Random indexing of multidimensional data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-07

AUTHORS

Fredrik Sandin, Blerim Emruli, Magnus Sahlgren

ABSTRACT

Random indexing (RI) is a lightweight dimension reduction method, which is used, for example, to approximate vector semantic relationships in online natural language processing systems. Here we generalise RI to multidimensional arrays and therefore enable approximation of higher-order statistical relationships in data. The generalised method is a sparse implementation of random projections, which is the theoretical basis also for ordinary RI and other randomisation approaches to dimensionality reduction and data representation. We present numerical experiments which demonstrate that a multidimensional generalisation of RI is feasible, including comparisons with ordinary RI and principal component analysis. The RI method is well suited for online processing of data streams because relationship weights can be updated incrementally in a fixed-size distributed representation, and inner products can be approximated on the fly at low computational cost. An open source implementation of generalised RI is provided. More... »

PAGES

267-290

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10115-016-1012-2

DOI

http://dx.doi.org/10.1007/s10115-016-1012-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011109987


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Lule\u00e5 University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.6926.b", 
          "name": [
            "EISLAB, Lule\u00e5 University of Technology, 971 87, Lule\u00e5, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sandin", 
        "givenName": "Fredrik", 
        "id": "sg:person.010377404153.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010377404153.16"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "SICS Swedish ICT, 722 13, V\u00e4ster\u00e5s, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Emruli", 
        "givenName": "Blerim", 
        "id": "sg:person.012457220110.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012457220110.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Swedish Institute of Computer Science", 
          "id": "https://www.grid.ac/institutes/grid.6383.e", 
          "name": [
            "SICS Swedish ICT, 164 29, Kista, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sahlgren", 
        "givenName": "Magnus", 
        "id": "sg:person.015471575003.94", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015471575003.94"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s12559-009-9009-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001203994", 
          "https://doi.org/10.1007/s12559-009-9009-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jbi.2009.02.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003728364"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1037/0033-295x.104.2.211", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004059775"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1645953.1646006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007243267"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-22218-4_45", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007940646", 
          "https://doi.org/10.1007/978-3-642-22218-4_45"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-22218-4_45", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007940646", 
          "https://doi.org/10.1007/978-3-642-22218-4_45"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1097-4571(199009)41:6<391::aid-asi1>3.0.co;2-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012153938"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1201/9781420028683.ch4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013788700"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/956750.956812", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014868628"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12559-013-9206-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015472257", 
          "https://doi.org/10.1007/s12559-013-9206-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.3758/s13428-011-0183-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015900645", 
          "https://doi.org/10.3758/s13428-011-0183-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jss.2012.06.025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017732179"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-0000(03)00025-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022116997"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-0000(03)00025-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022116997"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/2041-1480-2-s5-s7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023911410", 
          "https://doi.org/10.1186/2041-1480-2-s5-s7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/502512.502546", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026579448"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1162/coli_a_00016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031630229"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jbi.2010.04.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031807925"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1032573094", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-387-84858-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032573094", 
          "https://doi.org/10.1007/978-0-387-84858-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-387-84858-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032573094", 
          "https://doi.org/10.1007/978-0-387-84858-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.3758/bf03204766", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035784262", 
          "https://doi.org/10.3758/bf03204766"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/rsa.20218", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038179404"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/275487.275505", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039204113"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0095-8956(88)90043-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039742478"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1117/12.605553", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039832629"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/rsa.10073", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045759576"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1409620.1409621", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046323559"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10115-011-0453-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050682840", 
          "https://doi.org/10.1007/s10115-011-0453-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2559902", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051504112"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1197448", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062462880"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.331.6018.692", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062606308"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/07070111x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062851534"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/090771806", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062856710"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1561/2200000035", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068001410"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1090/conm/026/737400", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1089211716"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ijcnn.2013.6706829", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093295584"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ijcnn.1998.682302", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093503930"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/sequen.1997.666900", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095535976"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3115/1220175.1220221", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099221973"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3115/1220175.1220221", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099221973"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3115/1705415.1705426", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099222112"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3115/1073083.1073153", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099239642"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3115/1073083.1073153", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099239642"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1613/jair.2934", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105674405"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-07", 
    "datePublishedReg": "2017-07-01", 
    "description": "Random indexing (RI) is a lightweight dimension reduction method, which is used, for example, to approximate vector semantic relationships in online natural language processing systems. Here we generalise RI to multidimensional arrays and therefore enable approximation of higher-order statistical relationships in data. The generalised method is a sparse implementation of random projections, which is the theoretical basis also for ordinary RI and other randomisation approaches to dimensionality reduction and data representation. We present numerical experiments which demonstrate that a multidimensional generalisation of RI is feasible, including comparisons with ordinary RI and principal component analysis. The RI method is well suited for online processing of data streams because relationship weights can be updated incrementally in a fixed-size distributed representation, and inner products can be approximated on the fly at low computational cost. An open source implementation of generalised RI is provided.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10115-016-1012-2", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1041769", 
        "issn": [
          "0219-1377", 
          "0219-3116"
        ], 
        "name": "Knowledge and Information Systems", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "52"
      }
    ], 
    "name": "Random indexing of multidimensional data", 
    "pagination": "267-290", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "34eedf0b8a2f6ae9753f5ca64fc593685e794a3fdaacde948565449c84029c26"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10115-016-1012-2"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1011109987"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10115-016-1012-2", 
      "https://app.dimensions.ai/details/publication/pub.1011109987"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:21", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000362_0000000362/records_87079_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10115-016-1012-2"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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/s10115-016-1012-2'

