This document discusses using personalized ontologies to improve web information gathering by representing user profiles. It proposes a model that constructs personalized ontologies by adopting user feedback from a world knowledge base. The model also uses users' local instance repositories to discover background knowledge and populate the ontologies. The proposed ontology model is evaluated against benchmark models through experiments using a large standard dataset.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Learner Ontological Model for Intelligent Virtual Collaborative Learning Envi...ijceronline
An enacting approach to intelligent virtual collaborative learning model is explored through the lens of critical ontology. This ontological model enables to reuse of the domain knowledge and to make the knowledge explicitly available to the agent working as an Expert System, which uses the operational knowledge in collaborative learning environment. This ontological model used by the agent to identify the preliminary competency level of the user. This environment offers personalized education to each learner in accordance with his/her learning preferences, and learning capabilities. Here the factors considered to identify the learning capability taken are demographic profile, age, family profile, basic educational qualification and basic competency scale. The conception of heuristics is then used by the agent to determine the effectiveness of the learner by referring the different parameters of the learner available in the ontological model.To help getting over this, the paper describes the experience on using an ontological model for collaborative learning to relate and integrate the history of the learner by maintaining the history of learner in collaborative learning environment that will be used by the Multi-Objective Grey Situation Decision Making Theory to infer the understanding level of user and produces the conditional content to the user
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
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.
Ontology-based Semantic Approach for Learning Object RecommendationIDES Editor
The main focus of this paper is to apply an ontologybased
approach for semantic learning object recommendation
towards personalized e-learning systems. Ontologies for
learner model, learning objects and semantic mapping rules
are proposed. The recommender can be able to provide
individually learning object by taking the learner preferences
and styles, which used to adjust or fine-tune in learning object
recommending process. In the proposed framework, we
demonstrated how the ontologies can be used to enable
machines to interpret and process learning resources in
recommendation system. The recommendation consists of four
steps: semantic mapping between learner and learning
objects, preference score calculation, learning object ranking
and recommending the learning object. As a result, a
personalized and most suitable learning object is
recommended to the learner.
Building Quranic stories ontology using MappingMaster domain-specific language IJECEIAES
The Holy Quran, due to it is full of many inspiring stories and multiple lessons that need to understand it requires additional attention when it comes to searching issues and information retrieval. Many works were carried out in the Holy Quran field, but some of these dealt with a part of the Quran or covered it in general, and some of them did not support semantic research techniques and the possibility of understanding the Quranic knowledge by the people and computers. As for others, techniques of data analysis, processing, and ontology were adopted, which led to directed these to linguistic aspects more than semantic. Another weakness in the previous works, they have adopted the method manually entering ontology, which is costly and time-consuming. In this paper, we constructed the ontology of Quranic stories. This ontology depended in its construction on the MappingMaster domain-specific language (MappingMaster DSL) technology, through which concepts and individuals can be created and linked automatically to the ontology from Excel sheets. The conceptual structure was built using the object role modeling (ORM) modeling language. SPARQL query language used to test and evaluate the propsed ontology by asking many competency questions and as a result, the ontology answered all these questions well.
Design and Implementation of Efficient Search Methodology for Content-Based R...IDES Editor
E-Learning portal is the full of content of different
formats like text, metadata, image, audio, and video. Current
search methodologies have a direct impact on the fundamental
retrieval issues that information seekers encounter in their use
of the vast number of search systems on the web today. Recently,
information retrieval for text and multimedia content has
become an important research area. Content-based retrieval
in multimedia is a challenging problem since multimedia data
needs detailed interpretation from pixel values. Based on
several new technologies, such as ubiquitous computing,
ontology engineering, semantic web and grid computing, it is
observed that for flexible educational platform architecture
for E-Learning that is OntoEdu is must. In this paper we offer
review report of E-Learning architecture and propose efficient
search algorithm to retrieve multimedia content from the ELearning
environment. The purpose of this technique is to
efficient and fast retrieval of data from content based
environment. The results of these proposed searching
techniques have been found satisfactorily.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Learner Ontological Model for Intelligent Virtual Collaborative Learning Envi...ijceronline
An enacting approach to intelligent virtual collaborative learning model is explored through the lens of critical ontology. This ontological model enables to reuse of the domain knowledge and to make the knowledge explicitly available to the agent working as an Expert System, which uses the operational knowledge in collaborative learning environment. This ontological model used by the agent to identify the preliminary competency level of the user. This environment offers personalized education to each learner in accordance with his/her learning preferences, and learning capabilities. Here the factors considered to identify the learning capability taken are demographic profile, age, family profile, basic educational qualification and basic competency scale. The conception of heuristics is then used by the agent to determine the effectiveness of the learner by referring the different parameters of the learner available in the ontological model.To help getting over this, the paper describes the experience on using an ontological model for collaborative learning to relate and integrate the history of the learner by maintaining the history of learner in collaborative learning environment that will be used by the Multi-Objective Grey Situation Decision Making Theory to infer the understanding level of user and produces the conditional content to the user
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
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.
