scholarly journals Towards Technological Approaches for Concept Maps Mining from Text

2018 ◽  
Vol 21 (1) ◽  
Author(s):  
Camila Zacche Aguiar ◽  
Davidson Cury ◽  
Amal Zouaq
Keyword(s):  
Quantitative Analysis ◽  
Concept Maps ◽  
Concept Map ◽  
Graphic Representation ◽  
Data Bases ◽  
Automatic Construction ◽  
Construction Of Knowledge ◽  
Technological Advances ◽  
Direct Data ◽  
Data Source

Concept maps are resources for the representation and construction of knowledge. They allow showing, through concepts and relationships, how knowledge about a subject is organized. Technological advances have boosted the development of approaches for the automatic construction of a concept map, to facilitate and provide the benefits of that resource more broadly. Due to the need to better identify and analyze the functionalities and characteristics of those approaches, we conducted a detailed study on technological approaches for automatic construction of concept maps published between 1994 and 2016 in the IEEE Xplore, ACM and Elsevier Science Direct data bases. From this study, we elaborate a categorization defined on two perspectives, Data Source and Graphic Representation, and fourteen categories. That study collected 30 relevant articles, which were applied to the proposed categorization to identify the main features and limitations of each approach. A detailed view on these approaches, their characteristics and techniques are presented enabling a quantitative analysis. In addition, the categorization has given us objective conditions to establish new specification requirements for a new technological approach aiming at concept maps mining from texts.

Factors Influencing Individual Construction of Knowledge in an Online Community of Learning and Inquiry Using Concept Maps

2010 ◽  
pp. 100-119 ◽  
Author(s):  
Simone C.O. Conceição ◽  
Maria Julia Baldor ◽  
Carrie Ann Desnoyers
Keyword(s):  
Social Presence ◽  
Online Community ◽  
Concept Maps ◽  
Concept Map ◽  
Cognitive Presence ◽  
Construction Of Knowledge ◽  
Community Of Learning ◽  
Collaborative Community ◽  
Knowledge Group ◽  
The Individual

This chapter describes a study that used the community of learning and inquiry and concept maps as strategies to facilitate individual construction of knowledge in an asynchronous online course. Six factors influenced the concept map creation, which in turn affected individual construction of knowledge: group characteristics, social presence, cognitive presence, facilitation style of student, discussion summary format, and teacher presence. Working in a collaborative community allowed students to explore different ideas and concepts, but it was through the individual concept map work that students refined and expanded their knowledge and constructed personal meaning. The chapter concludes with strategies to facilitate individual learning in a collaborative online environment.

scholarly journals Minerando Mapa Conceitual a partir de Texto em Português

2019 ◽  
Vol 27 (01) ◽  
pp. 83
Author(s):  
Camila Zacche de Aguiar ◽  
Davidson Cury ◽  
Amal Zouaq
Keyword(s):  
Language Processing ◽  
Concept Maps ◽  
Concept Map ◽  
Cognitive Structure ◽  
Cognitive Effort ◽  
Construction Of Knowledge ◽  
Domain Identification ◽  
Conceptual Maps ◽  
Computational Resources ◽  
Processing Techniques

Concept maps are graphical tools for representation and construction of knowledge. The manual construction of a concept map requires time and cognitive effort, this being increased when the map should not represent the cognitive structure of the author, but rather, the information expressed in a text written by another author. Therefore, we propose a computational approach for concept map mining from texts in Portuguese that aims to represent the text in summary form through concepts and relationships. To this end, we define a technological architecture that includes the services of: (i) text formatting, removing characters and designing of the text; (ii) domain identification, information retrieval techniques to identify the domain to which refers the text; (iii) elements extractor, natural language processing techniques on the text to extract concept-relation-concept propositions; (iv) element summarizer, supported by graph analysis to identify the relevant concepts on the map; and (v) map visualization, presentation of the propositions in graphic form. The approach developed presents satisfactory results and contributes exceptionally to the summarization of texts to identify the relevant concepts of the text while maintaining its several and most important characteristics. Furthermore, this research introduces the specification of a project to provide computational resources for processing, handling and extraction of conceptual maps.

Mapping the Topics and Evolutions of Chinese Children’s Bestsellers

Libri ◽  
2020 ◽  
Vol 70 (4) ◽  
pp. 305-317
Author(s):  
Jiming Hu ◽  
Xiang Zheng ◽  
Peng Wen ◽  
Jie Xu
Keyword(s):  
Social Network ◽  
Quantitative Analysis ◽  
Network Analysis ◽  
Intellectual Structure ◽  
Structure Evolution ◽  
Children's Books ◽  
Children’S Books ◽  
Correlation Structures ◽  
The World ◽  
Data Source

