forked from D-Net/dnet-hadoop
fixed Crossref Mapping
This commit is contained in:
Sandro La Bruzzo
parent
b771d67e9d
commit
5818abaab4
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@ -137,6 +137,9 @@ case object Crossref2Oaf {
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if (StringUtils.isNotBlank(issuedDate)) {
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result.setDateofacceptance(asField(issuedDate))
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}
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else {
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result.setDateofacceptance(asField(createdDate.getValue))
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}
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result.setRelevantdate(List(createdDate, postedDate, acceptedDate, publishedOnlineDate, publishedPrintDate).filter(p => p != null).asJava)
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@ -233,6 +236,16 @@ case object Crossref2Oaf {
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val queue = new mutable.Queue[Relation]
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def snfRule(award:String): String = {
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var tmp1 = StringUtils.substringAfter(award,"_")
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val tmp2 = StringUtils.substringBefore(tmp1,"/")
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logger.debug(s"From $award to $tmp2")
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tmp2
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}
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def extractECAward(award: String): String = {
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val awardECRegex: Regex = "[0-9]{4,9}".r
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if (awardECRegex.findAllIn(award).hasNext)
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@ -298,7 +311,7 @@ case object Crossref2Oaf {
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case "10.13039/501100006588" |
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"10.13039/501100004488" => generateSimpleRelationFromAward(funder, "irb_hr______", a=>a.replaceAll("Project No.", "").replaceAll("HRZZ-","") )
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case "10.13039/501100006769"=> generateSimpleRelationFromAward(funder, "rsf_________", a=>a)
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case "10.13039/501100001711"=> generateSimpleRelationFromAward(funder, "snsf________", extractECAward)
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case "10.13039/501100001711"=> generateSimpleRelationFromAward(funder, "snsf________", snfRule)
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case "10.13039/501100004410"=> generateSimpleRelationFromAward(funder, "tubitakf____", a =>a)
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case "10.10.13039/100004440"=> generateSimpleRelationFromAward(funder, "wt__________", a =>a)
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case "10.13039/100004440"=> queue += generateRelation(sourceId,"1e5e62235d094afd01cd56e65112fc63", "wt__________" )
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@ -31,100 +31,99 @@ object SparkPreProcessMAG {
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implicit val mapEncoderPubs: Encoder[Publication] = org.apache.spark.sql.Encoders.kryo[Publication]
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implicit val tupleForJoinEncoder: Encoder[(String, Publication)] = Encoders.tuple(Encoders.STRING, mapEncoderPubs)
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// logger.info("Phase 1) make uninque DOI in Papers:")
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// val d: Dataset[MagPapers] = spark.read.load(s"${parser.get("sourcePath")}/Papers").as[MagPapers]
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//
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//
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// // Filtering Papers with DOI, and since for the same DOI we have multiple version of item with different PapersId we get the last one
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// val result: RDD[MagPapers] = d.where(col("Doi").isNotNull).rdd.map { p: MagPapers => Tuple2(p.Doi, p) }.reduceByKey { case (p1: MagPapers, p2: MagPapers) =>
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// var r = if (p1 == null) p2 else p1
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// if (p1 != null && p2 != null) {
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// if (p1.CreatedDate != null && p2.CreatedDate != null) {
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// if (p1.CreatedDate.before(p2.CreatedDate))
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// r = p1
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// else
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// r = p2
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// } else {
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// r = if (p1.CreatedDate == null) p2 else p1
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// }
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// }
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// r
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// }.map(_._2)
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//
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// val distinctPaper: Dataset[MagPapers] = spark.createDataset(result)
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// distinctPaper.write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/Papers_distinct")
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// logger.info(s"Total number of element: ${result.count()}")
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//
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// logger.info("Phase 3) Group Author by PaperId")
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// val authors = spark.read.load(s"$sourcePath/Authors").as[MagAuthor]
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//
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// val affiliation = spark.read.load(s"$sourcePath/Affiliations").as[MagAffiliation]
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//
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// val paperAuthorAffiliation = spark.read.load(s"$sourcePath/PaperAuthorAffiliations").as[MagPaperAuthorAffiliation]
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//
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//
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// paperAuthorAffiliation.joinWith(authors, paperAuthorAffiliation("AuthorId").equalTo(authors("AuthorId")))
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// .map { case (a: MagPaperAuthorAffiliation, b: MagAuthor) => (a.AffiliationId, MagPaperAuthorDenormalized(a.PaperId, b, null)) }
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// .joinWith(affiliation, affiliation("AffiliationId").equalTo(col("_1")), "left")
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// .map(s => {
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// val mpa = s._1._2
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// val af = s._2
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// if (af != null) {
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// MagPaperAuthorDenormalized(mpa.PaperId, mpa.author, af.DisplayName)
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// } else
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// mpa
