.load(s"$sourcePath/PaperUrls").as[MagPaperUrl].groupBy("PaperId").agg(collect_list(struct("sourceUrl")).as("instances")).as[MagUrl]
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logger.info("Phase 5) enrich publication with URL and Instances")
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magPubs.joinWith(paperUrlDataset, col("_1").equalTo(paperUrlDataset("PaperId")), "left")
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.map { a: ((String, Publication), MagUrl) => ConversionUtil.addInstances((a._1._2, a._2)) }
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.write.mode(SaveMode.Overwrite)
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.save(s"$workingPath/merge_step_3")
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// logger.info("Phase 6) Enrich Publication with description")
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// val pa = spark.read.load(s"${parser.get("sourcePath")}/PaperAbstractsInvertedIndex").as[MagPaperAbstract]
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// pa.map(ConversionUtil.transformPaperAbstract).write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/PaperAbstract")
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val paperAbstract = spark.read.load((s"$workingPath/PaperAbstract")).as[MagPaperAbstract]
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@ -162,12 +145,14 @@ object SparkProcessMAG {
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.write.mode(SaveMode.Overwrite)
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.save(s"$workingPath/mag_publication")
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spark.read.load(s"$workingPath/mag_publication").as[Publication]
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.filter(p => p.getId == null)
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.groupByKey(p => p.getId)
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.reduceGroups((a:Publication, b:Publication) => ConversionUtil.mergePublication(a,b))
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.map(_._2)
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.write.mode(SaveMode.Overwrite).save(s"$targetPath/magPublication")
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val s:RDD[Publication] = spark.read.load(s"$workingPath/mag_publication").as[Publication]
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.map(p=>Tuple2(p.getId, p)).rdd.reduceByKey((a:Publication, b:Publication) => ConversionUtil.mergePublication(a,b))
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.map(_._2)
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spark.createDataset(s).as[Publication].write.mode(SaveMode.Overwrite).save(s"$targetPath/magPublication")
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}
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}
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@ -612,4 +612,26 @@ class CrossrefMappingTest {
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}
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@Test
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def testMultipleURLs() :Unit = {
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val json = Source.fromInputStream(getClass.getResourceAsStream("multiple_urls.json")).mkString
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assertNotNull(json)
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assertFalse(json.isEmpty);
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val resultList: List[Oaf] = Crossref2Oaf.convert(json)
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assertTrue(resultList.nonEmpty)
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val item : Result = resultList.filter(p => p.isInstanceOf[Result]).head.asInstanceOf[Result]
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assertEquals(1, item.getInstance().size())
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assertEquals(1, item.getInstance().get(0).getUrl().size())
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assertEquals("https://doi.org/10.1016/j.jas.2019.105013", item.getInstance().get(0).getUrl().get(0))
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//println(mapper.writeValueAsString(item))
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}
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}
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@ -0,0 +1,614 @@
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{
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||||
"indexed": {
|
||||
"date-parts": [
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||||
[
|
||||
2021,
|
||||
10,
|
||||
31
|
||||
]
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||||
],
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"date-time": "2021-10-31T15:48:01Z",
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"timestamp": 1635695281393
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},
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"reference-count": 39,
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"publisher": "Elsevier BV",
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"license": [
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||||
{
|
||||
"start": {
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||||
"date-parts": [
|
||||
[
|
||||
2019,
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12,
|
||||
1
|
||||
]
|
||||
],
|
||||
"date-time": "2019-12-01T00:00:00Z",
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||||
"timestamp": 1575158400000
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},
|
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"content-version": "tdm",
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||||
"delay-in-days": 0,
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||||
"URL": "https://www.elsevier.com/tdm/userlicense/1.0/"
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},
|
||||
{
|
||||
"start": {
|
||||
"date-parts": [
|
||||
[
|
||||
2019,
|
||||
9,
|
||||
13
|
||||
]
|
||||
],
|
||||
"date-time": "2019-09-13T00:00:00Z",
|
||||
"timestamp": 1568332800000
|
||||
},
|
||||
"content-version": "vor",
|
||||
"delay-in-days": 0,
|
||||
"URL": "http://creativecommons.org/licenses/by/4.0/"
|
||||
}
|
||||
],
|
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"funder": [
|
||||
{
|
||||
"DOI": "10.13039/100001182",
|
||||
"name": "INSTAP",
|
||||
"doi-asserted-by": "publisher"
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},
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||||
{
|
||||