Ontology-based Semantic Approach for Learning Object RecommendationIDES Editor
The main focus of this paper is to apply an ontologybased
approach for semantic learning object recommendation
towards personalized e-learning systems. Ontologies for
learner model, learning objects and semantic mapping rules
are proposed. The recommender can be able to provide
individually learning object by taking the learner preferences
and styles, which used to adjust or fine-tune in learning object
recommending process. In the proposed framework, we
demonstrated how the ontologies can be used to enable
machines to interpret and process learning resources in
recommendation system. The recommendation consists of four
steps: semantic mapping between learner and learning
objects, preference score calculation, learning object ranking
and recommending the learning object. As a result, a
personalized and most suitable learning object is
recommended to the learner.
Building Quranic stories ontology using MappingMaster domain-specific language IJECEIAES
The Holy Quran, due to it is full of many inspiring stories and multiple lessons that need to understand it requires additional attention when it comes to searching issues and information retrieval. Many works were carried out in the Holy Quran field, but some of these dealt with a part of the Quran or covered it in general, and some of them did not support semantic research techniques and the possibility of understanding the Quranic knowledge by the people and computers. As for others, techniques of data analysis, processing, and ontology were adopted, which led to directed these to linguistic aspects more than semantic. Another weakness in the previous works, they have adopted the method manually entering ontology, which is costly and time-consuming. In this paper, we constructed the ontology of Quranic stories. This ontology depended in its construction on the MappingMaster domain-specific language (MappingMaster DSL) technology, through which concepts and individuals can be created and linked automatically to the ontology from Excel sheets. The conceptual structure was built using the object role modeling (ORM) modeling language. SPARQL query language used to test and evaluate the propsed ontology by asking many competency questions and as a result, the ontology answered all these questions well.
Design and Implementation of Efficient Search Methodology for Content-Based R...IDES Editor
E-Learning portal is the full of content of different
formats like text, metadata, image, audio, and video. Current
search methodologies have a direct impact on the fundamental
retrieval issues that information seekers encounter in their use
of the vast number of search systems on the web today. Recently,
information retrieval for text and multimedia content has
become an important research area. Content-based retrieval
in multimedia is a challenging problem since multimedia data
needs detailed interpretation from pixel values. Based on
several new technologies, such as ubiquitous computing,
ontology engineering, semantic web and grid computing, it is
observed that for flexible educational platform architecture
for E-Learning that is OntoEdu is must. In this paper we offer
review report of E-Learning architecture and propose efficient
search algorithm to retrieve multimedia content from the ELearning
environment. The purpose of this technique is to
efficient and fast retrieval of data from content based
environment. The results of these proposed searching
techniques have been found satisfactorily.
GIVING THE LEARNERS CONTROL OF NAVIGATION: COGNITIVE GAINS AND LOSSESTamas Makany
Web based learning often involves exploration of the information space similar to the exploration of space in the real world. Initial paths taken in a novel physical or virtual environment will not only guide the discoveries of what the environment contains, but also formulate the underlying organising principles. The suggested route in a museum frequently presents artworks that are either chronological or conceptually tied together. Deviating from this –and taking a route of our own– might be either confusing or insightful. The structure of presenting the information and the control that the learners have in exploring it, plays a major role in determining mental representations and meaning. In the virtual world these possibilities and degrees of freedom in navigation are not constrained like the physical world, thus, can be colossal. The question that arises is what are the gains and losses of allowing the learners to freely control their explorations in an information space? To investigate this, we designed three web based learning environments that differed in their navigational structure, but not in their content. The first environment (Linear) was strictly sequential and did not allow any alternation from a defined sequence of the pages. With this Linear structure, the learners had almost no control over their navigation as they could only continue to the next page in the sequence or go back to the previous page. The second environment (Star) partially restricted control by connecting the webpages through a central homepage. The third environment (Interconnected) gave full navigational control to the participants, as every webpage was accessible from each of the other webpages. Detailed navigational behaviour was recorded and learning efficiency was measured. Participants answered the same questions immediately after learning and also after a period of two weeks in order to evaluate both short and long term learning outcomes. In this paper, we describe and interpret the results from the experiment with special focus on how control in web based learning enhances efficiency in one case; while hinders performance in others.
