Figure 2.3: Type of relationships between concepts.
hierarchical concept map and Figure 2.5 shows an example of a mind map.
In some cases, concept maps consist of extensions that clarify and complement the concepts. Such extensions include resources such as Web pages, pictures, examples and text in the concept map [nHC+04].
2.2 Applications and uses of concept maps
Concept maps have been widely used in education. They have been demonstrated to be a successful instructional tool to help learners in their understanding process.
Concept maps are popular as they aid in creative thinking, knowledge extraction, planning, note taking, summarization [SRF03], idea generation, knowledge creation [AKM+03] and as assessment [HBN96] and evaluation tools [MMJ94]. Concept maps can also be used to summarize papers. According to [RF05], a concept map can be as good a summary as an abstract, and are easier to automatically prepare and translate than a written abstract.
David et. al [DSB] have used concept maps and concept questions for engineer-ing university level to help in their conceptual understandengineer-ing of the discipline and stimulate thinking. In [WSL06] concept maps have been used in searching through historical archives. These maps provide a representation of the important retrieved entities, that might be used in later searches. Maria [Jak03] demonstrated the
appli-Figure 2.4: Sample concept map of a concept map [NC06].
Figure 2.5: An example of a mind map representing the author’s understanding of Educational Technology course.
2.2. APPLICATIONS AND USES OF CONCEPT MAPS 7 cation of concept maps in conjunction with practical and cognitive apprenticeships to teach and improve programming skills in holistic learners. The use of concept maps proved to stimulate meaningful learning in undergraduate medical students taking a PBL (problem-based learning) [RFP06]. McClure et al [MSS99] researched on the use of concept maps to assess learners’ knowledge on certain concepts.
The use of concept maps is not restricted to education, but they are used in busi-ness planning, public administration and health sector, among others. Concept maps have been employed in community mental health [JBS00] for program plan-ning and evaluation purposes. Compared to other knowledge elicitation tools, con-cept mapping is considered as an efficient method for generating models of domain knowledge [HCCN02]. When integrated with other systems, concept maps have been used as interfaces for intelligent software (i.e., knowledge based systems and tutoring systems) in various domains [CCH+03].
From an educational instructor’s point of view, concept maps can be used to reveal a learners’ understanding or misconception [RRS98] of a certain knowledge domain.
There are no ”correct” concept maps but often the teacher’s concept map is used as a reference map [dRdCJF04]. However, a teacher’s map reflects the teacher’s way of thinking. For a more objective map, a different approach used to construct the concept map is applied. Automatically constructed concept maps are less biased, easy to generate and can be used as reference maps. Hideo et. al [FYI02] developed a concept mapping software that ”supports the externalization of ideas, reflection on thinking processes and dialogues” by allowing collaborative learning by permitting several users to construct one concept map. There have been several tools like CmapTools [LMR+03], Clouds [POC00], Leximancer [SH05] and GNOSIS [GS94], which attempt to construct concept maps, in interaction with the users to generate concept maps automatically.
In summary, a concept map is a type of knowledge representation to develop mental schemas or mind maps that act as a reference for future actions and thinking [BB00].
Concept maps can be applied in different areas and not limited to the education field.
A common issue arising, is the difficulty in evaluating different concept maps. Not even a human expert can say for certain what a ”correct” concept map should look like. Therefore, it can be hypothesized that an automatically constructed concept map has a reduced degree of bias compared to a manually constructed concept map.
Semi-automatic construction of concept maps
Semi-automatic construction of concept maps is an approach where a software tool is used to create concept maps with the help of the user.
In this chapter, we review four tools dedicated to assisting in the process of con-structing concept maps. These methods suggest elements (concepts, topics or re-lations), based on a given domain. As these methods are used for semi-automatic construction of concept maps, the role of the user in the process of construction of concept maps will be discussed. In the section below, we present the algorithms used in the existing methods for extracting and suggesting elements. We give a short summary and comparison of the introduced tools.
In the following section, we introduce four tools which are used in the area of semi-automatic concept map construction; Clouds[POC00], Textstorm[APC01], Cmap-Tools[LMR+03], and a tool for semi-automatically constructing topic ontologies [FMG05].
With no prior knowledge about the domain in focus,Textstorm [APC01] parses and tags a text file producing binary predicates (e.g ”eat(cow, plants)”). The system feeds the output into another system Clouds [PC00].
Textstorm tags a text file using WordNet [MBF+90]. The predicates built map rela-tions between two concepts from parsing sentences. Since concepts in a text are not named every time by the same name, Textstorm uses synonymy relationship from WordNet to find concepts previously referred with a different name. In Textstorm, relations are identified as verbs in a sentence, with the subject as the first concepts and the object (verbal phrase) as the second concept in the predicate (e.g., in a sen-tence ”Jupiter is a big planet”, Textstorm builds the predicate ”isa(Jupiter, big)”.
The resulting predicates produced act as inputs to Clouds [PC00], a system that, through interaction with the user, builds a complete concept map.