"DOI": "10.13039/100014440",
|
||||
"name": "Ministry of Science, Innovation and Universities",
|
||||
"doi-asserted-by": "publisher",
|
||||
"award": [
|
||||
"RYC-2016-19637"
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||||
]
|
||||
},
|
||||
{
|
||||
"DOI": "10.13039/100010661",
|
||||
"name": "European Union’s Horizon 2020",
|
||||
"doi-asserted-by": "publisher",
|
||||
"award": [
|
||||
"746446"
|
||||
]
|
||||
}
|
||||
],
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||||
"content-domain": {
|
||||
"domain": [
|
||||
"elsevier.com",
|
||||
"sciencedirect.com"
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||||
],
|
||||
"crossmark-restriction": true
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||||
},
|
||||
"short-container-title": [
|
||||
"Journal of Archaeological Science"
|
||||
],
|
||||
"published-print": {
|
||||
"date-parts": [
|
||||
[
|
||||
2019,
|
||||
12
|
||||
]
|
||||
]
|
||||
},
|
||||
"DOI": "10.1016/j.jas.2019.105013",
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||||
"type": "journal-article",
|
||||
"created": {
|
||||
"date-parts": [
|
||||
[
|
||||
2019,
|
||||
9,
|
||||
25
|
||||
]
|
||||
],
|
||||
"date-time": "2019-09-25T20:05:08Z",
|
||||
"timestamp": 1569441908000
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},
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"page": "105013",
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||||
"update-policy": "http://dx.doi.org/10.1016/elsevier_cm_policy",
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||||
"source": "Crossref",
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"is-referenced-by-count": 21,
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||||
"title": [
|
||||
"A brave new world for archaeological survey: Automated machine learning-based potsherd detection using high-resolution drone imagery"
|
||||
],
|
||||
"prefix": "10.1016",
|
||||
"volume": "112",
|
||||
"author": [
|
||||
{
|
||||
"given": "H.A.",
|
||||
"family": "Orengo",
|
||||
"sequence": "first",
|
||||
"affiliation": [
|
||||
|
||||
]
|
||||
},
|
||||
{
|
||||
"given": "A.",
|
||||
"family": "Garcia-Molsosa",
|
||||
"sequence": "additional",
|
||||
"affiliation": [
|
||||
|
||||
]
|
||||
}
|
||||
],
|
||||
"member": "78",
|
||||
"reference": [
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib1",
|
||||
"doi-asserted-by": "crossref",
|
||||
"first-page": "85",
|
||||
"DOI": "10.1080/17538947.2016.1250829",
|
||||
"article-title": "Remote sensing heritage in a petabyte-scale: satellite data and heritage Earth Engine© applications",
|
||||
"volume": "10",
|
||||
"author": "Agapiou",
|
||||
"year": "2017",
|
||||
"journal-title": "Int. J. Digit. Earth"
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib2",
|
||||
"series-title": "Extracting Meaning from Ploughsoil Assemblages",
|
||||
"first-page": "1",
|
||||
"article-title": "Extracting meaning from ploughsoil assemblages: assessments of the past, strategies for the future",
|
||||
"author": "Alcock",
|
||||
"year": "2000"
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib3",
|
||||
"series-title": "Side-by-Side Survey. Comparative Regional Studies in the Mediterranean World",
|
||||
"first-page": "1",
|
||||
"article-title": "Introduction",
|
||||
"author": "Alcock",
|
||||
"year": "2004"
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib4",
|
||||
"doi-asserted-by": "crossref",
|
||||
"first-page": "93",
|
||||
"DOI": "10.1111/j.1538-4632.1995.tb00338.x",
|
||||
"article-title": "Local indicators of spatial association—LISA",
|
||||
"volume": "27",
|
||||
"author": "Anselin",
|
||||
"year": "1995",
|
||||
"journal-title": "Geogr. Anal."
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib5",
|
||||
"series-title": "Archaeological Survey",
|
||||
"author": "Banning",
|
||||
"year": "2002"
|
||||
},
|
||||
{
|
||||
"issue": "1/2",
|
||||
"key": "10.1016/j.jas.2019.105013_bib6",
|
||||
"doi-asserted-by": "crossref",
|
||||
"first-page": "123",
|
||||
"DOI": "10.2307/3181488",
|
||||
"article-title": "GIS, archaeological survey and landscape archaeology on the island of Kythera, Greece",
|
||||
"volume": "29",
|
||||
"author": "Bevan",
|
||||
"year": "2004",
|
||||
"journal-title": "J. Field Archaeol."
|
||||
},
|
||||
{
|
||||
"issue": "1",
|
||||
"key": "10.1016/j.jas.2019.105013_bib8",
|
||||
"doi-asserted-by": "crossref",
|
||||
"first-page": "5",
|
||||
"DOI": "10.1023/A:1010933404324",
|
||||
"article-title": "Random forests",
|
||||
"volume": "45",
|
||||
"author": "Breiman",
|
||||
"year": "2001",
|
||||
"journal-title": "Mach. Learn."
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib9",
|
||||
"series-title": "Sampling in Contemporary British Archaeology",
|
||||
"author": "Cherry",
|
||||
"year": "1978"
|
||||
},
|
||||
{
|
||||
"issue": "3",
|
||||
"key": "10.1016/j.jas.2019.105013_bib10",
|
||||
"doi-asserted-by": "crossref",
|
||||
"first-page": "273",
|
||||
"DOI": "10.1016/0734-189X(84)90197-X",
|
||||
"article-title": "Segmentation of a high-resolution urban scene using texture operators",
|
||||
"volume": "25",
|
||||
"author": "Conners",
|
||||
"year": "1984",
|
||||
"journal-title": "Comput. Vis. Graph Image Process"
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib11",
|
||||
"first-page": "31",
|
||||
"article-title": "Old land surfaces and modern ploughsoil: implications of recent work at Maxey, Cambridgeshire",
|
||||
"volume": "2",
|
||||
"author": "Crowther",
|
||||
"year": "1983",
|
||||
"journal-title": "Scott. Archaeol. Rev."
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib12",
|
||||
"series-title": "Settlement Pattern Studies in the Americas: Fifty Years since Virú",
|
||||
"first-page": "203",
|
||||
"article-title": "Conclusions: the settlement pattern concept from an Americanist perspective",
|
||||
"author": "Fish",
|
||||
"year": "1999"
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib13",
|
||||
"doi-asserted-by": "crossref",
|
||||
"first-page": "21",
|
||||
"DOI": "10.3390/geosciences9010021",
|
||||
"article-title": "Remote sensing and historical morphodynamics of alluvial plains. The 1909 indus flood and the city of Dera Gazhi Khan (province of Punjab, Pakistan)",
|
||||
"volume": "9",
|
||||
"author": "Garcia",
|
||||
"year": "2019",
|
||||
"journal-title": "Geosciences"
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib14",
|
||||
"unstructured": "Georgiadis, M.; Garcia-Molsosa, A.; Orengo, H.A.; Kefalidou, E. and Kallintzi, K. In Preparation. APAX Project 2015-2018: A Preliminary Report. (Hesperia)."
