Development and Evaluation of Concept Maps as Viable Educational Technology t...paperpublications3
Â
Abstract: This study had developed and evaluated concept maps as viable educational technology to facilitate learning and assessment. The development process concluded upon establishing validity and reliability. These maps were classified into two: concept maps to facilitate learning; and, fill-in-the-maps to facilitate assessment. A one group pre-test-posttest pre-experimental design was employed. Fill-in-the-maps were utilized for unit pre-tests and posttests. Complete concept maps were used to facilitate learning. For midterm examination, students were given composition as basis for constructing concept map. For final examination, students were provided concept maps to write their own composition. Rubrics were used to assess studentsâ outputs. z-test for correlated means showed significant increases of Mean Percentage Score (MPS) from pre-test to posttest. The overall posttest result was correlated with those of objective, fill-in-the-map, map construction and composition writing. Significant correlations were observed. Results accentuated that concept maps can be developed and evaluated to facilitate learning and assessment.
This article is a continuation of our researches on the
competency-based approach (CBA). It presents the ways that can
facilitate and generalize the understanding of CBA, its adoption
and its implementation in the educational system of Morocco.
The work described in this paper aims of the final stages of an
ontologyâs development, when consensus is reached. More
precisely, the stage of operationalization: the process that allows
the transforming from the conceptual representation of
knowledge in an ontology regardless of use, to one operational
representation appropriate to its use. This article gives an
overview of the constraints that characterize this stage and
opportunities that can be offered by the ontologyâs
implementation. It outlines a functional draft of a learning
platform architecture based on CBA, in order to guide the
choices made in the operationalization phase of CBA ontology.
The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract conceptual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowledge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is
handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particular, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a number that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
Â
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the conceptâs names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
Visual representation and organization of the knowledge have been utilized in different ways in tutoring
systems to upgrade their usefulness. This paper concentrates on the usage of various graphical formalisms,
for example, the conceptual graph, ontology, and concept map in tutoring systems. The paper addresses
what is way of the utilization of every formalism and the offering of the potential outcomes to assist the
student in education systems.
GRAPHICAL REPRESENTATION IN TUTORING SYSTEMSijcsit
Â
Visual representation and organization of the knowledge have been utilized in different ways in tutoring systems to upgrade their usefulness. This paper concentrates on the usage of various graphical formalisms, for example, the conceptual graph, ontology, and concept map in tutoring systems. The paper addresses what is way of the utilization of every formalism and the offering of the potential outcomes to assist the student in education systems.
Visual representation and organization of the knowledge have been utilized in different ways in tutoring systems to upgrade their usefulness. This paper concentrates on the usage of various graphical formalisms, for example, the conceptual graph, ontology, and concept map in tutoring systems. The paper addresses what is way of the utilization of every formalism and the offering of the potential outcomes to assist the student in education systems.
Development and Evaluation of Concept Maps as Viable Educational Technology t...paperpublications3
Â
Abstract: This study had developed and evaluated concept maps as viable educational technology to facilitate learning and assessment. The development process concluded upon establishing validity and reliability. These maps were classified into two: concept maps to facilitate learning; and, fill-in-the-maps to facilitate assessment. A one group pre-test-posttest pre-experimental design was employed. Fill-in-the-maps were utilized for unit pre-tests and posttests. Complete concept maps were used to facilitate learning. For midterm examination, students were given composition as basis for constructing concept map. For final examination, students were provided concept maps to write their own composition. Rubrics were used to assess studentsâ outputs. z-test for correlated means showed significant increases of Mean Percentage Score (MPS) from pre-test to posttest. The overall posttest result was correlated with those of objective, fill-in-the-map, map construction and composition writing. Significant correlations were observed. Results accentuated that concept maps can be developed and evaluated to facilitate learning and assessment.
This article is a continuation of our researches on the
competency-based approach (CBA). It presents the ways that can
facilitate and generalize the understanding of CBA, its adoption
and its implementation in the educational system of Morocco.