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// }).groupBy("PaperId").agg(collect_list(struct($"author", $"affiliation")).as("authors"))
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// .write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/merge_step_1_paper_authors")
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//
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// logger.info("Phase 4) create First Version of publication Entity with Paper Journal and Authors")
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//
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// val journals = spark.read.load(s"$sourcePath/Journals").as[MagJournal]
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//
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// val papers = spark.read.load((s"${parser.get("targetPath")}/Papers_distinct")).as[MagPapers]
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//
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// val paperWithAuthors = spark.read.load(s"${parser.get("targetPath")}/merge_step_1_paper_authors").as[MagPaperWithAuthorList]
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//
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// val firstJoin = papers.joinWith(journals, papers("JournalId").equalTo(journals("JournalId")), "left")
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// firstJoin.joinWith(paperWithAuthors, firstJoin("_1.PaperId").equalTo(paperWithAuthors("PaperId")), "left")
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// .map { a: ((MagPapers, MagJournal), MagPaperWithAuthorList) => ConversionUtil.createOAFFromJournalAuthorPaper(a) }.write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/merge_step_2")
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//
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//
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// var magPubs: Dataset[(String, Publication)] = spark.read.load(s"${parser.get("targetPath")}/merge_step_2").as[Publication].map(p => (ConversionUtil.extractMagIdentifier(p.getOriginalId.asScala), p)).as[(String, Publication)]
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//
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// val paperUrlDataset = spark.read.load(s"$sourcePath/PaperUrls").as[MagPaperUrl].groupBy("PaperId").agg(collect_list(struct("sourceUrl")).as("instances")).as[MagUrl]
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//
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//
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// logger.info("Phase 5) enrich publication with URL and Instances")
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//
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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"${parser.get("targetPath")}/merge_step_3")
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//
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//
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// logger.info("Phase 6) Enrich Publication with description")
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// val pa = spark.read.load(s"${parser.get("sourcePath")}/PaperAbstractsInvertedIndex").as[MagPaperAbstract]
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// pa.map(ConversionUtil.transformPaperAbstract).write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/PaperAbstract")
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//
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// val paperAbstract = spark.read.load((s"${parser.get("targetPath")}/PaperAbstract")).as[MagPaperAbstract]
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//
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//
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// magPubs = spark.read.load(s"${parser.get("targetPath")}/merge_step_3").as[Publication].map(p => (ConversionUtil.extractMagIdentifier(p.getOriginalId.asScala), p)).as[(String, Publication)]
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//
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// magPubs.joinWith(paperAbstract, col("_1").equalTo(paperAbstract("PaperId")), "left").map(p => {
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// val pub = p._1._2
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// val abst = p._2
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// if (abst != null) {
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// pub.setDescription(List(asField(abst.IndexedAbstract)).asJava)
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// }
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// pub
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// }
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// ).write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/merge_step_4")
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//
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logger.info("Phase 1) make uninque DOI in Papers:")
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val d: Dataset[MagPapers] = spark.read.load(s"${parser.get("sourcePath")}/Papers").as[MagPapers]
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// Filtering Papers with DOI, and since for the same DOI we have multiple version of item with different PapersId we get the last one
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val result: RDD[MagPapers] = d.where(col("Doi").isNotNull).rdd.map { p: MagPapers => Tuple2(p.Doi, p) }.reduceByKey { case (p1: MagPapers, p2: MagPapers) =>
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var r = if (p1 == null) p2 else p1
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if (p1 != null && p2 != null) {
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if (p1.CreatedDate != null && p2.CreatedDate != null) {
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if (p1.CreatedDate.before(p2.CreatedDate))
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r = p1
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else
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r = p2
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} else {
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r = if (p1.CreatedDate == null) p2 else p1
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}
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}
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r
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}.map(_._2)
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val distinctPaper: Dataset[MagPapers] = spark.createDataset(result)
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distinctPaper.write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/Papers_distinct")
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logger.info(s"Total number of element: ${result.count()}")
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logger.info("Phase 3) Group Author by PaperId")
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val authors = spark.read.load(s"$sourcePath/Authors").as[MagAuthor]
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val affiliation = spark.read.load(s"$sourcePath/Affiliations").as[MagAffiliation]
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val paperAuthorAffiliation = spark.read.load(s"$sourcePath/PaperAuthorAffiliations").as[MagPaperAuthorAffiliation]
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paperAuthorAffiliation.joinWith(authors, paperAuthorAffiliation("AuthorId").equalTo(authors("AuthorId")))
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.map { case (a: MagPaperAuthorAffiliation, b: MagAuthor) => (a.AffiliationId, MagPaperAuthorDenormalized(a.PaperId, b, null)) }