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib15",
|
||||
"series-title": "Geographical Information Systems and Landscape Archaeology",
|
||||
"first-page": "35",
|
||||
"article-title": "Regional survey and GIS: the boeotia project",
|
||||
"author": "Gillings",
|
||||
"year": "1999"
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib16",
|
||||
"doi-asserted-by": "crossref",
|
||||
"first-page": "18",
|
||||
"DOI": "10.1016/j.rse.2017.06.031",
|
||||
"article-title": "Google Earth engine: planetary-scale geospatial analysis for everyone",
|
||||
"volume": "202",
|
||||
"author": "Gorelick",
|
||||
"year": "2017",
|
||||
"journal-title": "Remote Sens. Environ."
|
||||
},
|
||||
{
|
||||
"issue": "107",
|
||||
"key": "10.1016/j.jas.2019.105013_bib17",
|
||||
"doi-asserted-by": "crossref",
|
||||
"first-page": "177",
|
||||
"DOI": "10.1111/j.0031-868X.2004.00278.x",
|
||||
"article-title": "Photogrammetric reconstruction of the great buddha of Bamiyan, Afghanistan",
|
||||
"volume": "19",
|
||||
"author": "Grün",
|
||||
"year": "2004",
|
||||
"journal-title": "Photogramm. Rec."
|
||||
},
|
||||
{
|
||||
"issue": "6",
|
||||
"key": "10.1016/j.jas.2019.105013_bib18",
|
||||
"doi-asserted-by": "crossref",
|
||||
"first-page": "610",
|
||||
"DOI": "10.1109/TSMC.1973.4309314",
|
||||
"article-title": "Textural features for image classification",
|
||||
"author": "Haralick",
|
||||
"year": "1973",
|
||||
"journal-title": "IEEE Trans. Syst., Man, Cybernet., SMC-3"
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib19",
|
||||
"doi-asserted-by": "crossref",
|
||||
"first-page": "76",
|
||||
"DOI": "10.1558/jmea.v14i1.76",
|
||||
"article-title": "Excavating to excess? Implications of the last decade of archaeology in Israel",
|
||||
"volume": "14",
|
||||
"author": "Kletter",
|
||||
"year": "2001",
|
||||
"journal-title": "J. Mediterr. Archaeol."
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib20",
|
||||
"first-page": "299",
|
||||
"article-title": "Testing Google Earth Engine for the automatic identification and vectorization of archaeological features: a case study from Faynan, Jordan",
|
||||
"volume": "15",
|
||||
"author": "Liss",
|
||||
"year": "2017",
|
||||
"journal-title": "J. Archaeol. Sci.: Report"
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib21",
|
||||
"series-title": "Geographical Information Systems and Landscape Archaeology",
|
||||
"first-page": "55",
|
||||
"article-title": "Towards a methodology for modelling surface survey data: the sangro valley project",
|
||||
"author": "Lock",
|
||||
"year": "1999"
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib22",
|
||||
"series-title": "Extracting Meaning from Ploughsoil Assemblages",
|
||||
"first-page": "5",
|
||||
"article-title": "Methods of collection recording and quantification",
|
||||
"author": "Mattingly",
|
||||
"year": "2000"
|
||||
},
|
||||
{
|
||||
"issue": "14",
|
||||
"key": "10.1016/j.jas.2019.105013_bib23",
|
||||
"doi-asserted-by": "crossref",
|
||||
"first-page": "E778",
|
||||
"DOI": "10.1073/pnas.1115472109",
|
||||
"article-title": "Mapping patterns of long-term settlement in Northern Mesopotamia at a large scale",
|
||||
"volume": "109",
|
||||
"author": "Menze",
|
||||
"year": "2012",
|
||||
"journal-title": "Proc. Natl. Acad. Sci."
|
||||
},
|
||||
{
|
||||
"key": "10.1016/j.jas.2019.105013_bib24",
|
||||
"doi-asserted-by": "crossref",
|
||||
"first-page": "80",
|
||||
"DOI": "10.1016/j.jas.2015.04.002",
|
||||
"article-title": "A supervised machine-learning approach towards geochemical predictive modelling in archaeology",
|
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|
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"year": "2015",
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{
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"doi-asserted-by": "crossref",
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"article-title": "Combining terrestrial stereophotogrammetry, DGPS and GIS-based 3D voxel modelling in the volumetric recording of archaeological features",
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"volume": "76",
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{
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"doi-asserted-by": "crossref",
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"first-page": "100",
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"DOI": "10.1016/j.jas.2015.10.008",
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"article-title": "Photogrammetric re-discovery of the Eastern Thessalian hidden long-term landscapes",
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"doi-asserted-by": "crossref",
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"first-page": "479",
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"article-title": "Towards a definition of Minoan agro-pastoral landscapes: results of the survey at Palaikastro (Crete)",
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"volume": "122",
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},
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{
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|
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"doi-asserted-by": "crossref",
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"first-page": "735",
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"DOI": "10.3390/rs9070735",
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"article-title": "Large-scale, multi-temporal remote sensing of palaeo-river networks: a case study from Northwest India and its implications for the Indus civilisation",
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"volume": "9",
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||||
"author": "Orengo",
|
||||
"year": "2017",
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"journal-title": "Remote Sens."
