Semi-Automatic Creation of Concept Maps

Information

• Publication Type: Master Thesis
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: August 2020
• Date (Start): 23. January 2020
• Date (End): 20. August 2020
• Second Supervisor: Manuela Waldner
• Diploma Examination: 20. August 2020
• Open Access: yes
• First Supervisor: Eduard Gröller
• Pages: 122
• Keywords: Concept Map, Natural language processing, NLP

Abstract

Concept maps are a method for the visualization of knowledge and an established tool in education, knowledge organization and a variety of other fields. They are composed of concepts and interlinked relations between them and are displayed as a node-link diagram. Concept map mining is the process of extracting concept maps from unstructured text. The three approaches to mine concept maps are: manual, semi-automatic or fully automatic. A fully automatic approach cannot mirror the mental knowledge model, which a user would transfer to a manually created concept map. The manual process is often perceived as tedious and ineÿcient, limiting a wide-range application of concept maps. This thesis presents a semi-automatic concept map mining approach that tries to bridge the gap between all manual construction and fully automatic approaches. The advantage of this approach is that the users still have control over how their concept map is constructed, but are not impeded by manual tasks that are often repetitive and ineÿcient. The presented approach is composed of an automatic text processing part, which extracts concepts and relations out of an unstructured text document and is powered by state-of-the-art natural language processing and neural coreference resolution. The second manual concept map creation part allows the creation of concept maps in a user interface and presents the extracted concepts and relations as suggestions to the user. In a user study, an implemented prototype of the proposed semi-automatic concept map mining approach was evaluated. Manual gold standard concept maps that were created by the users and concept maps created by a fully automatic tool were compared to concept maps that were created with the prototype, proving the usefulness of the process. Results show that concept maps created with the semi-automatic prototype are significantly more similar to the gold standard than the ones created by the fully automatic tool. Additionally, considerably improved eÿciency in creation duration and user satisfaction could be observed in comparison to the manual creation of the gold standard maps.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.34726/hss.2020.70123

BibTeX

@mastersthesis{Presch_2020,
  title =      "Semi-Automatic Creation of Concept Maps",
  author =     "Christoph Presch",
  year =       "2020",
  abstract =   "Concept maps are a method for the visualization of knowledge
               and an established tool in education, knowledge organization
               and a variety of other fields. They are composed of concepts
               and interlinked relations between them and are displayed as
               a node-link diagram. Concept map mining is the process of
               extracting concept maps from unstructured text. The three
               approaches to mine concept maps are: manual, semi-automatic
               or fully automatic. A fully automatic approach cannot mirror
               the mental knowledge model, which a user would transfer to a
               manually created concept map. The manual process is often
               perceived as tedious and ineÿcient, limiting a wide-range
               application of concept maps. This thesis presents a
               semi-automatic concept map mining approach that tries to
               bridge the gap between all manual construction and fully
               automatic approaches. The advantage of this approach is that
               the users still have control over how their concept map is
               constructed, but are not impeded by manual tasks that are
               often repetitive and ineÿcient. The presented approach is
               composed of an automatic text processing part, which
               extracts concepts and relations out of an unstructured text
               document and is powered by state-of-the-art natural language
               processing and neural coreference resolution. The second
               manual concept map creation part allows the creation of
               concept maps in a user interface and presents the extracted
               concepts and relations as suggestions to the user. In a user
               study, an implemented prototype of the proposed
               semi-automatic concept map mining approach was evaluated.
               Manual gold standard concept maps that were created by the
               users and concept maps created by a fully automatic tool
               were compared to concept maps that were created with the
               prototype, proving the usefulness of the process. Results
               show that concept maps created with the semi-automatic
               prototype are significantly more similar to the gold
               standard than the ones created by the fully automatic tool.
               Additionally, considerably improved eÿciency in creation
               duration and user satisfaction could be observed in
               comparison to the manual creation of the gold standard maps.",
  month =      aug,
  pages =      "122",
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien",
  keywords =   "Concept Map, Natural language processing, NLP",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2020/Presch_2020/",
}