The work described in this paper aims of the final stages of an
ontologyâs development, when consensus is reached. More
precisely, the stage of operationalization: the process that allows
the transforming from the conceptual representation of
knowledge in an ontology regardless of use, to one operational
representation appropriate to its use. This article gives an
overview of the constraints that characterize this stage and
opportunities that can be offered by the ontologyâs
implementation. It outlines a functional draft of a learning
platform architecture based on CBA, in order to guide the
choices made in the operationalization phase of CBA ontology.
The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract conceptual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowledge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is
handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particular, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a number that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
Â
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the conceptâs names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
Visual representation and organization of the knowledge have been utilized in different ways in tutoring
systems to upgrade their usefulness. This paper concentrates on the usage of various graphical formalisms,
for example, the conceptual graph, ontology, and concept map in tutoring systems. The paper addresses
what is way of the utilization of every formalism and the offering of the potential outcomes to assist the
student in education systems.
GRAPHICAL REPRESENTATION IN TUTORING SYSTEMSijcsit
Â
Visual representation and organization of the knowledge have been utilized in different ways in tutoring systems to upgrade their usefulness. This paper concentrates on the usage of various graphical formalisms, for example, the conceptual graph, ontology, and concept map in tutoring systems. The paper addresses what is way of the utilization of every formalism and the offering of the potential outcomes to assist the student in education systems.
Visual representation and organization of the knowledge have been utilized in different ways in tutoring systems to upgrade their usefulness. This paper concentrates on the usage of various graphical formalisms, for example, the conceptual graph, ontology, and concept map in tutoring systems. The paper addresses what is way of the utilization of every formalism and the offering of the potential outcomes to assist the student in education systems.
Knowledge maps for e-learning. Jae Hwa Lee, Aviv Segev
Maps such as concept maps and knowledge maps are often used as learning materials. These maps havenodes and links, nodes as key concepts and links as relationships between key concepts. From a map, theuser can recognize the important concepts and the relationships between them. To build concept orknowledge maps, domain experts are needed. Therefore, since these experts are hard to obtain, the costof map creation is high. In this study, an attempt was made to automatically build a domain knowledgemap for e-learning using text mining techniques. From a set of documents about a specific topic,keywords are extracted using the TF/IDF algorithm. A domain knowledge map (K-map) is based onranking pairs of keywords according to the number of appearances in a sentence and the number ofwords in a sentence. The experiments analyzed the number of relations required to identify theimportant ideas in the text. In addition, the experiments compared K-map learning to document learningand found that K-map identifies the more important ideas
A hybrid composite features based sentence level sentiment analyzerIAESIJAI
Â
Current lexica and machine learning based sentiment analysis approaches
still suffer from a two-fold limitation. First, manual lexicon construction and
machine training is time consuming and error-prone. Second, the
predictionâs accuracy entails sentences and their corresponding training text
should fall under the same domain. In this article, we experimentally
evaluate four sentiment classifiers, namely support vector machines (SVMs),
Naive Bayes (NB), logistic regression (LR) and random forest (RF). We
quantify the quality of each of these models using three real-world datasets
that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic
movie reviews. Specifically, we study the impact of a variety of natural
language processing (NLP) pipelines on the quality of the predicted
sentiment orientations. Additionally, we measure the impact of incorporating
lexical semantic knowledge captured by WordNet on expanding original
words in sentences. Findings demonstrate that the utilizing different NLP
pipelines and semantic relationships impacts the quality of the sentiment
analyzers. In particular, results indicate that coupling lemmatization and
knowledge-based n-gram features proved to produce higher accuracy results.
With this coupling, the accuracy of the SVM classifier has improved to
90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three
other classifiers.
Cmaps as intellectual prosthesis (GERAS 34, Paris)Lawrie Hunter
Â
At the present time, 'increasing accessibility of technology' is readily read as 'increasing accessibility of electronic information technology', but this is to ignore a history of pre-electronic technologies which have generally been conflated with the original media of education, first speech and rather later the writing of continuous text.