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||||
},
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{
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"doi-asserted-by": "crossref",
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"first-page": "1361",
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"DOI": "10.1002/esp.4317",
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"article-title": "Multi-scale relief model (MSRM): a new algorithm for the visualization of subtle topographic change of variable size in digital elevation models",
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"volume": "43",
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"author": "Orengo",
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"year": "2018",
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"journal-title": "Earth Surf. Process. Landforms"
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},
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{
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"series-title": "Submitted to Proceedings of the National Academy of Sciences",
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"article-title": "Living on the edge of the desert: automated detection of archaeological mounds in Cholistan (Pakistan) using machine learning classification of multi-sensor multi-temporal satellite data",
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"author": "Orengo",
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"year": "2019"
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},
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{
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"first-page": "154",
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"volume": "vol. 7376",
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{
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"article-title": "Decision-making in modern surveys",
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"volume": "ume 1",
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{
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"doi-asserted-by": "crossref",
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"first-page": "100",
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"DOI": "10.3390/geosciences7040100",
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"article-title": "From above and on the ground: geospatial methods for recording endangered archaeology in the Middle East and north africa",
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"volume": "7",
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"author": "Rayne",
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"year": "2017",
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"journal-title": "Geosciences"
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},
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{
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"key": "10.1016/j.jas.2019.105013_bib34",
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"doi-asserted-by": "crossref",
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"first-page": "1",
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"DOI": "10.1080/00438243.1978.9979712",
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"article-title": "The design of archaeological surveys",
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"volume": "10",
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"author": "Schiffer",
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"year": "1978",
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"journal-title": "World Archaeol."
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||||
},
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{
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"key": "10.1016/j.jas.2019.105013_bib35",
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"series-title": "Experiments in the Collection and Analysis of Archaeological Survey Data: the East Hampshire Survey",
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{
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"doi-asserted-by": "crossref",
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"first-page": "1066",
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"DOI": "10.1016/j.culher.2016.06.006",
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"article-title": "Drones over Mediterranean landscapes. The potential of small UAV's (drones) for site detection and heritage management in archaeological survey projects: a case study from Le Pianelle in the Tappino Valley, Molise (Italy)",
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"volume": "22",
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"author": "Stek",
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"year": "2016",
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"journal-title": "J. Cult. Herit."
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},
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{
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"series-title": "Side-by-Side Survey. Comparative Regional Studies in the Mediterranean World",
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"first-page": "65",
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"article-title": "Side-by-side and back to front: exploring intra-regional latitudinal and longitudinal comparability in survey data. Three case studies from Metaponto, southern Italy",
