Research on a new automatic generation algorithm of concept map based on text analysis and association rules mining View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-07-11

AUTHORS

Zengzhen Shao, Yancong Li, Xiao Wang, Xuechen Zhao, Yanhui Guo

ABSTRACT

As an important knowledge visualization tool, concept map has become a research hotspot in educational data mining. Traditional concept map generation algorithms are difficult to generate concept maps quickly because of their strong reliance on experts’ experience. A hybrid TA-ARM algorithm for automatic generation of concept map based on text analysis and association rule mining is proposed. The TA-ARM algorithm fully considers the association rules between concepts, uses the text classification algorithm in text analysis technology instead of manually classify the questions into concepts, and combines the association rule mining method to generate concept maps. The experimental result shows that the TA-ARM algorithm can automatically and rapidly generate the concept map, which not only reduces the impact of outside experts, but can also dynamically adjusts the concept map based on the parameters such as the threshold of confidence between test questions. The concept map generated by the TA-ARM algorithm expresses the association rules between the concepts and the degree of closeness through the associated pairs and relevant degree, and can clearly show the structural associations between concepts. The contrast experiment shows that the quality of the concept map automatically generated by the TA-ARM has a high quality and can visualize the associations between concepts and provide optimization and guidance for knowledge visualization. More... »

PAGES

1-13

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12652-018-0934-9

DOI

http://dx.doi.org/10.1007/s12652-018-0934-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105490833


