The automatic creation of concept maps from documents written using morphologically rich languages

TL;DR

This paper discusses methods for automatically creating concept maps from texts, focusing on highly inflected languages like Croatian, using statistical, data mining, and linguistic tools to assist knowledge visualization.

Contribution

It introduces a method tailored for morphologically rich languages, combining statistical, data mining, and linguistic techniques for concept map creation from unstructured texts.

Findings

Method effectively creates concept maps from Croatian texts.

Approach can be adapted to other morphologically rich languages.

Enhances automatic knowledge visualization from unstructured data.

Abstract

Concept map is a graphical tool for representing knowledge. They have been used in many different areas, including education, knowledge management, business and intelligence. Constructing of concept maps manually can be a complex task; an unskilled person may encounter difficulties in determining and positioning concepts relevant to the problem area. An application that recommends concept candidates and their position in a concept map can significantly help the user in that situation. This paper gives an overview of different approaches to automatic and semi-automatic creation of concept maps from textual and non-textual sources. The concept map mining process is defined, and one method suitable for the creation of concept maps from unstructured textual sources in highly inflected languages such as the Croatian language is described in detail. Proposed method uses statistical and data mining techniques enriched with linguistic tools. With minor adjustments, that method can also be used for concept map mining from textual sources in other morphologically rich languages.