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"author": "Thomson",
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{
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"series-title": "Digital Discovery. Exploring New Frontiers in Human Heritage. Computer Applications and Quantitative Methods in Archaeology",
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"article-title": "Computer vision and machine learning for archaeology",
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"author": "van der Maaten",
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"year": "2007"
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},
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{
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"doi-asserted-by": "crossref",
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"first-page": "1114",
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"DOI": "10.1111/j.1475-4754.2012.00667.x",
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"article-title": "Computer vision-based orthophoto mapping of complex archaeological sites: the ancient quarry of Pitaranha (Portugal-Spain)",
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"volume": "54",
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"author": "Verhoeven",
|
||||
"year": "2012",
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||||
"journal-title": "Archaeometry"
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||||
},
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{
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"key": "10.1016/j.jas.2019.105013_bib40",
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"series-title": "A Guide for Salvage Archeology",
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"author": "Wendorf",
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||||
"year": "1962"
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}
|
||||
],
|
||||
"container-title": [
|
||||
"Journal of Archaeological Science"
|
||||
],
|
||||
"original-title": [
|
||||
|
||||
],
|
||||
"language": "en",
|
||||
"link": [
|
||||
{
|
||||
"URL": "https://api.elsevier.com/content/article/PII:S0305440319301001?httpAccept=text/xml",
|
||||
"content-type": "text/xml",
|
||||
"content-version": "vor",
|
||||
"intended-application": "text-mining"
|
||||
},
|
||||
{
|
||||
"URL": "https://api.elsevier.com/content/article/PII:S0305440319301001?httpAccept=text/plain",
|
||||
"content-type": "text/plain",
|
||||
"content-version": "vor",
|
||||
"intended-application": "text-mining"
|
||||
}
|
||||
],
|
||||
"deposited": {
|
||||
"date-parts": [
|
||||
[
|
||||
2019,
|
||||
11,
|
||||
25
|
||||
]
|
||||
],
|
||||
"date-time": "2019-11-25T06:46:34Z",
|
||||
"timestamp": 1574664394000
|
||||
},
|
||||
"score": 1,
|
||||
"subtitle": [
|
||||
|
||||
],
|
||||
"short-title": [
|
||||
|
||||
],
|
||||
"issued": {
|
||||
"date-parts": [
|
||||
[
|
||||
2019,
|
||||
12
|
||||
]
|
||||
]
|
||||
},
|
||||
"references-count": 39,
|
||||
"alternative-id": [
|
||||
"S0305440319301001"
|
||||
],
|
||||
"URL": "http://dx.doi.org/10.1016/j.jas.2019.105013",
|
||||
"relation": {
|
||||
|
||||
},
|
||||
"ISSN": [
|
||||
"0305-4403"
|
||||
],
|
||||
"issn-type": [
|
||||
{
|
||||
"value": "0305-4403",
|
||||
"type": "print"
|
||||
}
|
||||
],
|
||||
"subject": [
|
||||
"Archaeology",
|
||||
"Archaeology"
|
||||
],
|
||||
"published": {
|
||||
"date-parts": [
|
||||
[
|
||||
2019,
|
||||
12
|
||||
]
|
||||
]
|
||||
},
|
||||
"assertion": [
|
||||
{
|
||||
"value": "Elsevier",
|
||||
"name": "publisher",
|
||||
"label": "This article is maintained by"
|
||||
},
|
||||
{
|
||||
"value": "A brave new world for archaeological survey: Automated machine learning-based potsherd detection using high-resolution drone imagery",
|
||||
"name": "articletitle",
|
||||
"label": "Article Title"
|
||||
},
|
||||
{
|
||||
"value": "Journal of Archaeological Science",
|
||||
"name": "journaltitle",
|
||||
"label": "Journal Title"
|
||||
},
|
||||
{
|
||||
"value": "https://doi.org/10.1016/j.jas.2019.105013",
|
||||
"name": "articlelink",
|
||||
"label": "CrossRef DOI link to publisher maintained version"
|
||||
},
|
||||
{
|
||||
"value": "article",
|
||||
"name": "content_type",
|
||||
"label": "Content Type"
|
||||
},
|
||||
{
|
||||
"value": "© 2019 The Authors. Published by Elsevier Ltd.",
|
||||
"name": "copyright",
|
||||
"label": "Copyright"
|
||||
}
|
||||
],
|
||||
"article-number": "105013"
|
||||
}
|
||||
|
|
@ -0,0 +1,107 @@
|
|||
package eu.dnetlib.dhp.oa.graph.resolution
|
||||
|
||||
import com.fasterxml.jackson.databind.ObjectMapper
|
||||
import eu.dnetlib.dhp.application.ArgumentApplicationParser
|
||||
import eu.dnetlib.dhp.common.HdfsSupport
|
||||
import eu.dnetlib.dhp.schema.common.EntityType
|
||||
import eu.dnetlib.dhp.schema.oaf.{OtherResearchProduct, Publication, Result, Software, Dataset => OafDataset}
|
||||
import org.apache.commons.io.IOUtils
|
||||
import org.apache.hadoop.fs.{FileSystem, Path}
|
||||
import org.apache.spark.SparkConf
|
||||
import org.apache.spark.sql._
|
||||
import org.slf4j.{Logger, LoggerFactory}
|
||||
|
||||
object SparkResolveEntities {
|
||||
|
||||
val mapper = new ObjectMapper()
|
||||
val entities = List(EntityType.dataset,EntityType.publication, EntityType.software, EntityType.otherresearchproduct)
|
||||
|
||||
def main(args: Array[String]): Unit = {
|
||||