Semi Automated Text Categorization Using Demonstration Based Term SetIJCSEA Journal
Manual Analysis of huge amount of textual data requires a tremendous amount of processing time and effort in reading the text and organizing them in required format. In the current scenario, the major problem is with text categorization because of the high dimensionality of feature space. Now-a-days there are many methods available to deal with text feature selection. This paper aims at such semi automated text categorization feature selection methodology to deal with a massive data using one of the phases of David Merrill’s First principles of instruction (FPI). It uses a pre-defined category group by providing them with the proper training set based on the demonstration phase of FPI. The methodology involves the text tokenization, text categorization and text analysis.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Collaborative Learning of Organisational KnolwedgeWaqas Tariq
This paper presents recent research into methods used in Australian Indigenous Knowledge sharing and looks at how these can support the creation of suitable collaborative envi- ronments for timely organisational learning. The protocols and practices as used today and in the past by Indigenous communities are presented and discussed in relation to their relevance to a personalised system of knowledge sharing in modern organisational cultures. This research focuses on user models, knowledge acquisition and integration of data for constructivist learning in a networked repository of or- ganisational knowledge. The data collected in the repository is searched to provide collections of up-to-date and relevant material for training in a work environment. The aim is to improve knowledge collection and sharing in a team envi- ronment. This knowledge can then be collated into a story or workflow that represents the present knowledge in the organisation.
MalayIK: An Ontological Approach to Knowledge Transformation in Malay Unstruc...IJECEIAES
The number of unstructured documents written in Malay language is enormously available on the web and intranets. However, unstructured documents cannot be queried in simple ways, hence the knowledge contained in such documents can neither be used by automatic systems nor could be understood easily and clearly by humans. This paper proposes a new approach to transform extracted knowledge in Malay unstructured document using ontology by identifying, organizing, and structuring the documents into an interrogative structured form. A Malay knowledge base, the MalayIK corpus is developed and used to test the MalayIK-Ontology against Ontos, an existing data extraction engine. The experimental results from MalayIKOntology have shown a significant improvement of knowledge extraction over Ontos implementation. This shows that clear knowledge organization and structuring concept is able to increase understanding, which leads to potential increase in sharable and reusable of concepts among the community.
Comparative evaluation of four multi label classification algorithms in class...csandit
The classification of learning objects (LOs) enables users to search for, access, and reuse them
as needed. It makes e-learning as effective and efficient as possible. In this article the multilabel
learning approach is represented for classifying and ranking multi-labelled LOs, whereas
each LO might be associated with multiple labels as opposed to a single-label approach. A
comprehensive overview of the common fundamental multi-label classification algorithms and
metrics will be discussed. In this article, a new multi-labelled LOs dataset will be created and
extracted from ARIADNE Learning Object Repository. We experimentally train four effective
multi-label classifiers on the created LOs dataset and then, assess their performance based on
the results of 16 evaluation metrics. The result of this article will answer the question of: what is
the best multi-label classification algorithm for classifying multi-labelled LOs?
Concept integration using edit distance and n gram match ijdms
Information is growing more rapidly on the World Wide Web (WWW) has made it necessary to make all
this information not only available to people but also to the machines. Ontology and token are widely being
used to add the semantics in data processing or information processing. A concept formally refers to the
meaning of the specification which is encoded in a logic-based language, explicit means concepts,
properties that specification is machine readable and also a conceptualization model how people think
about things of a particular subject area. In modern scenario more ontologies has been developed on
various different topics, results in an increased heterogeneity of entities among the ontologies. The concept
integration becomes vital over last decade and a tool to minimize heterogeneity and empower the data
processing. There are various techniques to integrate the concepts from different input sources, based on
the semantic or syntactic match values. In this paper, an approach is proposed to integrate concept
(Ontologies or Tokens) using edit distance or n-gram match values between pair of concept and concept
frequency is used to dominate the integration process. The proposed techniques performance is compared
with semantic similarity based integration techniques on quality parameters like Recall, Precision, FMeasure
& integration efficiency over the different size of concepts. The analysis indicates that edit
distance value based interaction outperformed n-gram integration and semantic similarity techniques.