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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Shandong Normal University", 
          "id": "https://www.grid.ac/institutes/grid.410585.d", 
          "name": [
            "School of Data Science and Computer Science, Shandong Women\u2019s University, 250002, Jinan, China", 
            "School of Information Science and Engineering, Shandong Normal University, 250014, Jinan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shao", 
        "givenName": "Zengzhen", 
        "id": "sg:person.07615037473.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07615037473.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shandong Normal University", 
          "id": "https://www.grid.ac/institutes/grid.410585.d", 
          "name": [
            "School of Information Science and Engineering, Shandong Normal University, 250014, Jinan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Yancong", 
        "id": "sg:person.012065504610.35", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012065504610.35"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shandong Normal University", 
          "id": "https://www.grid.ac/institutes/grid.410585.d", 
          "name": [
            "School of Information Science and Engineering, Shandong Normal University, 250014, Jinan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Xiao", 
        "id": "sg:person.012663065210.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012663065210.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shandong Women\u2019s University", 
          "id": "https://www.grid.ac/institutes/grid.495262.e", 
          "name": [
            "School of Data Science and Computer Science, Shandong Women\u2019s University, 250002, Jinan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhao", 
        "givenName": "Xuechen", 
        "id": "sg:person.016054062471.60", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016054062471.60"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shandong Women\u2019s University", 
          "id": "https://www.grid.ac/institutes/grid.495262.e", 
          "name": [
            "School of Data Science and Computer Science, Shandong Women\u2019s University, 250002, Jinan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Guo", 
        "givenName": "Yanhui", 
        "id": "sg:person.011017664212.30", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011017664212.30"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/tea.3660310109", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007196016"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0169-2070(01)00128-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011896737"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-387-30164-8_27", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015237128", 
          "https://doi.org/10.1007/978-0-387-30164-8_27"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00778-007-0067-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015540559", 
          "https://doi.org/10.1007/s00778-007-0067-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00778-007-0067-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015540559", 
          "https://doi.org/10.1007/s00778-007-0067-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-23765-2_31", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017132285", 
          "https://doi.org/10.1007/978-3-642-23765-2_31"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-23765-2_31", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017132285", 
          "https://doi.org/10.1007/978-3-642-23765-2_31"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/18.12.1553", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020879079"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1353/lan.2004.0017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022049031"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2851613.2851735", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022501703"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.artmed.2004.03.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023014886"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eswa.2007.06.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025372168"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compedu.2005.11.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026329678"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/170035.170072", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028726331"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.teln.2016.02.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030179917"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2990508", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031101289"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01972243.2012.632283", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035002265"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0165551513494645", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036262706"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0165551513494645", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036262706"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1502650.1502718", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039124560"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s41039-015-0018-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040430281", 
          "https://doi.org/10.1186/s41039-015-0018-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s41039-015-0018-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040430281", 
          "https://doi.org/10.1186/s41039-015-0018-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s41039-015-0018-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040430281", 
          "https://doi.org/10.1186/s41039-015-0018-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eswa.2006.04.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043747056"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-20910-4_18", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044913728", 
          "https://doi.org/10.1007/978-3-319-20910-4_18"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-11933-5_64", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046723700", 
          "https://doi.org/10.1007/978-3-319-11933-5_64"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-36618-0_30", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047791641", 
          "https://doi.org/10.1007/3-540-36618-0_30"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt1206-1565", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051026888", 
          "https://doi.org/10.1038/nbt1206-1565"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt1206-1565", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051026888", 
          "https://doi.org/10.1038/nbt1206-1565"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eswa.2012.11.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052503713"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mis.2014.42", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061406476"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/tech.2007.s697", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064202505"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3102/1076998616666808", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070973334"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3102/1076998616666808", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070973334"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4899-7687-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084844808", 
          "https://doi.org/10.1007/978-1-4899-7687-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4899-7687-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084844808", 
          "https://doi.org/10.1007/978-1-4899-7687-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compedu.2017.08.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091051983"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/16-sts602", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091468108"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4899-7993-3_918-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091564151", 
          "https://doi.org/10.1007/978-1-4899-7993-3_918-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.12973/ejmste/78195", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092107421"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icalt.2006.1652537", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094443290"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/nabic.2011.6089624", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094447746"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/scp.2015.7342234", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095685970"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iiai-aai.2017.165", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095686164"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/cbo9781139173469", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098663941"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5220/0005554702480254", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099466841"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.12948/issn14531305/21.4.2017.04", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100146866"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/3167132.3234663", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105354256"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/3167132.3234663", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105354256"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-07-11", 
    "datePublishedReg": "2018-07-11", 
    "description": "As an important knowledge visualization tool, concept map has become a research hotspot in educational data mining. Traditional concept map generation algorithms are difficult to generate concept maps quickly because of their strong reliance on experts\u2019 experience. A hybrid TA-ARM algorithm for automatic generation of concept map based on text analysis and association rule mining is proposed. The TA-ARM algorithm fully considers the association rules between concepts, uses the text classification algorithm in text analysis technology instead of manually classify the questions into concepts, and combines the association rule mining method to generate concept maps. The experimental result shows that the TA-ARM algorithm can automatically and rapidly generate the concept map, which not only reduces the impact of outside experts, but can also dynamically adjusts the concept map based on the parameters such as the threshold of confidence between test questions. The concept map generated by the TA-ARM algorithm expresses the association rules between the concepts and the degree of closeness through the associated pairs and relevant degree, and can clearly show the structural associations between concepts. The contrast experiment shows that the quality of the concept map automatically generated by the TA-ARM has a high quality and can visualize the associations between concepts and provide optimization and guidance for knowledge visualization.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s12652-018-0934-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1043999", 
        "issn": [
          "1868-5137", 
          "1868-5145"
        ], 
        "name": "Journal of Ambient Intelligence and Humanized Computing", 
        "type": "Periodical"
      }
    ], 
    "name": "Research on a new automatic generation algorithm of concept map based on text analysis and association rules mining", 
    "pagination": "1-13", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "ceb6188b505d1eca225644d22e208100a764a7d603d620e7bc93995adeb5b609"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s12652-018-0934-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1105490833"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s12652-018-0934-9", 
      "https://app.dimensions.ai/details/publication/pub.1105490833"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:43", 
    "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/v90/0000000363_0000000363/records_70064_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs12652-018-0934-9"
  }
]
 

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/s12652-018-0934-9'

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/s12652-018-0934-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12652-018-0934-9'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12652-018-0934-9'


 