val log: Logger = LoggerFactory.getLogger(getClass)
|
||||
val conf: SparkConf = new SparkConf()
|
||||
val parser = new ArgumentApplicationParser(IOUtils.toString(getClass.getResourceAsStream("/eu/dnetlib/dhp/oa/graph/resolution/resolve_params.json")))
|
||||
parser.parseArgument(args)
|
||||
val spark: SparkSession =
|
||||
SparkSession
|
||||
.builder()
|
||||
.config(conf)
|
||||
.appName(getClass.getSimpleName)
|
||||
.master(parser.get("master")).getOrCreate()
|
||||
|
||||
|
||||
val graphBasePath = parser.get("graphBasePath")
|
||||
log.info(s"graphBasePath -> $graphBasePath")
|
||||
val workingPath = parser.get("workingPath")
|
||||
log.info(s"workingPath -> $workingPath")
|
||||
val unresolvedPath = parser.get("unresolvedPath")
|
||||
log.info(s"unresolvedPath -> $unresolvedPath")
|
||||
|
||||
val fs = FileSystem.get(spark.sparkContext.hadoopConfiguration)
|
||||
fs.mkdirs(new Path(workingPath))
|
||||
|
||||
resolveEntities(spark, workingPath, unresolvedPath)
|
||||
generateResolvedEntities(spark, workingPath, graphBasePath)
|
||||
|
||||
// TO BE conservative we keep the original entities in the working dir
|
||||
// and save the resolved entities on the graphBasePath
|
||||
//In future these lines of code should be removed
|
||||
entities.foreach {
|
||||
e =>
|
||||
fs.rename(new Path(s"$graphBasePath/$e"), new Path(s"$workingPath/${e}_old"))
|
||||
fs.rename(new Path(s"$workingPath/resolvedGraph/$e"), new Path(s"$graphBasePath/$e"))
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
def resolveEntities(spark: SparkSession, workingPath: String, unresolvedPath: String) = {
|
||||
implicit val resEncoder: Encoder[Result] = Encoders.kryo(classOf[Result])
|
||||
import spark.implicits._
|
||||
|
||||
val rPid: Dataset[(String, String)] = spark.read.load(s"$workingPath/relationResolvedPid").as[(String, String)]
|
||||
val up: Dataset[(String, Result)] = spark.read.text(unresolvedPath).as[String].map(s => mapper.readValue(s, classOf[Result])).map(r => (r.getId, r))(Encoders.tuple(Encoders.STRING, resEncoder))
|
||||
|
||||
rPid.joinWith(up, rPid("_2").equalTo(up("_1")), "inner").map {
|
||||
r =>
|
||||
val result = r._2._2
|
||||
val dnetId = r._1._1
|
||||
result.setId(dnetId)
|
||||
result
|
||||
}.write.mode(SaveMode.Overwrite).save(s"$workingPath/resolvedEntities")
|
||||
}
|
||||
|
||||
|
||||
def deserializeObject(input:String, entity:EntityType ) :Result = {
|
||||
|
||||
entity match {
|
||||
case EntityType.publication => mapper.readValue(input, classOf[Publication])
|
||||
case EntityType.dataset => mapper.readValue(input, classOf[OafDataset])
|
||||
case EntityType.software=> mapper.readValue(input, classOf[Software])
|
||||
case EntityType.otherresearchproduct=> mapper.readValue(input, classOf[OtherResearchProduct])
|
||||
}
|
||||
}
|
||||
|
||||
def generateResolvedEntities(spark:SparkSession, workingPath: String, graphBasePath:String) = {
|
||||
|
||||
implicit val resEncoder: Encoder[Result] = Encoders.kryo(classOf[Result])
|
||||
import spark.implicits._
|
||||
|
||||
val re:Dataset[Result] = spark.read.load(s"$workingPath/resolvedEntities").as[Result]
|
||||
entities.foreach {
|
||||
e =>
|
||||
|
||||
spark.read.text(s"$graphBasePath/$e").as[String]
|
||||
.map(s => deserializeObject(s, e))
|
||||
.union(re)
|
||||
.groupByKey(_.getId)
|
||||
.reduceGroups {
|
||||
(x, y) =>
|
||||
x.mergeFrom(y)
|
||||
x
|
||||
}.map(_._2)
|
||||
.filter(r => r.getClass.getSimpleName.toLowerCase != "result")
|
||||
.map(r => mapper.writeValueAsString(r))(Encoders.STRING)
|
||||
.write.mode(SaveMode.Overwrite).option("compression", "gzip").text(s"$workingPath/resolvedGraph/$e")
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -96,6 +96,21 @@ object SparkResolveRelation {
|
|||
.text(s"$graphBasePath/relation")
|
||||
}
|
||||
|
||||
def extractInstanceCF(input: String): List[(String, String)] = {
|
||||
implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats
|
||||
lazy val json: json4s.JValue = parse(input)
|
||||
val result: List[(String, String)] = for {
|
||||
JObject(iObj) <- json \ "instance"
|
||||
JField("collectedfrom", JObject(cf)) <- iObj
|
||||
JField("instancetype", JObject(instancetype)) <- iObj
|
||||
JField("value", JString(collectedFrom)) <- cf
|
||||
JField("classname", JString(classname)) <- instancetype
|
||||
} yield (classname, collectedFrom)
|
||||
|
||||
result
|
||||
|
||||
}
|
||||
|
||||
|
||||
def extractPidsFromRecord(input: String): (String, List[(String, String)]) = {
|
||||
implicit lazy val formats: DefaultFormats.type = org.json4s.DefaultFormats
|
||||
|
|
@ -108,14 +123,7 @@ object SparkResolveRelation {
|
|||
JField("classid", JString(pidType)) <- qualifier
|
||||
} yield (pidValue, pidType)
|
||||
|
||||
val alternateIds: List[(String, String)] = for {
|
||||
JObject(pids) <- json \\ "alternateIdentifier"
|
||||
JField("value", JString(pidValue)) <- pids
|
||||
JField("qualifier", JObject(qualifier)) <- pids
|
||||
JField("classid", JString(pidType)) <- qualifier
|
||||
} yield (pidValue, pidType)
|
||||
|
||||
(id, result ::: alternateIds)
|
||||
(id, result)
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -128,7 +136,7 @@ object SparkResolveRelation {
|
|||
source != null
|
||||
}
|
||||
|
||||
private def extractPidResolvedTableFromJsonRDD(spark: SparkSession, graphPath: String, workingPath: String) = {
|
||||
def extractPidResolvedTableFromJsonRDD(spark: SparkSession, graphPath: String, workingPath: String) = {
|
||||
import spark.implicits._