A novel method for generating an elearning ontologyIJDKP
The Semantic Web provides a common framework that allows data to be shared and reused across
applications, enterprises, and community boundaries. The existing web applications need to express
semantics that can be extracted from users' navigation and content, in order to fulfill users' needs. Elearning
has specific requirements that can be satisfied through the extraction of semantics from learning
management systems (LMS) that use relational databases (RDB) as backend. In this paper, we propose
transformation rules for building owl ontology from the RDB of the open source LMS Moodle. It allows
transforming all possible cases in RDBs into ontological constructs. The proposed rules are enriched by
analyzing stored data to detect disjointness and totalness constraints in hierarchies, and calculating the
participation level of tables in n-ary relations. In addition, our technique is generic; hence it can be applied
to any RDB.
Mining in Ontology with Multi Agent System in Semantic Web : A Novel Approachijma
A large amount of data is present on the web. It contains huge number of web pages and to find suitable
information from them is very cumbersome task. There is need to organize data in formal manner so that
user can easily access and use them. To retrieve information from documents, there are many Information
Retrieval (IR) techniques. Current IR techniques are not so advanced that they can be able to exploit
semantic knowledge within documents and give precise results. IR technology is major factor responsible
for handling annotations in Semantic Web (SW) languages. With the rate of growth of web and huge
amount of information available on the web which may be in unstructured, semi structured or structured
form, it has become increasingly difficult to identify the relevant pieces of information on the internet. IR
technology is major factor responsible for handling annotations in Semantic Web (SW) languages.
Knowledgeable representation languages are used for retrieving information. So, there is need to build an
ontology that uses well defined methodology and process of developing ontology is called Ontology
Development. Secondly, Cloud computing and data mining have become famous phenomena in the current
application of information technology. With the changing trends and emerging of the new concept in the
information technology sector, data mining and knowledge discovery have proved to be of significant
importance. Data mining can be defined as the process of extracting data or information from a database
which is not explicitly defined by the database and can be used to come up with generalized conclusions
based on the trends obtained from the data. A database may be described as a collection of formerly
structured data. Multi agents data mining may be defined as the use of various agents cooperatively
interact with the environment to achieve a specified objective. Multi agents will always act on behalf of
users and will coordinate, cooperate, negotiate and exchange data with each other. An agent would
basically refer to a software agent, a robot or a human being Knowledge discovery can be defined as the
process of critically searching large collections of data with the aim of coming up with patterns that can be
used to make generalized conclusions. These patterns are sometimes referred to as knowledge about the
data. Cloud computing can be defined as the delivery of computing services in which shared resources,
information and software’s are provided over a network, for example, the information super highway.
Cloud computing is normally provided over a web based service which hosts all the resources required. As,
the knowledge mining is used in many fields of study such as in science and medicine, finance, education,
manufacturing and commerce. In this paper, the Semantic Web addresses the first part of this challenge by
trying to make the data also machine understandable in the form of Ontology, while Multi-Agen
Text Detection and Recognition with Speech Output for Visually Challenged Per...IJERA Editor
Reading text from scene, images and text boards is an exigent task for visually challenged persons. This task has been proposed to be carried out with the help of image processing. Since a long period of time, image processing has helped a lot in the field of object recognition and still an emerging area of research. The proposed system reads the text encountered in images and text boards with the aim to provide support to the visually challenged persons. Text detection and recognition in natural scene can give valuable information for many applications. In this work, an approach has been attempted to extract and recognize text from scene images and convert that recognized text into speech. This task can definitely be an empowering force in a visually challenged person's life and can be supportive in relieving them of their frustration of not being able to read whatever they want, thus enhancing the quality of their lives.
Autonomous Educational Testing System Using Unsupervised Feature Learning.IJITE
With the increase of ubiquitous data all over the internet, intelligent classroom systems that integrate
traditional learning techniques with modern e-learning tools have become quite popular and necessary
today. Although a substantial amount of work has been done in the field of e-learning, specifically in
automation of objective question and answer evaluation, personalized learning, adaptive evaluation
systems, the field of qualitative analysis of a student’s subjective paragraph answers remains unexplored to
a large extent.