The insertion of spaces between words in text was a technology for accessibility of encoding. The paragraph was a technology for the signaling of rhetorical shifts. The bullet list is used for the representation of clusters of notions, either atomic (listing) or aggregates (classification). More substantial technological innovations include the data table and the graph.
One revolutionary technology that has not become mainstream in instructional communication is the Novakian concept map (i.e. the map whose links have text labels to specify the relation between two nodes). This technology has been substantially migrated to electronic information technology, and is arguably more prevalent there than in the traditional sphere, though it is still largely regarded as a novelty or non-essential element of instructional discourse.
This paper reports a case study of a fruitful application of Novakian mapping, wherein EAP learners of academic writing for management discover intellectual leverage in mapping, and develop their own use of the technique, in an iterative manner, in counterpoint with text analysis work. It tracks the cycling between moves analysis and concept mapping as these members of a graduate seminar work to unpack a paper that they have identified as a 'good model', but which they have realized is not a well-written paper.
The observations made here suggest that concept mapping is a pre-electronic technology that deserves a place amongst the essential tools for instructional discourse, particularly in settings such as EAP where the identification of rhetorical orchestration is difficult and where argument is often masked by other rhetorical devices.
ConNeKTion: A Tool for Exploiting Conceptual Graphs Automatically Learned fro...University of Bari (Italy)
Â
Studying, understanding and exploiting the content of a digital library, and extracting useful information thereof, require automatic techniques that can effectively support the users. To this aim, a relevant role can be played by concept taxonomies. Unfortunately, the availability of such a kind of resources is limited, and their manual building and maintenance are costly and error-prone. This work presents ConNeKTion, a tool for conceptual graph learning and exploitation. It allows to learn conceptual graphs from plain text and to enrich them by finding concept generalizations. The resulting graph can be used for several purposes: finding relationships between concepts (if any), filtering the concepts from a particular perspective, keyword extraction and information retrieval. A suitable control panel is provided for the user to comfortably carry out these activities.
Association Rule Mining Based Extraction of Semantic Relations Using Markov ...dannyijwest
Â
Ontology may be a conceptualization of a website into a human understandable, however machine-
readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the
intentional aspects of a site, whereas the denotative part is provided by a mental object that contains
assertions about instances of concepts and relations. Semantic relation it might be potential to extract the
whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations
describe the linguistics relationships among the entities involve that is beneficial for a higher
understanding of human language. The relation can be identified from the result of concept hierarchy
extraction. The existing ontology learning process only produces the result of concept hierarchy extraction.
It does not produce the semantic relation between the concepts. Here, we have to do the process of
constructing the predicates and also first order logic formula. Here, also find the inference and learning
weights using Markov Logic Network. To improve the relation of every input and also improve the relation
between the contents we have to propose the concept of ARSRE.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
Â
Ontology may be a conceptualization of a website into a human understandable, however machine-readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
Concept mapping and text analysis (WRAB3 poster)Lawrie Hunter
Â
A low text representation of the content of text can reveal rhetorical structure or orchestration (or their absence). Cmap representation can have a valuable place in the writing center toolkit.
CLASSIFICATION OF QUESTIONS AND LEARNING OUTCOME STATEMENTS (LOS) INTO BLOOMâ...IJMIT JOURNAL
Â
Bloomâs Taxonomy (BT) have been used to classify the objectives of learning outcome by dividing the
learning into three different domains; the cognitive domain, the effective domain and the psychomotor
domain. In this paper, we are introducing a new approach to classify the questions and learning outcome
statements (LOS) into Blooms taxonomy (BT) and to verify BT verb lists, which are being cited and used by
academicians to write questions and (LOS). An experiment was designed to investigate the semantic
relationship between the action verbs used in both questions and LOS to obtain more accurate
classification of the levels of BT. A sample of 775 different action verbs collected from different universities
allows us to measure an accurate and clear-cut cognitive level for the action verb. It is worth mentioning
that natural language processing techniques were used to develop our rules as to induce the questions into
chunks in order to extract the action verbs. Our proposed solution was able to classify the action verb into
a precise level of the cognitive domain. We, on our side, have tested and evaluated our proposed solution
using confusion matrix. The results of evaluation tests yielded 97% for the macro average of precision and
90% for F1. Thus, the outcome of the research suggests that it is crucial to analyse and verify the action
verbs cited and used by academicians to write LOS and classify their questions based on blooms taxonomy
in order to obtain a definite and more accurate classification.