AbstractChildren’s books involve a large number of topics. Research on them has been paid much attention to by both scholars and practitioners. However, the existing achievements do not focus on China, which is the fastest growing market for children’s books in the world. Studies using quantitative analysis are low in number, especially on the intellectual structure, evolution patterns, and development trends of topics of children’s bestsellers in China. Dangdang.com, the biggest Chinese online bookstore, was chosen as a data source to obtain children’s bestsellers, and topic words in them were extracted from brief introductions. With the aid of co-occurrence theory and tools of social network analysis and visualization, the distribution, correlation structures, and evolution patterns of topics were revealed and visualized. This study shows that topics of Chinese children’s bestsellers are broad and relatively concentrated, but their distribution is unbalanced. There are four distinguished topic communities (Living, Animal, World, and Child) in terms of centrality and maturity, and they all establish their individual systems and tend to be mature. The evolution of these communities tends to be stable with powerful continuity.

scholarly journals A robust machine learning approach to SDG data segmentation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Kassim S. Mwitondi ◽  
Isaac Munyakazi ◽  
Barnabas N. Gatsheni
Keyword(s):  
Temporal Variations ◽  
Data Repository ◽  
The Sustainable Development ◽  
Complex Interactions ◽  
Data Segmentation ◽  
Technological Advances ◽  
Machine Learning Approach ◽  
Development Goals ◽  
Data Source ◽  
Spatio Temporal

Abstract In the light of the recent technological advances in computing and data explosion, the complex interactions of the Sustainable Development Goals (SDG) present both a challenge and an opportunity to researchers and decision makers across fields and sectors. The deep and wide socio-economic, cultural and technological variations across the globe entail a unified understanding of the SDG project. The complexity of SDGs interactions and the dynamics through their indicators align naturally to technical and application specifics that require interdisciplinary solutions. We present a consilient approach to expounding triggers of SDG indicators. Illustrated through data segmentation, it is designed to unify our understanding of the complex overlap of the SDGs by utilising data from different sources. The paper treats each SDG as a Big Data source node, with the potential to contribute towards a unified understanding of applications across the SDG spectrum. Data for five SDGs was extracted from the United Nations SDG indicators data repository and used to model spatio-temporal variations in search of robust and consilient scientific solutions. Based on a number of pre-determined assumptions on socio-economic and geo-political variations, the data is subjected to sequential analyses, exploring distributional behaviour, component extraction and clustering. All three methods exhibit pronounced variations across samples, with initial distributional and data segmentation patterns isolating South Africa from the remaining five countries. Data randomness is dealt with via a specially developed algorithm for sampling, measuring and assessing, based on repeated samples of different sizes. Results exhibit consistent variations across samples, based on socio-economic, cultural and geo-political variations entailing a unified understanding, across disciplines and sectors. The findings highlight novel paths towards attaining informative patterns for a unified understanding of the triggers of SDG indicators and open new paths to interdisciplinary research.

Evaluation of Semantic Fisheye Zooming to Provide Focus+Context

2007 ◽  
Vol 51 (5) ◽  
pp. 459-463
Author(s):  
Andrew J. Afram ◽  
John Briedis ◽  
Daisuke Fujiwara ◽  
Robert J.K. Jacob ◽  
Caroline G.L. Cao ◽  
...  
Keyword(s):  
Concept Maps ◽  
User Study ◽  
Concept Map ◽  
Digital Display ◽  
Individual Concepts ◽  
Semantic Zooming ◽  
Zoom In

A concept map is a diagram that consists of nodes that contain individual concepts or pieces of information. These nodes are connected by lines that represent relationships between the information. Large concept maps are difficult to explore and navigate using current digital display interfaces. As users zoom in on a desired node, connections between the node of interest and surrounding nodes become hidden from the user. A combination of fisheye zooming and semantic zooming mechanisms to maintain the visual connections between the nodes was implemented, and a user study to determine whether this technique helps users learn from the map was conducted. The user study revealed that participants were able to recall more information presented in a concept map, with practically no difference in the amount of time spent using the map, despite the novelty of the semantic fisheye interface.

Enhancing students' HOTS in laboratory educational activity by using concept map as an alternative assessment tool

2017 ◽  
Vol 18 (4) ◽  
pp. 849-874 ◽  
Author(s):  
I. B. A. Ghani ◽  
N. H. Ibrahim ◽  
N. A. Yahaya ◽  
J. Surif
Keyword(s):  
Alternative Assessment ◽  
Concept Maps ◽  
Assessment Tool ◽  
Concept Map ◽  
Thinking Skills ◽  
Learning Activities ◽  
Educational Activity ◽  
Chemistry Laboratory ◽  
Mapping Technique ◽  
Laboratory Learning