The traditional board, chalk, talk based classroom scenario involves a teacher setting question papers
based on the concepts taught, checks the answers written by students manually and thus evaluates the
students’ performance. However, setting question papers remains a time consuming process with the
teacher having to bother about question quality, level of difficulty and redundancy. In addition the process
of manually correcting students’ answers is a cumbersome and tedious task especially where the class size
is large.
In this paper, we put forth the design, analysis and implementation details along with some experimental
outputs to build a system that integrates all the above mentioned tasks with minimal teacher involvement
that not only automates the traditional classroom scenario but also overcomes its inherent shortcomings
and fallacies.
GIVING THE LEARNERS CONTROL OF NAVIGATION: COGNITIVE GAINS AND LOSSESTamas Makany
Web based learning often involves exploration of the information space similar to the exploration of space in the real world. Initial paths taken in a novel physical or virtual environment will not only guide the discoveries of what the environment contains, but also formulate the underlying organising principles. The suggested route in a museum frequently presents artworks that are either chronological or conceptually tied together. Deviating from this –and taking a route of our own– might be either confusing or insightful. The structure of presenting the information and the control that the learners have in exploring it, plays a major role in determining mental representations and meaning. In the virtual world these possibilities and degrees of freedom in navigation are not constrained like the physical world, thus, can be colossal. The question that arises is what are the gains and losses of allowing the learners to freely control their explorations in an information space? To investigate this, we designed three web based learning environments that differed in their navigational structure, but not in their content. The first environment (Linear) was strictly sequential and did not allow any alternation from a defined sequence of the pages. With this Linear structure, the learners had almost no control over their navigation as they could only continue to the next page in the sequence or go back to the previous page. The second environment (Star) partially restricted control by connecting the webpages through a central homepage. The third environment (Interconnected) gave full navigational control to the participants, as every webpage was accessible from each of the other webpages. Detailed navigational behaviour was recorded and learning efficiency was measured. Participants answered the same questions immediately after learning and also after a period of two weeks in order to evaluate both short and long term learning outcomes. In this paper, we describe and interpret the results from the experiment with special focus on how control in web based learning enhances efficiency in one case; while hinders performance in others.
Semi Automated Text Categorization Using Demonstration Based Term SetIJCSEA Journal
Manual Analysis of huge amount of textual data requires a tremendous amount of processing time and effort in reading the text and organizing them in required format. In the current scenario, the major problem is with text categorization because of the high dimensionality of feature space. Now-a-days there are many methods available to deal with text feature selection. This paper aims at such semi automated text categorization feature selection methodology to deal with a massive data using one of the phases of David Merrill’s First principles of instruction (FPI). It uses a pre-defined category group by providing them with the proper training set based on the demonstration phase of FPI. The methodology involves the text tokenization, text categorization and text analysis.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Collaborative Learning of Organisational KnolwedgeWaqas Tariq
This paper presents recent research into methods used in Australian Indigenous Knowledge sharing and looks at how these can support the creation of suitable collaborative envi- ronments for timely organisational learning. The protocols and practices as used today and in the past by Indigenous communities are presented and discussed in relation to their relevance to a personalised system of knowledge sharing in modern organisational cultures. This research focuses on user models, knowledge acquisition and integration of data for constructivist learning in a networked repository of or- ganisational knowledge. The data collected in the repository is searched to provide collections of up-to-date and relevant material for training in a work environment. The aim is to improve knowledge collection and sharing in a team envi- ronment. This knowledge can then be collated into a story or workflow that represents the present knowledge in the organisation.
MalayIK: An Ontological Approach to Knowledge Transformation in Malay Unstruc...IJECEIAES
The number of unstructured documents written in Malay language is enormously available on the web and intranets. However, unstructured documents cannot be queried in simple ways, hence the knowledge contained in such documents can neither be used by automatic systems nor could be understood easily and clearly by humans. This paper proposes a new approach to transform extracted knowledge in Malay unstructured document using ontology by identifying, organizing, and structuring the documents into an interrogative structured form. A Malay knowledge base, the MalayIK corpus is developed and used to test the MalayIK-Ontology against Ontos, an existing data extraction engine. The experimental results from MalayIKOntology have shown a significant improvement of knowledge extraction over Ontos implementation. This shows that clear knowledge organization and structuring concept is able to increase understanding, which leads to potential increase in sharable and reusable of concepts among the community.