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

217 TRIPLES      21 PREDICATES      64 URIs      16 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s12652-018-0934-9 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Nee64fde85dcd4d2c9645475f6e72e40a
4 schema:citation sg:pub.10.1007/3-540-36618-0_30
5 sg:pub.10.1007/978-0-387-30164-8_27
6 sg:pub.10.1007/978-1-4899-7687-1
7 sg:pub.10.1007/978-1-4899-7993-3_918-2
8 sg:pub.10.1007/978-3-319-11933-5_64
9 sg:pub.10.1007/978-3-319-20910-4_18
10 sg:pub.10.1007/978-3-642-23765-2_31
11 sg:pub.10.1007/s00778-007-0067-9
12 sg:pub.10.1038/nbt1206-1565
13 sg:pub.10.1186/s41039-015-0018-9
14 https://doi.org/10.1002/tea.3660310109
15 https://doi.org/10.1016/j.artmed.2004.03.006
16 https://doi.org/10.1016/j.compedu.2005.11.020
17 https://doi.org/10.1016/j.compedu.2017.08.001
18 https://doi.org/10.1016/j.eswa.2006.04.005
19 https://doi.org/10.1016/j.eswa.2007.06.013
20 https://doi.org/10.1016/j.eswa.2012.11.018
21 https://doi.org/10.1016/j.teln.2016.02.001
22 https://doi.org/10.1016/s0169-2070(01)00128-5
23 https://doi.org/10.1017/cbo9781139173469
24 https://doi.org/10.1080/01972243.2012.632283
25 https://doi.org/10.1093/bioinformatics/18.12.1553
26 https://doi.org/10.1109/icalt.2006.1652537
27 https://doi.org/10.1109/iiai-aai.2017.165
28 https://doi.org/10.1109/mis.2014.42
29 https://doi.org/10.1109/nabic.2011.6089624
30 https://doi.org/10.1109/scp.2015.7342234
31 https://doi.org/10.1145/1502650.1502718
32 https://doi.org/10.1145/170035.170072
33 https://doi.org/10.1145/2851613.2851735
34 https://doi.org/10.1145/2990508
35 https://doi.org/10.1145/3167132.3234663
36 https://doi.org/10.1177/0165551513494645
37 https://doi.org/10.1198/tech.2007.s697
38 https://doi.org/10.1214/16-sts602
39 https://doi.org/10.12948/issn14531305/21.4.2017.04
40 https://doi.org/10.12973/ejmste/78195
41 https://doi.org/10.1353/lan.2004.0017
42 https://doi.org/10.3102/1076998616666808
43 https://doi.org/10.5220/0005554702480254
44 schema:datePublished 2018-07-11
45 schema:datePublishedReg 2018-07-11
46 schema:description As an important knowledge visualization tool, concept map has become a research hotspot in educational data mining. Traditional concept map generation algorithms are difficult to generate concept maps quickly because of their strong reliance on experts’ experience. A hybrid TA-ARM algorithm for automatic generation of concept map based on text analysis and association rule mining is proposed. The TA-ARM algorithm fully considers the association rules between concepts, uses the text classification algorithm in text analysis technology instead of manually classify the questions into concepts, and combines the association rule mining method to generate concept maps. The experimental result shows that the TA-ARM algorithm can automatically and rapidly generate the concept map, which not only reduces the impact of outside experts, but can also dynamically adjusts the concept map based on the parameters such as the threshold of confidence between test questions. The concept map generated by the TA-ARM algorithm expresses the association rules between the concepts and the degree of closeness through the associated pairs and relevant degree, and can clearly show the structural associations between concepts. The contrast experiment shows that the quality of the concept map automatically generated by the TA-ARM has a high quality and can visualize the associations between concepts and provide optimization and guidance for knowledge visualization.
47 schema:genre research_article
48 schema:inLanguage en
49 schema:isAccessibleForFree false
50 schema:isPartOf sg:journal.1043999
51 schema:name Research on a new automatic generation algorithm of concept map based on text analysis and association rules mining
52 schema:pagination 1-13
53 schema:productId N11b369d5acef4b00aeb178cf79fa5724
54 Na15cb140fee8410f89f445849b3e5c80
55 Nc3fd8e167da24e95a6c8252e27819fdc
56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105490833
57 https://doi.org/10.1007/s12652-018-0934-9
58 schema:sdDatePublished 2019-04-11T12:43
59 schema:sdLicense https://scigraph.springernature.com/explorer/license/
60 schema:sdPublisher Nd7eb3801edf046069fda13c1fec46c83
61 schema:url https://link.springer.com/10.1007%2Fs12652-018-0934-9
62 sgo:license sg:explorer/license/
63 sgo:sdDataset articles
64 rdf:type schema:ScholarlyArticle
65 N11b369d5acef4b00aeb178cf79fa5724 schema:name dimensions_id
66 schema:value pub.1105490833
67 rdf:type schema:PropertyValue
68 N2787520aaf69431c8b92813ee8af8cf0 rdf:first sg:person.012663065210.18