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/s10115-016-1012-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10115-016-1012-2'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10115-016-1012-2'


 

This table displays all metadata directly associated to this object as RDF triples.

207 TRIPLES      21 PREDICATES      67 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10115-016-1012-2 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author Nda6a07536d33458bbd6b5ec501356958
4 schema:citation sg:pub.10.1007/978-0-387-84858-7
5 sg:pub.10.1007/978-3-642-22218-4_45
6 sg:pub.10.1007/s10115-011-0453-x
7 sg:pub.10.1007/s12559-009-9009-8
8 sg:pub.10.1007/s12559-013-9206-3
9 sg:pub.10.1186/2041-1480-2-s5-s7
10 sg:pub.10.3758/bf03204766
11 sg:pub.10.3758/s13428-011-0183-8
12 https://app.dimensions.ai/details/publication/pub.1032573094
13 https://doi.org/10.1002/(sici)1097-4571(199009)41:6<391::aid-asi1>3.0.co;2-9
14 https://doi.org/10.1002/rsa.10073
15 https://doi.org/10.1002/rsa.20218
16 https://doi.org/10.1016/0095-8956(88)90043-3
17 https://doi.org/10.1016/j.jbi.2009.02.002
18 https://doi.org/10.1016/j.jbi.2010.04.001
19 https://doi.org/10.1016/j.jss.2012.06.025
20 https://doi.org/10.1016/s0022-0000(03)00025-4
21 https://doi.org/10.1037/0033-295x.104.2.211
22 https://doi.org/10.1090/conm/026/737400
23 https://doi.org/10.1109/ijcnn.1998.682302
24 https://doi.org/10.1109/ijcnn.2013.6706829
25 https://doi.org/10.1109/sequen.1997.666900
26 https://doi.org/10.1117/12.605553
27 https://doi.org/10.1126/science.1197448
28 https://doi.org/10.1126/science.331.6018.692
29 https://doi.org/10.1137/07070111x
30 https://doi.org/10.1137/090771806
31 https://doi.org/10.1145/1409620.1409621
32 https://doi.org/10.1145/1645953.1646006
33 https://doi.org/10.1145/2559902
34 https://doi.org/10.1145/275487.275505
35 https://doi.org/10.1145/502512.502546
36 https://doi.org/10.1145/956750.956812
37 https://doi.org/10.1162/coli_a_00016
38 https://doi.org/10.1201/9781420028683.ch4
39 https://doi.org/10.1561/2200000035
40 https://doi.org/10.1613/jair.2934
41 https://doi.org/10.3115/1073083.1073153
42 https://doi.org/10.3115/1220175.1220221
43 https://doi.org/10.3115/1705415.1705426
44 schema:datePublished 2017-07
45 schema:datePublishedReg 2017-07-01
46 schema:description Random indexing (RI) is a lightweight dimension reduction method, which is used, for example, to approximate vector semantic relationships in online natural language processing systems. Here we generalise RI to multidimensional arrays and therefore enable approximation of higher-order statistical relationships in data. The generalised method is a sparse implementation of random projections, which is the theoretical basis also for ordinary RI and other randomisation approaches to dimensionality reduction and data representation. We present numerical experiments which demonstrate that a multidimensional generalisation of RI is feasible, including comparisons with ordinary RI and principal component analysis. The RI method is well suited for online processing of data streams because relationship weights can be updated incrementally in a fixed-size distributed representation, and inner products can be approximated on the fly at low computational cost. An open source implementation of generalised RI is provided.
47 schema:genre research_article
48 schema:inLanguage en
49 schema:isAccessibleForFree true
50 schema:isPartOf N296e3c45b7594bb48d22703223ed249f
51 Nf013138af95441ed96819cac69492781
52 sg:journal.1041769
53 schema:name Random indexing of multidimensional data
54 schema:pagination 267-290
55 schema:productId N363654c2f8bd4384ba7633f64baa6a2e
56 N6219595b9b4e4a74868ca279478751df
57 Nfc6675ac5315449d992102172b6cec56
58 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011109987
59 https://doi.org/10.1007/s10115-016-1012-2
60 schema:sdDatePublished 2019-04-11T12:21
61 schema:sdLicense https://scigraph.springernature.com/explorer/license/
62 schema:sdPublisher Nea7cabd6087e4028a8885705d3e4d081
63 schema:url https://link.springer.com/10.1007%2Fs10115-016-1012-2
64 sgo:license sg:explorer/license/
65 sgo:sdDataset articles
66 rdf:type schema:ScholarlyArticle
67 N0dbc22d906a945acb4504f4b8b0b55e8 rdf:first sg:person.015471575003.94