CLASSIFICATION OF QUESTIONS AND LEARNING OUTCOME STATEMENTS (LOS) INTO BLOOMâ...IJMIT JOURNAL
Â
Bloomâs Taxonomy (BT) have been used to classify the objectives of learning outcome by dividing the learning into three different domains; the cognitive domain, the effective domain and the psychomotor domain. In this paper, we are introducing a new approach to classify the questions and learning outcome
statements (LOS) into Blooms taxonomy (BT) and to verify BT verb lists, which are being cited and used by academicians to write questions and (LOS). An experiment was designed to investigate the semantic relationship between the action verbs used in both questions and LOS to obtain more accurate
classification of the levels of BT. A sample of 775 different action verbs collected from different universities allows us to measure an accurate and clear-cut cognitive level for the action verb. It is worth mentioning that natural language processing techniques were used to develop our rules as to induce the questions into
chunks in order to extract the action verbs. Our proposed solution was able to classify the action verb into a precise level of the cognitive domain. We, on our side, have tested and evaluated our proposed solution using confusion matrix. The results of evaluation tests yielded 97% for the macro average of precision and 90% for F1. Thus, the outcome of the research suggests that it is crucial to analyse and verify the action
verbs cited and used by academicians to write LOS and classify their questions based on blooms taxonomy in order to obtain a definite and more accurate classification.
French machine reading for question answeringAli Kabbadj
Â
This paper proposes to unlock the main barrier to machine reading and comprehension French natural language texts. This open the way to machine to find to a question a precise answer buried in the mass of unstructured French texts. Or to create a universal French chatbot. Deep learning has produced extremely promising results for various tasks in natural language understanding particularly topic classification, sentiment analysis, question answering, and language translation. But to be effective Deep Learning methods need very large training da-tasets. Until now these technics cannot be actually used for French texts Question Answering (Q&A) applications since there was not a large Q&A training dataset. We produced a large (100 000+) French training Dataset for Q&A by translating and adapting the English SQuAD v1.1 Dataset, a GloVe French word and character embed-ding vectors from Wikipedia French Dump. We trained and evaluated of three different Q&A neural network ar-chitectures in French and carried out a French Q&A models with F1 score around 70%.
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
Â
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
Knowledge maps for e-learning. Jae Hwa Lee, Aviv Segev
Maps such as concept maps and knowledge maps are often used as learning materials. These maps havenodes and links, nodes as key concepts and links as relationships between key concepts. From a map, theuser can recognize the important concepts and the relationships between them. To build concept orknowledge maps, domain experts are needed. Therefore, since these experts are hard to obtain, the costof map creation is high. In this study, an attempt was made to automatically build a domain knowledgemap for e-learning using text mining techniques. From a set of documents about a specific topic,keywords are extracted using the TF/IDF algorithm. A domain knowledge map (K-map) is based onranking pairs of keywords according to the number of appearances in a sentence and the number ofwords in a sentence. The experiments analyzed the number of relations required to identify theimportant ideas in the text. In addition, the experiments compared K-map learning to document learningand found that K-map identifies the more important ideas
A hybrid composite features based sentence level sentiment analyzerIAESIJAI
Â
Current lexica and machine learning based sentiment analysis approaches
still suffer from a two-fold limitation. First, manual lexicon construction and
machine training is time consuming and error-prone. Second, the
predictionâs accuracy entails sentences and their corresponding training text
should fall under the same domain. In this article, we experimentally
evaluate four sentiment classifiers, namely support vector machines (SVMs),
Naive Bayes (NB), logistic regression (LR) and random forest (RF). We
quantify the quality of each of these models using three real-world datasets
that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic
movie reviews. Specifically, we study the impact of a variety of natural
language processing (NLP) pipelines on the quality of the predicted
sentiment orientations. Additionally, we measure the impact of incorporating
lexical semantic knowledge captured by WordNet on expanding original
words in sentences. Findings demonstrate that the utilizing different NLP
pipelines and semantic relationships impacts the quality of the sentiment
analyzers. In particular, results indicate that coupling lemmatization and
knowledge-based n-gram features proved to produce higher accuracy results.
