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

Žubrinić, Krunoslav; Kalpić, Damir; Miličević, Mario

doi:10.1016/j.eswa.2012.04.065

Cited by 37 publications

(22 citation statements)

References 39 publications

(76 reference statements)

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“…La problemática de la extracción de información en RMC se ha enfocado en la extracción de conceptos [7], proposiciones [5] o MC [8,9], mediante procesos de búsquedas ejecutados a partir consultas definidas sobre uno o varios conceptos. En [10] se explora la viabilidad del análisis de MC a partir de la extracción de conceptos y submapas frecuentes en un RMC; reportándose algo similar en [4].…”

Section: Trabajos Relacionadosunclassified

“…En este sentido, mejorar los mecanismos de consulta sobre este tipo de base de conocimiento es una de las metas a alcanzar para lograr ese propósito. La mayoría de las propuestas actuales que reportan mecanismos de consultas sobre RMC se centran en obtener conceptos, proposiciones, MC o estructuras proposicionales frecuentes [4,5,7,8,9,10]. Por otra parte, en [2] se reporta CMQL (Concept Maps Query Language) como una propuesta más abarcadora para consultar un RMC.…”

Section: Introductionunclassified

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Método de análisis semántico basado en WordNet para la extracción de información en mapas conceptuales

Mazo¹,

Simón-Cuevas²,

Campos³

2016

RCS

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Resumen. El mapa conceptual es un tipo de representación de conocimiento basada en grafo, donde el conocimiento se expresa en lenguaje natural a través de conceptos y relaciones entre ellos. Este se puede construir manualmente o automáticamente a partir de un texto, propiciando la obtención de repositorios de conocimiento de gran valor para la gestión de conocimiento y el análisis de textos. En este trabajo, se propone un método de análisis semántico a ser aplicado en el procesamiento de consultas sobre un repositorio de mapas conceptuales. El método se basa en la extensión semántica de conceptos usando WordNet y un algoritmo de desambiguación, e incluye reglas que guían los procesos de búsqueda e integración de información. Mediante el desarrollo de un caso de estudio en el ámbito de la ingeniería ontológica se ejemplifica la ejecución del método y se muestran sus beneficios para extraer información en este tipo de repositorios.

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Section: Trabajos Relacionadosunclassified

Section: Introductionunclassified

Método de análisis semántico basado en WordNet para la extracción de información en mapas conceptuales

Mazo¹,

Simón-Cuevas²,

Campos³

2016

RCS

View full text Add to dashboard Cite

show abstract

“…Manual CM construction is a complex task. An unskilful learner has difficulty in positioning concepts, a time‐consuming and costly activity, for CM organisation (Chen et al , ; Tseng et al , ; Zubrinic, Kalpic & Milicevic, ). Generally, CM construction relies on domain experts (Chen et al , ; Lee, Lee & Leu, ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Chen and Sue () overcame the drawbacks of Lee et al () and Chen and Bai () through data‐mining techniques by considering more conditions. In addition, previous studies have focused on automatic CM construction from text (Chen et al , ; Qasim, Jeong, Heu & Lee, ; Zubrinic et al , ).…”

Section: Literature Reviewmentioning

confidence: 99%

Effects of concept map extraction and a test‐based diagnostic environment on learning achievement and learners' perceptions

Lin¹,

Chang

Liew

et al. 2015

Brit J Educational Tech

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Computerised testing and diagnostics are critical challenges within an e-learning environment, where the learners can assess their learning performance through tests. However, a test result based on only a single score is insufficient information to provide a full picture of learning performance. In addition, because test results implicitly include information about the underlying subject concepts and their relationships to each other, this paper proposes a more effective method for analysing test results by providing a concept map (CM) to facilitate learners' understanding of their learning performance. An innovative approach, not explored in previous studies, is proposed to automatically construct a personalised CM. A CM-smart extraction and explicit diagnosis (CM-SEED) learning system has been developed to diagnose learning barriers and misconceptions and to supply relevant suggestions and guidance for remedial learning. This study examined 90 students from two classes at a university and assigned one class to be the experimental group and another class to be the control group. The results indicated that the students who used the CM-SEED learning system had superior perceptions regarding their learning; furthermore, they accomplished superior learning achievement that displayed statistical significance. Consequently, the study concluded that CM extraction in a test-based diagnostic environment can lead learners to enhanced learning performance.

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“…Bai and Chen () improved the study of Lee et al by including questions that were answered incorrectly in the process of constructing the association rules for the concept maps. Zubrinic, Kalpic, and Milicevic () promoted a method for generating a concept map from an unstructured Croatian text. This method was based on summarization and tf–idf indices.…”

Section: Introductionmentioning

confidence: 99%

An empirical study on evolutionary feature selection in intelligent tutors for learning concept detection

et al. 2018

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Concept map mining (CMM) has emerged as a new research area with recent developments in computational intelligence in educational technology. CMM includes the following steps: extracting the learning concepts from educational content, specifying relations among them, and generating a concept map as a result. The purpose of this study was to develop a mechanism using data mining technique to determine the features that characterize a learning concept extracted automatically from a single educational text. The 3 major features that distinguish the real learning concepts from other sequences of strings are detected by using a hybrid system of a feed‐forward neural network and some evolutionary algorithms. Ant colony optimization and genetic algorithm and particle swarm optimization are used as a binary feature selection method. In addition, the aforementioned methods are hybridized to get better accuracy and precision. The performance comparisons with two different state‐of‐the‐art algorithms have been made from the viewpoint of a typical classification problem.

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

Cited by 37 publications

References 39 publications

Método de análisis semántico basado en WordNet para la extracción de información en mapas conceptuales

Método de análisis semántico basado en WordNet para la extracción de información en mapas conceptuales

Effects of concept map extraction and a test‐based diagnostic environment on learning achievement and learners' perceptions

An empirical study on evolutionary feature selection in intelligent tutors for learning concept detection

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