68 rdf:rest rdf:nil
69 N296e3c45b7594bb48d22703223ed249f schema:volumeNumber 52
70 rdf:type schema:PublicationVolume
71 N363654c2f8bd4384ba7633f64baa6a2e schema:name dimensions_id
72 schema:value pub.1011109987
73 rdf:type schema:PropertyValue
74 N6219595b9b4e4a74868ca279478751df schema:name doi
75 schema:value 10.1007/s10115-016-1012-2
76 rdf:type schema:PropertyValue
77 N66ebbc0c43f7489c9d39239ae3710a35 rdf:first sg:person.012457220110.50
78 rdf:rest N0dbc22d906a945acb4504f4b8b0b55e8
79 Nda6a07536d33458bbd6b5ec501356958 rdf:first sg:person.010377404153.16
80 rdf:rest N66ebbc0c43f7489c9d39239ae3710a35
81 Ne32513fb7fec4a52a14587ee697e8216 schema:name SICS Swedish ICT, 722 13, Västerås, Sweden
82 rdf:type schema:Organization
83 Nea7cabd6087e4028a8885705d3e4d081 schema:name Springer Nature - SN SciGraph project
84 rdf:type schema:Organization
85 Nf013138af95441ed96819cac69492781 schema:issueNumber 1
86 rdf:type schema:PublicationIssue
87 Nfc6675ac5315449d992102172b6cec56 schema:name readcube_id
88 schema:value 34eedf0b8a2f6ae9753f5ca64fc593685e794a3fdaacde948565449c84029c26
89 rdf:type schema:PropertyValue
90 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
91 schema:name Mathematical Sciences
92 rdf:type schema:DefinedTerm
93 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
94 schema:name Statistics
95 rdf:type schema:DefinedTerm
96 sg:journal.1041769 schema:issn 0219-1377
97 0219-3116
98 schema:name Knowledge and Information Systems
99 rdf:type schema:Periodical
100 sg:person.010377404153.16 schema:affiliation https://www.grid.ac/institutes/grid.6926.b
101 schema:familyName Sandin
102 schema:givenName Fredrik
103 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010377404153.16
104 rdf:type schema:Person
105 sg:person.012457220110.50 schema:affiliation Ne32513fb7fec4a52a14587ee697e8216
106 schema:familyName Emruli
107 schema:givenName Blerim
108 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012457220110.50
109 rdf:type schema:Person
110 sg:person.015471575003.94 schema:affiliation https://www.grid.ac/institutes/grid.6383.e
111 schema:familyName Sahlgren
112 schema:givenName Magnus
113 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015471575003.94
114 rdf:type schema:Person
115 sg:pub.10.1007/978-0-387-84858-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032573094
116 https://doi.org/10.1007/978-0-387-84858-7
117 rdf:type schema:CreativeWork
118 sg:pub.10.1007/978-3-642-22218-4_45 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007940646
119 https://doi.org/10.1007/978-3-642-22218-4_45
120 rdf:type schema:CreativeWork
121 sg:pub.10.1007/s10115-011-0453-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1050682840
122 https://doi.org/10.1007/s10115-011-0453-x
123 rdf:type schema:CreativeWork
124 sg:pub.10.1007/s12559-009-9009-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001203994
125 https://doi.org/10.1007/s12559-009-9009-8
126 rdf:type schema:CreativeWork
127 sg:pub.10.1007/s12559-013-9206-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015472257
128 https://doi.org/10.1007/s12559-013-9206-3
129 rdf:type schema:CreativeWork
130 sg:pub.10.1186/2041-1480-2-s5-s7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023911410
131 https://doi.org/10.1186/2041-1480-2-s5-s7
132 rdf:type schema:CreativeWork
133 sg:pub.10.3758/bf03204766 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035784262
134 https://doi.org/10.3758/bf03204766
135 rdf:type schema:CreativeWork
136 sg:pub.10.3758/s13428-011-0183-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015900645
137 https://doi.org/10.3758/s13428-011-0183-8
138 rdf:type schema:CreativeWork
139 https://app.dimensions.ai/details/publication/pub.1032573094 schema:CreativeWork
140 https://doi.org/10.1002/(sici)1097-4571(199009)41:6<391::aid-asi1>3.0.co;2-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012153938
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1002/rsa.10073 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045759576
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1002/rsa.20218 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038179404