With this coupling, the accuracy of the SVM classifier has improved to
90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three
other classifiers.
Cmaps as intellectual prosthesis (GERAS 34, Paris)Lawrie Hunter
Â
At the present time, 'increasing accessibility of technology' is readily read as 'increasing accessibility of electronic information technology', but this is to ignore a history of pre-electronic technologies which have generally been conflated with the original media of education, first speech and rather later the writing of continuous text.
The insertion of spaces between words in text was a technology for accessibility of encoding. The paragraph was a technology for the signaling of rhetorical shifts. The bullet list is used for the representation of clusters of notions, either atomic (listing) or aggregates (classification). More substantial technological innovations include the data table and the graph.
One revolutionary technology that has not become mainstream in instructional communication is the Novakian concept map (i.e. the map whose links have text labels to specify the relation between two nodes). This technology has been substantially migrated to electronic information technology, and is arguably more prevalent there than in the traditional sphere, though it is still largely regarded as a novelty or non-essential element of instructional discourse.
This paper reports a case study of a fruitful application of Novakian mapping, wherein EAP learners of academic writing for management discover intellectual leverage in mapping, and develop their own use of the technique, in an iterative manner, in counterpoint with text analysis work. It tracks the cycling between moves analysis and concept mapping as these members of a graduate seminar work to unpack a paper that they have identified as a 'good model', but which they have realized is not a well-written paper.
The observations made here suggest that concept mapping is a pre-electronic technology that deserves a place amongst the essential tools for instructional discourse, particularly in settings such as EAP where the identification of rhetorical orchestration is difficult and where argument is often masked by other rhetorical devices.
ConNeKTion: A Tool for Exploiting Conceptual Graphs Automatically Learned fro...University of Bari (Italy)
Â
Studying, understanding and exploiting the content of a digital library, and extracting useful information thereof, require automatic techniques that can effectively support the users. To this aim, a relevant role can be played by concept taxonomies. Unfortunately, the availability of such a kind of resources is limited, and their manual building and maintenance are costly and error-prone. This work presents ConNeKTion, a tool for conceptual graph learning and exploitation. It allows to learn conceptual graphs from plain text and to enrich them by finding concept generalizations. The resulting graph can be used for several purposes: finding relationships between concepts (if any), filtering the concepts from a particular perspective, keyword extraction and information retrieval. A suitable control panel is provided for the user to comfortably carry out these activities.
Association Rule Mining Based Extraction of Semantic Relations Using Markov ...dannyijwest
Â
Ontology may be a conceptualization of a website into a human understandable, however machine-
readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the
intentional aspects of a site, whereas the denotative part is provided by a mental object that contains
assertions about instances of concepts and relations. Semantic relation it might be potential to extract the
whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations
describe the linguistics relationships among the entities involve that is beneficial for a higher
understanding of human language. The relation can be identified from the result of concept hierarchy
extraction. The existing ontology learning process only produces the result of concept hierarchy extraction.