Educational transformation in the 21st century demands in-depth knowledge and understanding in order to promote the development of higher-order thinking skills (HOTS). However, the most commonly reported problem with respect to developing a knowledge of chemistry is poor mastery of basic concepts. Chemistry laboratory educational activities are shown to be less effective in developing an optimum conceptual understanding and HOTS among students. One factor is a lack of effective assessment and evaluation tools. Therefore, the primary focus of this study is to explore concept maps as an assessment tool in order to move students' thinking skills to a higher level during laboratory learning activities. An embedded mixed method design is used in this study, which has also employed a pre-experimental research design. This design triangulates quantitative and qualitative data, which are combined to strengthen the findings. A low-directed concept mapping technique, convergence scoring method, and pre-post laboratory concept map were used in this study. An electrolysis HOTS test was used as the research instrument in order to measure the level of student achievement with respect to high-level questions. In addition, the thought process that is involved when students construct concept maps has been explored and studied in detail by utilising a think-aloud protocol. Results showed a positive development towards understanding and higher level thinking skills in students with respect to electrolysis concepts learned through chemistry laboratory activities. An investigation of the students' thinking processes showed that high-achieving students were more capable of giving a content-based explanation of electrolysis and engaged in monitoring activities more often while building a concept map. Nonetheless, all categories of students managed to show a positive increase in the activities of explanation and monitoring during the construction of concept maps after they were exposed to the assessment tool in the laboratory learning activities. In conclusion, the assessment activity using concept maps in laboratory learning activities has a positive impact on students' understanding and stimulates students to increase their HOTS.

scholarly journals КОНЦЕПТ-КАРТА ЯК ДІЄВИЙ ЗАСІБ НАВЧАННЯ ІНОЗЕМНОЇ МОВИ

2020 ◽  
Vol 15 ◽  
pp. 22-25
Author(s):  
Nataliia Borysova
Keyword(s):  
Foreign Language ◽  
Teaching And Learning ◽  
Concept Maps ◽  
Concept Map ◽  
Hierarchical Organization ◽  
Conceptual Mapping ◽  
Approximate Description ◽  
The Difference ◽  
Mind Maps

The article reveals the concept of conceptual mapping in the process of learning a foreign language. It is stated that a concept map is a diagram that shows the relationships between notions. Such maps are graphical tools for organizing and presenting knowledge. It is emphasized that the most useful form of a concept map for teaching and learning is one that is placed in a hierarchical organization, where more general and comprehensive notions are at the top of the map and more specific at the bottom. The difference between concert cards and mind maps is given. It is emphasized that despite a similarity of mind maps and concept maps, these two methods differ in many respects, in particular, concept maps are characterized by clear links between the described ideas and are more structured than mind maps, as a formally approximate description, which places ideas in some sequence and organizes them hierarchically by levels of importance.

Low-Quality Error Detection for Noisy Knowledge Graphs

2021 ◽  
Vol 32 (4) ◽  
pp. 48-64
Author(s):  
*Chenyang Bu ◽  
Xingchen Yu ◽  
Yan Hong ◽  
Tingting Jiang
Keyword(s):  
Error Detection ◽  
Data Sources ◽  
Construction Process ◽  
Automatic Construction ◽  
Construction Of Knowledge ◽  
Unstructured Text ◽  
Multiple Data ◽  
Extraction Processes ◽  
Knowledge Graphs ◽  
Type Information

The automatic construction of knowledge graphs (KGs) from multiple data sources has received increasing attention. The automatic construction process inevitably brings considerable noise, especially in the construction of KGs from unstructured text. The noise in a KG can be divided into two categories: factual noise and low-quality noise. Factual noise refers to plausible triples that meet the requirements of ontology constraints. For example, the plausible triple <New_York, IsCapitalOf, America> satisfies the constraints that the head entity “New_York” is a city and the tail entity “America” belongs to a country. Low-quality noise denotes the obvious errors commonly created in information extraction processes. This study focuses on entity type errors. Most existing approaches concentrate on refining an existing KG, assuming that the type information of most entities or the ontology information in the KG is known in advance. However, such methods may not be suitable at the start of a KG's construction. Therefore, the authors propose an effective framework to eliminate entity type errors. The experimental results demonstrate the effectiveness of the proposed method.

Exploring Semiotic Approaches to Analysing Multidimensional Concept Maps Using Methods that Value Collaboration

2010 ◽  
pp. 256-282 ◽  
Author(s):  
Christina J. Preston
Keyword(s):  
Professional Learning ◽  
Concept Maps ◽  
Concept Map ◽  
Assessment Process ◽  
Multimodal Literacy ◽  
Equal Importance ◽  
E Learning ◽  
Collaborative Engagement ◽  
One Year ◽  
Multidimensional Concept

This chapter focuses on teachers’ multidimensional concept mapping data collected at the beginning and end of a one-year Masters level course about e-learning. A multidimensional concept map (MDCM) defines any concept map that is multimodal, multimedia, multilayered and/or multi-authored. The teachers’ personal and professional learning priorities are analysed using two semiotic methods: the first is a traditional analysis of the words used to label the nodes; the second is an innovative analysis method that treats the whole map as a semiotic artefact, in which all the elements, including the words, have equal importance. The findings suggest that these tools offer deep insights into the learning priorities of individuals and groups, especially the affective and motivational factors. The teachers, as co-researchers, also adopted MDCM to underpin collaborative thinking. These research tools can be used in the assessment process to value multimodal literacy and collaborative engagement in new knowledge construction.

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