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1016/0095-8956(88)90043-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039742478
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/j.jbi.2009.02.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003728364
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1016/j.jbi.2010.04.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031807925
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1016/j.jss.2012.06.025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017732179
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1016/s0022-0000(03)00025-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022116997
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1037/0033-295x.104.2.211 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004059775
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1090/conm/026/737400 schema:sameAs https://app.dimensions.ai/details/publication/pub.1089211716
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1109/ijcnn.1998.682302 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093503930
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1109/ijcnn.2013.6706829 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093295584
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1109/sequen.1997.666900 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095535976
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1117/12.605553 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039832629
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1126/science.1197448 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062462880
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1126/science.331.6018.692 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062606308
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1137/07070111x schema:sameAs https://app.dimensions.ai/details/publication/pub.1062851534
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1137/090771806 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062856710
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1145/1409620.1409621 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046323559
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1145/1645953.1646006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007243267
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1145/2559902 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051504112
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1145/275487.275505 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039204113
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1145/502512.502546 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026579448
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1145/956750.956812 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014868628
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1162/coli_a_00016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031630229
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1201/9781420028683.ch4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013788700
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1561/2200000035 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068001410
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1613/jair.2934 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105674405
195 rdf:type schema:CreativeWork
196 https://doi.org/10.3115/1073083.1073153 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099239642
197 rdf:type schema:CreativeWork
198 https://doi.org/10.3115/1220175.1220221 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099221973
199 rdf:type schema:CreativeWork
200 https://doi.org/10.3115/1705415.1705426 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099222112
201 rdf:type schema:CreativeWork
202 https://www.grid.ac/institutes/grid.6383.e schema:alternateName Swedish Institute of Computer Science
203 schema:name SICS Swedish ICT, 164 29, Kista, Sweden
204 rdf:type schema:Organization
205 https://www.grid.ac/institutes/grid.6926.b schema:alternateName Luleå University of Technology
206 schema:name EISLAB, Luleå University of Technology, 971 87, Luleå, Sweden
207 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...