It does not produce the semantic relation between the concepts. Here, we have to do the process of
constructing the predicates and also first order logic formula. Here, also find the inference and learning
weights using Markov Logic Network. To improve the relation of every input and also improve the relation
between the contents we have to propose the concept of ARSRE.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
Â
Ontology may be a conceptualization of a website into a human understandable, however machine-readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
Concept mapping and text analysis (WRAB3 poster)Lawrie Hunter
Â
A low text representation of the content of text can reveal rhetorical structure or orchestration (or their absence). Cmap representation can have a valuable place in the writing center toolkit.
CLASSIFICATION OF QUESTIONS AND LEARNING OUTCOME STATEMENTS (LOS) INTO BLOOMâ...IJMIT JOURNAL
Â
Bloomâs Taxonomy (BT) have been used to classify the objectives of learning outcome by dividing the
learning into three different domains; the cognitive domain, the effective domain and the psychomotor
domain. In this paper, we are introducing a new approach to classify the questions and learning outcome
statements (LOS) into Blooms taxonomy (BT) and to verify BT verb lists, which are being cited and used by
academicians to write questions and (LOS). An experiment was designed to investigate the semantic
relationship between the action verbs used in both questions and LOS to obtain more accurate
classification of the levels of BT. A sample of 775 different action verbs collected from different universities
allows us to measure an accurate and clear-cut cognitive level for the action verb. It is worth mentioning
that natural language processing techniques were used to develop our rules as to induce the questions into
chunks in order to extract the action verbs. Our proposed solution was able to classify the action verb into
a precise level of the cognitive domain. We, on our side, have tested and evaluated our proposed solution
using confusion matrix. The results of evaluation tests yielded 97% for the macro average of precision and
90% for F1. Thus, the outcome of the research suggests that it is crucial to analyse and verify the action
verbs cited and used by academicians to write LOS and classify their questions based on blooms taxonomy
in order to obtain a definite and more accurate classification.
CLASSIFICATION OF QUESTIONS AND LEARNING OUTCOME STATEMENTS (LOS) INTO BLOOMâ...IJMIT JOURNAL
Â
Bloomâs Taxonomy (BT) have been used to classify the objectives of learning outcome by dividing the learning into three different domains; the cognitive domain, the effective domain and the psychomotor domain. In this paper, we are introducing a new approach to classify the questions and learning outcome
statements (LOS) into Blooms taxonomy (BT) and to verify BT verb lists, which are being cited and used by academicians to write questions and (LOS). An experiment was designed to investigate the semantic relationship between the action verbs used in both questions and LOS to obtain more accurate
classification of the levels of BT. A sample of 775 different action verbs collected from different universities allows us to measure an accurate and clear-cut cognitive level for the action verb. It is worth mentioning that natural language processing techniques were used to develop our rules as to induce the questions into
chunks in order to extract the action verbs. Our proposed solution was able to classify the action verb into a precise level of the cognitive domain. We, on our side, have tested and evaluated our proposed solution using confusion matrix. The results of evaluation tests yielded 97% for the macro average of precision and 90% for F1. Thus, the outcome of the research suggests that it is crucial to analyse and verify the action
verbs cited and used by academicians to write LOS and classify their questions based on blooms taxonomy in order to obtain a definite and more accurate classification.
French machine reading for question answeringAli Kabbadj
Â
This paper proposes to unlock the main barrier to machine reading and comprehension French natural language texts. This open the way to machine to find to a question a precise answer buried in the mass of unstructured French texts. Or to create a universal French chatbot. Deep learning has produced extremely promising results for various tasks in natural language understanding particularly topic classification, sentiment analysis, question answering, and language translation. But to be effective Deep Learning methods need very large training da-tasets. Until now these technics cannot be actually used for French texts Question Answering (Q&A) applications since there was not a large Q&A training dataset. We produced a large (100 000+) French training Dataset for Q&A by translating and adapting the English SQuAD v1.1 Dataset, a GloVe French word and character embed-ding vectors from Wikipedia French Dump. We trained and evaluated of three different Q&A neural network ar-chitectures in French and carried out a French Q&A models with F1 score around 70%.
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
Â
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
Similar to A Semantic Scoring Rubric For Concept Maps Design And Reliability (20)
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
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http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
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This will be used as part of your Personal Professional Portfolio once graded.