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.joinWith(affiliation, affiliation("AffiliationId").equalTo(col("_1")), "left")
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.map(s => {
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val mpa = s._1._2
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val af = s._2
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if (af != null) {
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MagPaperAuthorDenormalized(mpa.PaperId, mpa.author, af.DisplayName)
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} else
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mpa
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}).groupBy("PaperId").agg(collect_list(struct($"author", $"affiliation")).as("authors"))
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.write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/merge_step_1_paper_authors")
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logger.info("Phase 4) create First Version of publication Entity with Paper Journal and Authors")
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val journals = spark.read.load(s"$sourcePath/Journals").as[MagJournal]
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val papers = spark.read.load((s"${parser.get("targetPath")}/Papers_distinct")).as[MagPapers]
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val paperWithAuthors = spark.read.load(s"${parser.get("targetPath")}/merge_step_1_paper_authors").as[MagPaperWithAuthorList]
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val firstJoin = papers.joinWith(journals, papers("JournalId").equalTo(journals("JournalId")), "left")
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firstJoin.joinWith(paperWithAuthors, firstJoin("_1.PaperId").equalTo(paperWithAuthors("PaperId")), "left")
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.map { a: ((MagPapers, MagJournal), MagPaperWithAuthorList) => ConversionUtil.createOAFFromJournalAuthorPaper(a) }.write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/merge_step_2")
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var magPubs: Dataset[(String, Publication)] = spark.read.load(s"${parser.get("targetPath")}/merge_step_2").as[Publication].map(p => (ConversionUtil.extractMagIdentifier(p.getOriginalId.asScala), p)).as[(String, Publication)]
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val paperUrlDataset = spark.read.load(s"$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"${parser.get("targetPath")}/merge_step_3")
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logger.info("Phase 6) Enrich Publication with description")
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val pa = spark.read.load(s"${parser.get("sourcePath")}/PaperAbstractsInvertedIndex").as[MagPaperAbstract]
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pa.map(ConversionUtil.transformPaperAbstract).write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/PaperAbstract")
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val paperAbstract = spark.read.load((s"${parser.get("targetPath")}/PaperAbstract")).as[MagPaperAbstract]
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magPubs = spark.read.load(s"${parser.get("targetPath")}/merge_step_3").as[Publication].map(p => (ConversionUtil.extractMagIdentifier(p.getOriginalId.asScala), p)).as[(String, Publication)]
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magPubs.joinWith(paperAbstract, col("_1").equalTo(paperAbstract("PaperId")), "left").map(p => {
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val pub = p._1._2
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val abst = p._2
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if (abst != null) {
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pub.setDescription(List(asField(abst.IndexedAbstract)).asJava)
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}
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pub
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}
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).write.mode(SaveMode.Overwrite).save(s"${parser.get("targetPath")}/merge_step_4")
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logger.info("Phase 7) Enrich Publication with FieldOfStudy")
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val magPubs = spark.read.load(s"${parser.get("targetPath")}/merge_step_4").as[Publication].map(p => (ConversionUtil.extractMagIdentifier(p.getOriginalId.asScala), p)).as[(String, Publication)]
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magPubs = spark.read.load(s"${parser.get("targetPath")}/merge_step_4").as[Publication].map(p => (ConversionUtil.extractMagIdentifier(p.getOriginalId.asScala), p)).as[(String, Publication)]
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val fos = spark.read.load(s"$sourcePath/FieldsOfStudy").select($"FieldOfStudyId".alias("fos"), $"DisplayName", $"MainType")
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@ -135,7 +134,6 @@ object SparkPreProcessMAG {
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.groupBy($"PaperId").agg(collect_list(struct($"FieldOfStudyId", $"DisplayName", $"MainType", $"Score")).as("subjects"))
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.as[MagFieldOfStudy]
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magPubs.joinWith(paperField, col("_1").equalTo(paperField("PaperId")), "left").
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map(item => {
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val publication = item._1._2
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@ -90,9 +90,9 @@ class CrossrefMappingTest {
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val rels:List[Relation] = resultList.filter(p => p.isInstanceOf[Relation]).map(r=> r.asInstanceOf[Relation])
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assertEquals(rels.size, 4)
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rels.foreach(s => logger.info(s.getTarget))
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rels.foreach(s => logger.info(s.getTarget))
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assertEquals(rels.size, 3 )
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}
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@ -179,12 +179,13 @@
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],
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"funder": [
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{
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"DOI": "10.13039/501100001711",
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"name": "Swiss National Science Foundation (Schweizerische Nationalfonds)",
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"doi-asserted-by": "publisher",
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"DOI": "10.13039/501100000781",
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"name": "European Research Council",
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"award": [
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"284236-REPCOLLAB",
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"FP7/2007-2013"
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"CR32I3_156724",
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"31003A_173281/1",
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"200021_165850"
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]
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}
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],
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