69 rdf:rest N37721a55e76b4319b18bc3ca74a59fbf
70 N37721a55e76b4319b18bc3ca74a59fbf rdf:first sg:person.016054062471.60
71 rdf:rest N83665d9807864562bd4f81640635a885
72 N83665d9807864562bd4f81640635a885 rdf:first sg:person.011017664212.30
73 rdf:rest rdf:nil
74 Na15cb140fee8410f89f445849b3e5c80 schema:name doi
75 schema:value 10.1007/s12652-018-0934-9
76 rdf:type schema:PropertyValue
77 Na16b41f1929642a2952dc3270b5ccb5e rdf:first sg:person.012065504610.35
78 rdf:rest N2787520aaf69431c8b92813ee8af8cf0
79 Nc3fd8e167da24e95a6c8252e27819fdc schema:name readcube_id
80 schema:value ceb6188b505d1eca225644d22e208100a764a7d603d620e7bc93995adeb5b609
81 rdf:type schema:PropertyValue
82 Nd7eb3801edf046069fda13c1fec46c83 schema:name Springer Nature - SN SciGraph project
83 rdf:type schema:Organization
84 Nee64fde85dcd4d2c9645475f6e72e40a rdf:first sg:person.07615037473.39
85 rdf:rest Na16b41f1929642a2952dc3270b5ccb5e
86 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
87 schema:name Information and Computing Sciences
88 rdf:type schema:DefinedTerm
89 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
90 schema:name Artificial Intelligence and Image Processing
91 rdf:type schema:DefinedTerm
92 sg:journal.1043999 schema:issn 1868-5137
93 1868-5145
94 schema:name Journal of Ambient Intelligence and Humanized Computing
95 rdf:type schema:Periodical
96 sg:person.011017664212.30 schema:affiliation https://www.grid.ac/institutes/grid.495262.e
97 schema:familyName Guo
98 schema:givenName Yanhui
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011017664212.30
100 rdf:type schema:Person
101 sg:person.012065504610.35 schema:affiliation https://www.grid.ac/institutes/grid.410585.d
102 schema:familyName Li
103 schema:givenName Yancong
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012065504610.35
105 rdf:type schema:Person
106 sg:person.012663065210.18 schema:affiliation https://www.grid.ac/institutes/grid.410585.d
107 schema:familyName Wang
108 schema:givenName Xiao
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012663065210.18
110 rdf:type schema:Person
111 sg:person.016054062471.60 schema:affiliation https://www.grid.ac/institutes/grid.495262.e
112 schema:familyName Zhao
113 schema:givenName Xuechen
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016054062471.60
115 rdf:type schema:Person
116 sg:person.07615037473.39 schema:affiliation https://www.grid.ac/institutes/grid.410585.d
117 schema:familyName Shao
118 schema:givenName Zengzhen
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07615037473.39
120 rdf:type schema:Person
121 sg:pub.10.1007/3-540-36618-0_30 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047791641
122 https://doi.org/10.1007/3-540-36618-0_30
123 rdf:type schema:CreativeWork
124 sg:pub.10.1007/978-0-387-30164-8_27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015237128
125 https://doi.org/10.1007/978-0-387-30164-8_27
126 rdf:type schema:CreativeWork
127 sg:pub.10.1007/978-1-4899-7687-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084844808
128 https://doi.org/10.1007/978-1-4899-7687-1
129 rdf:type schema:CreativeWork
130 sg:pub.10.1007/978-1-4899-7993-3_918-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091564151
131 https://doi.org/10.1007/978-1-4899-7993-3_918-2
132 rdf:type schema:CreativeWork
133 sg:pub.10.1007/978-3-319-11933-5_64 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046723700
134 https://doi.org/10.1007/978-3-319-11933-5_64
135 rdf:type schema:CreativeWork
136 sg:pub.10.1007/978-3-319-20910-4_18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044913728
137 https://doi.org/10.1007/978-3-319-20910-4_18
138 rdf:type schema:CreativeWork
139 sg:pub.10.1007/978-3-642-23765-2_31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017132285
140 https://doi.org/10.1007/978-3-642-23765-2_31
141 rdf:type schema:CreativeWork
142 sg:pub.10.1007/s00778-007-0067-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015540559
143 https://doi.org/10.1007/s00778-007-0067-9
144 rdf:type schema:CreativeWork
145 sg:pub.10.1038/nbt1206-1565 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051026888
146 https://doi.org/10.1038/nbt1206-1565
147 rdf:type schema:CreativeWork
148 sg:pub.10.1186/s41039-015-0018-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040430281
149 https://doi.org/10.1186/s41039-015-0018-9
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1002/tea.3660310109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007196016
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1016/j.artmed.2004.03.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023014886