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Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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8. 2 pts. The map contains cross-links and these establish correct (true) relationships. However, they are redundant or
not particularly relevant or adequate.
3 pts. The map contains 1-2 correct, relevant and adequate cross-links with physically separate links. However,
based on the concepts present in the map, important and/or evident cross-links are missing.
4 pts. The map contains more than 2 correct, relevant and adequate cross-links with physically separate links.
However, based on the concepts present in the map, important and/or evident cross-links are missing.
5 pts. The map contains more than 2 correct, relevant and adequate cross-links with physically separate links.
Based on the concepts present in the map, no important or evident cross-links are missing.
CRITERION # 6: Presence of cycles7
0 pts. The map contains no cycles.
1 pts. The map contains at least 1 cycle, but some propositions in the cycle do not satisfy criterion # 2.
2 pts. The map contains at least 1 cycle and all propositions in the cycle satisfy criterion # 2.
Levels:
Unevaluated 0
Very low 1 â 5
Low 6 â 8
Intermediate 9 â 11
High 12 â 14
Very high 15 â 18
NOTES
1
A concept is considered irrelevant if: 1) it is not related to the topic under consideration; or 2) it is related to the topic, but does not
contribute substantially to it. One way to decide whether a concept is irrelevant is to think of removing it from the map and ask ourselves if
this alters the mapâs content significantly (in relation to the root concept and the focus question). If our answer is ânoâ, it is quite likely that
this particular concept is not relevant to this map.
2
Examples are specific instances or occurrences of concepts. For instance, âChagres Riverâ is an instance of the concept âriverâ. Examples
are usually joined to concepts by the following linking words: âfor exampleâ, âlikeâ, âsuch asâ, among others.
3
A triad is not a proposition if 1) it lacks the required structure CONCEPT + LINKING PHRASE + CONCEPT; 2) it does not make
logical sense, either because its meaning depends on previous propositions, or due to grammatical mistakes, incorrect use of CmapTools, or
some other reason; 3) it is not autonomous, i.e., it is clearly a fragment or continuation of a larger grammatical structure.
4
A second level proposition corresponds to the second linking phrase counted from the root concept.
5
Dynamic propositions involve: 1) movement, 2) action, 3) change of state, or 4) dependency relationships. They are subdivided into non-
causative and causative dynamic propositions. In causative propositions, one of the concepts must clearly correspond to the cause while the
other one clearly corresponds to the effect. Causative propositions, in turn, may be quantified. Quantified propositions explicitly indicate
the manner in which a certain change in one concept induces a corresponding change in the other concept.
Examples of non-causative dynamic propositions: Roots absorb water; herbivores eat plants; digestive system breaks down food
product; living beings need oxygen.
Examples of causative dynamic propositions: Electric charge generates electric fields; reproduction allows continuity of species;
cigarettes may produce cancer; independent journalism strengthens credibility; exercise decreases risk of developing diabetes; rule
of law attracts foreign investment.
Examples of quantified causative dynamic propositions: Increased transparency in public affairs discourages corruption; under-
activity of the thyroid gland (hypothyroidism) decreases body metabolism; increased quality of education contributes to greater
national development.
Static propositions, on the other hand, serve only to describe characteristics, define properties and organize knowledge. They are generally
associated to linking phrases such as: âisâ, âareâ, âhaveâ, âpossessâ, âare made up ofâ, âare classified intoâ, âare divided intoâ, âcontainâ,
âliveâ, âare calledâ, âis located inâ, âlikesâ, etc.
Examples of static propositions: Sun is a star; means of transportation include land transport means; Panama is located in Central
America; animals may be vertebrates.
6
By propositions with âphysically separate linksâ we mean propositions that use distinct linking entities (boxes) to join one concept to
another. However, the linking words within these separate boxes may be repeated.
7
A cycle is a directed circuit in which the direction of the arrows allows traversing the entire closed path in a single direction.