|
||||
|
||||
val d: RDD[(String, String)] = spark.sparkContext.textFile(s"$graphPath/*")
|
||||
|
|
|
|||
|
|
@ -59,7 +59,12 @@ object SparkConvertRDDtoDataset {
|
|||
log.info("Converting Relation")
|
||||
|
||||
|
||||
val rddRelation =spark.sparkContext.textFile(s"$sourcePath/relation").map(s => mapper.readValue(s, classOf[Relation])).filter(r=> r.getSource.startsWith("50") && r.getTarget.startsWith("50"))
|
||||
val relationSemanticFilter = List("cites", "iscitedby","merges", "ismergedin")
|
||||
|
||||
val rddRelation =spark.sparkContext.textFile(s"$sourcePath/relation")
|
||||
.map(s => mapper.readValue(s, classOf[Relation]))
|
||||
.filter(r=> r.getSource.startsWith("50") && r.getTarget.startsWith("50"))
|
||||
.filter(r => !relationSemanticFilter.exists(k => k.equalsIgnoreCase(r.getRelClass)))
|
||||
spark.createDataset(rddRelation).as[Relation].write.mode(SaveMode.Overwrite).save(s"$relPath")
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -4,6 +4,10 @@
|
|||
<name>graphBasePath</name>
|
||||
<description>the path of the graph</description>
|
||||
</property>
|
||||
<property>
|
||||
<name>unresolvedPath</name>
|
||||
<description>the path of the unresolved Entities</description>
|
||||
</property>
|
||||
</parameters>
|
||||
|
||||
<start to="ResolveRelations"/>
|
||||
|
|
@ -36,5 +40,33 @@
|
|||
<ok to="End"/>
|
||||
<error to="Kill"/>
|
||||
</action>
|
||||
|
||||
<action name="ResolveEntities">
|
||||
<spark xmlns="uri:oozie:spark-action:0.2">
|
||||
<master>yarn</master>
|
||||
<mode>cluster</mode>
|
||||
<name>Resolve Relations in raw graph</name>
|
||||
<class>eu.dnetlib.dhp.oa.graph.resolution.SparkResolveEntities</class>
|
||||
<jar>dhp-graph-mapper-${projectVersion}.jar</jar>
|
||||
<spark-opts>
|
||||
--executor-memory=${sparkExecutorMemory}
|
||||
--executor-cores=${sparkExecutorCores}
|
||||
--driver-memory=${sparkDriverMemory}
|
||||
--conf spark.extraListeners=${spark2ExtraListeners}
|
||||
--conf spark.sql.shuffle.partitions=10000
|
||||
--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
|
||||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
||||
</spark-opts>
|
||||
<arg>--master</arg><arg>yarn</arg>
|
||||
<arg>--graphBasePath</arg><arg>${graphBasePath}</arg>
|
||||
<arg>--unresolvedPath</arg><arg>${unresolvedPath}</arg>
|
||||
<arg>--workingPath</arg><arg>${workingDir}</arg>
|
||||
</spark>
|
||||
<ok to="End"/>
|
||||
<error to="Kill"/>
|
||||
</action>
|
||||
|
||||
|
||||
<end name="End"/>
|
||||
</workflow-app>
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
[
|
||||
{"paramName":"mt", "paramLongName":"master", "paramDescription": "should be local or yarn", "paramRequired": true},
|
||||
{"paramName":"w", "paramLongName":"workingPath", "paramDescription": "the source Path", "paramRequired": true},
|
||||
{"paramName":"u", "paramLongName":"unresolvedPath", "paramDescription": "the source Path", "paramRequired": true},
|
||||
{"paramName":"g", "paramLongName":"graphBasePath", "paramDescription": "the path of the raw graph", "paramRequired": true}
|
||||
]
|
||||
|
|
@ -0,0 +1,190 @@
|
|||
package eu.dnetlib.dhp.oa.graph.resolution
|
||||
|
||||
|
||||
import com.fasterxml.jackson.databind.ObjectMapper
|
||||
import eu.dnetlib.dhp.schema.common.EntityType
|
||||
import eu.dnetlib.dhp.schema.oaf.utils.OafMapperUtils
|
||||
import eu.dnetlib.dhp.schema.oaf.{Result, StructuredProperty}
|
||||
import org.apache.commons.io.FileUtils
|
||||
import org.apache.spark.SparkConf
|
||||
import org.apache.spark.sql._
|
||||
import org.junit.jupiter.api.Assertions._
|
||||
import org.junit.jupiter.api.TestInstance.Lifecycle
|
||||
import org.junit.jupiter.api.{AfterAll, BeforeAll, Test, TestInstance}
|
||||
|
||||
import java.nio.file.{Files, Path}
|
||||
import scala.collection.JavaConverters._
|
||||
import scala.io.Source
|
||||
|
||||
@TestInstance(Lifecycle.PER_CLASS)
|
||||
class ResolveEntitiesTest extends Serializable {
|
||||
|
||||
var workingDir:Path = null
|
||||
|
||||
val FAKE_TITLE = "FAKETITLE"
|
||||
val FAKE_SUBJECT = "FAKESUBJECT"
|
||||
|
||||
var sparkSession:Option[SparkSession] = None
|
||||
|
||||
|
||||
@BeforeAll
|
||||
def setUp() :Unit = {
|
||||
workingDir = Files.createTempDirectory(getClass.getSimpleName)
|
||||
|
||||
val conf = new SparkConf()
|
||||
sparkSession = Some(SparkSession
|
||||
.builder()
|
||||
.config(conf)
|
||||
.appName(getClass.getSimpleName)
|
||||
.master("local[*]").getOrCreate())
|
||||
populateDatasets(sparkSession.get)
|
||||
generateUpdates(sparkSession.get)
|
||||
|
||||
}
|
||||
|
||||
|
||||
@AfterAll
|
||||
def tearDown():Unit = {
|
||||
FileUtils.deleteDirectory(workingDir.toFile)
|
||||
sparkSession.get.stop()
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
def generateUpdates(spark:SparkSession):Unit = {
|
||||
val template = Source.fromInputStream(this.getClass.getResourceAsStream("updates")).mkString
|
||||
|
||||
|
||||
val pids:List[String] = template.lines.map{id =>
|
||||
val r = new Result
|
||||
r.setId(id.toLowerCase.trim)
|
||||
r.setSubject(List(OafMapperUtils.structuredProperty(FAKE_SUBJECT, OafMapperUtils.qualifier("fos","fosCS", "fossSchema", "fossiFIgo"), null)).asJava)
|
||||
r.setTitle(List(OafMapperUtils.structuredProperty(FAKE_TITLE, OafMapperUtils.qualifier("fos","fosCS", "fossSchema", "fossiFIgo"), null)).asJava)
|
||||
r
|
||||
}.map{r =>
|
||||
val mapper = new ObjectMapper()
|
||||
|
||||
mapper.writeValueAsString(r)}.toList
|
||||
|
||||
|