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1016/j.compedu.2005.11.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026329678
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1016/j.compedu.2017.08.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091051983
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1016/j.eswa.2006.04.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043747056
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1016/j.eswa.2007.06.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025372168
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1016/j.eswa.2012.11.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052503713
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1016/j.teln.2016.02.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030179917
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1016/s0169-2070(01)00128-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011896737
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1017/cbo9781139173469 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098663941
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1080/01972243.2012.632283 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035002265
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1093/bioinformatics/18.12.1553 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020879079
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1109/icalt.2006.1652537 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094443290
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1109/iiai-aai.2017.165 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095686164
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1109/mis.2014.42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061406476
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1109/nabic.2011.6089624 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094447746
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1109/scp.2015.7342234 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095685970
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1145/1502650.1502718 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039124560
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1145/170035.170072 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028726331
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1145/2851613.2851735 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022501703
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1145/2990508 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031101289
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1145/3167132.3234663 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105354256
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1177/0165551513494645 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036262706
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1198/tech.2007.s697 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064202505
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1214/16-sts602 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091468108
200 rdf:type schema:CreativeWork
201 https://doi.org/10.12948/issn14531305/21.4.2017.04 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100146866
202 rdf:type schema:CreativeWork
203 https://doi.org/10.12973/ejmste/78195 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092107421
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1353/lan.2004.0017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022049031
206 rdf:type schema:CreativeWork
207 https://doi.org/10.3102/1076998616666808 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070973334
208 rdf:type schema:CreativeWork
209 https://doi.org/10.5220/0005554702480254 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099466841
210 rdf:type schema:CreativeWork
211 https://www.grid.ac/institutes/grid.410585.d schema:alternateName Shandong Normal University
212 schema:name School of Data Science and Computer Science, Shandong Women’s University, 250002, Jinan, China
213 School of Information Science and Engineering, Shandong Normal University, 250014, Jinan, China
214 rdf:type schema:Organization
215 https://www.grid.ac/institutes/grid.495262.e schema:alternateName Shandong Women’s University
216 schema:name School of Data Science and Computer Science, Shandong Women’s University, 250002, Jinan, China
217 rdf:type schema:Organization
 




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


...