||||
val sc =spark.sparkContext
|
||||
|
||||
println(sc.parallelize(pids).count())
|
||||
|
||||
spark.createDataset(sc.parallelize(pids))(Encoders.STRING).write.mode(SaveMode.Overwrite).option("compression", "gzip").text(s"$workingDir/updates")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
import spark.implicits._
|
||||
implicit val resEncoder: Encoder[Result] = Encoders.bean(classOf[Result])
|
||||
val ds = spark.read.text(s"$workingDir/updates").as[String].map{s => val mapper = new ObjectMapper()
|
||||
mapper.readValue(s, classOf[Result])}.collect()
|
||||
|
||||
|
||||
|
||||
|
||||
assertEquals(4, ds.length)
|
||||
ds.foreach{r => assertNotNull(r.getSubject)}
|
||||
ds.foreach{r => assertEquals(1,r.getSubject.size())}
|
||||
ds.foreach{r => assertNotNull(r.getTitle)}
|
||||
ds.foreach{r => assertEquals(1,r.getTitle.size())}
|
||||
|
||||
|
||||
|
||||
ds.flatMap(r => r.getTitle.asScala.map(t => t.getValue)).foreach(t => assertEquals(FAKE_TITLE,t))
|
||||
ds.flatMap(r => r.getSubject.asScala.map(t => t.getValue)).foreach(t => assertEquals(FAKE_SUBJECT,t))
|
||||
|
||||
println("generated Updates")
|
||||
}
|
||||
|
||||
|
||||
def populateDatasets(spark:SparkSession):Unit = {
|
||||
import spark.implicits._
|
||||
val entities =SparkResolveEntities.entities
|
||||
|
||||
entities.foreach{
|
||||
e =>
|
||||
val template = Source.fromInputStream(this.getClass.getResourceAsStream(s"$e")).mkString
|
||||
spark.createDataset(spark.sparkContext.parallelize(template.lines.toList)).as[String].write.option("compression", "gzip").text(s"$workingDir/graph/$e")
|
||||
println(s"Created Dataset $e")
|
||||
}
|
||||
SparkResolveRelation.extractPidResolvedTableFromJsonRDD(spark, s"$workingDir/graph", s"$workingDir/work")
|
||||
|
||||
}
|
||||
|
||||
|
||||
@Test
|
||||
def testResolution():Unit = {
|
||||
val spark:SparkSession = sparkSession.get
|
||||
implicit val resEncoder: Encoder[Result] = Encoders.kryo(classOf[Result])
|
||||
SparkResolveEntities.resolveEntities(spark,s"$workingDir/work", s"$workingDir/updates" )
|
||||
|
||||
val ds = spark.read.load(s"$workingDir/work/resolvedEntities").as[Result]
|
||||
|
||||
assertEquals(3, ds.count())
|
||||
|
||||
ds.collect().foreach{
|
||||
r =>
|
||||
assertTrue(r.getId.startsWith("50"))
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
private def structuredPContainsValue(l:java.util.List[StructuredProperty], exptectedValue:String):Boolean = {
|
||||
l.asScala.exists(p =>p.getValue!= null && p.getValue.equalsIgnoreCase(exptectedValue))
|
||||
}
|
||||
|
||||
@Test
|
||||
def testUpdate():Unit = {
|
||||
val spark:SparkSession = sparkSession.get
|
||||
import spark.implicits._
|
||||
implicit val resEncoder: Encoder[Result] = Encoders.kryo(classOf[Result])
|
||||
val m = new ObjectMapper()
|
||||
SparkResolveEntities.resolveEntities(spark,s"$workingDir/work", s"$workingDir/updates" )
|
||||
SparkResolveEntities.generateResolvedEntities(spark,s"$workingDir/work",s"$workingDir/graph" )
|
||||
|
||||
|
||||
|
||||
|
||||
val pubDS:Dataset[Result] = spark.read.text(s"$workingDir/work/resolvedGraph/publication").as[String].map(s => SparkResolveEntities.deserializeObject(s, EntityType.publication))
|
||||
val t = pubDS.filter(p => p.getTitle!=null && p.getSubject!=null).filter(p => p.getTitle.asScala.exists(t => t.getValue.equalsIgnoreCase("FAKETITLE"))).count()
|
||||
|
||||
|
||||
|
||||
val datDS:Dataset[Result] = spark.read.text(s"$workingDir/work/resolvedGraph/dataset").as[String].map(s => SparkResolveEntities.deserializeObject(s, EntityType.dataset))
|
||||
val td = datDS.filter(p => p.getTitle!=null && p.getSubject!=null).filter(p => p.getTitle.asScala.exists(t => t.getValue.equalsIgnoreCase("FAKETITLE"))).count()
|
||||
|
||||
|
||||
val softDS:Dataset[Result] = spark.read.text(s"$workingDir/work/resolvedGraph/software").as[String].map(s => SparkResolveEntities.deserializeObject(s, EntityType.software))
|
||||
val ts = softDS.filter(p => p.getTitle!=null && p.getSubject!=null).filter(p => p.getTitle.asScala.exists(t => t.getValue.equalsIgnoreCase("FAKETITLE"))).count()
|
||||
|
||||
|
||||
val orpDS:Dataset[Result] = spark.read.text(s"$workingDir/work/resolvedGraph/otherresearchproduct").as[String].map(s => SparkResolveEntities.deserializeObject(s, EntityType.otherresearchproduct))
|
||||
val to = orpDS.filter(p => p.getTitle!=null && p.getSubject!=null).filter(p => p.getTitle.asScala.exists(t => t.getValue.equalsIgnoreCase("FAKETITLE"))).count()
|
||||
|
||||
|
||||
assertEquals(0, t)
|
||||
assertEquals(2, td)
|
||||
assertEquals(1, ts)
|
||||
assertEquals(0, to)
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,4 @@
|
|||
unresolved::10.17026/dans-x3z-fsq5::doi
|
||||
unresolved::10.17026/dans-xsw-qtnx::doi
|
||||
unresolved::10.5281/zenodo.1473694::doi
|
||||
unresolved::10.17632/fake::doi
|
||||
2
pom.xml
2
pom.xml
|
|
@ -753,7 +753,7 @@
|
|||
<mockito-core.version>3.3.3</mockito-core.version>
|
||||
<mongodb.driver.version>3.4.2</mongodb.driver.version>
|
||||
<vtd.version>[2.12,3.0)</vtd.version>
|
||||
<dhp-schemas.version>[2.8.22-SNAPSHOT]</dhp-schemas.version>
|
||||
<dhp-schemas.version>[2.8.22]</dhp-schemas.version>
|
||||
<dnet-actionmanager-api.version>[4.0.3]</dnet-actionmanager-api.version>
|
||||
<dnet-actionmanager-common.version>[6.0.5]</dnet-actionmanager-common.version>
|
||||
<dnet-openaire-broker-common.version>[3.1.6]</dnet-openaire-broker-common.version>
|
||||
|
|
|
|||
Loading…
Reference in New Issue