Comparative evaluation of four multi label classification algorithms in class...csandit
The classification of learning objects (LOs) enables users to search for, access, and reuse them
as needed. It makes e-learning as effective and efficient as possible. In this article the multilabel
learning approach is represented for classifying and ranking multi-labelled LOs, whereas
each LO might be associated with multiple labels as opposed to a single-label approach. A
comprehensive overview of the common fundamental multi-label classification algorithms and
metrics will be discussed. In this article, a new multi-labelled LOs dataset will be created and
extracted from ARIADNE Learning Object Repository. We experimentally train four effective
multi-label classifiers on the created LOs dataset and then, assess their performance based on
the results of 16 evaluation metrics. The result of this article will answer the question of: what is
the best multi-label classification algorithm for classifying multi-labelled LOs?
Concept integration using edit distance and n gram match ijdms
Information is growing more rapidly on the World Wide Web (WWW) has made it necessary to make all
this information not only available to people but also to the machines. Ontology and token are widely being
used to add the semantics in data processing or information processing. A concept formally refers to the
meaning of the specification which is encoded in a logic-based language, explicit means concepts,
properties that specification is machine readable and also a conceptualization model how people think
about things of a particular subject area. In modern scenario more ontologies has been developed on
various different topics, results in an increased heterogeneity of entities among the ontologies. The concept
integration becomes vital over last decade and a tool to minimize heterogeneity and empower the data
processing. There are various techniques to integrate the concepts from different input sources, based on
the semantic or syntactic match values. In this paper, an approach is proposed to integrate concept
(Ontologies or Tokens) using edit distance or n-gram match values between pair of concept and concept
frequency is used to dominate the integration process. The proposed techniques performance is compared
with semantic similarity based integration techniques on quality parameters like Recall, Precision, FMeasure
& integration efficiency over the different size of concepts. The analysis indicates that edit
distance value based interaction outperformed n-gram integration and semantic similarity techniques.
A novel method for generating an elearning ontologyIJDKP
The Semantic Web provides a common framework that allows data to be shared and reused across
applications, enterprises, and community boundaries. The existing web applications need to express
semantics that can be extracted from users' navigation and content, in order to fulfill users' needs. Elearning
has specific requirements that can be satisfied through the extraction of semantics from learning
management systems (LMS) that use relational databases (RDB) as backend. In this paper, we propose
transformation rules for building owl ontology from the RDB of the open source LMS Moodle. It allows
transforming all possible cases in RDBs into ontological constructs. The proposed rules are enriched by
analyzing stored data to detect disjointness and totalness constraints in hierarchies, and calculating the
participation level of tables in n-ary relations. In addition, our technique is generic; hence it can be applied
to any RDB.
Mining in Ontology with Multi Agent System in Semantic Web : A Novel Approachijma
A large amount of data is present on the web. It contains huge number of web pages and to find suitable
information from them is very cumbersome task. There is need to organize data in formal manner so that
user can easily access and use them. To retrieve information from documents, there are many Information
Retrieval (IR) techniques. Current IR techniques are not so advanced that they can be able to exploit
semantic knowledge within documents and give precise results. IR technology is major factor responsible
for handling annotations in Semantic Web (SW) languages. With the rate of growth of web and huge
amount of information available on the web which may be in unstructured, semi structured or structured
form, it has become increasingly difficult to identify the relevant pieces of information on the internet. IR
technology is major factor responsible for handling annotations in Semantic Web (SW) languages.
Knowledgeable representation languages are used for retrieving information. So, there is need to build an
ontology that uses well defined methodology and process of developing ontology is called Ontology
Development. Secondly, Cloud computing and data mining have become famous phenomena in the current
application of information technology. With the changing trends and emerging of the new concept in the
information technology sector, data mining and knowledge discovery have proved to be of significant
importance. Data mining can be defined as the process of extracting data or information from a database
which is not explicitly defined by the database and can be used to come up with generalized conclusions
based on the trends obtained from the data. A database may be described as a collection of formerly
structured data. Multi agents data mining may be defined as the use of various agents cooperatively
interact with the environment to achieve a specified objective. Multi agents will always act on behalf of
users and will coordinate, cooperate, negotiate and exchange data with each other. An agent would
basically refer to a software agent, a robot or a human being Knowledge discovery can be defined as the
process of critically searching large collections of data with the aim of coming up with patterns that can be
used to make generalized conclusions. These patterns are sometimes referred to as knowledge about the
data. Cloud computing can be defined as the delivery of computing services in which shared resources,
information and software’s are provided over a network, for example, the information super highway.
Cloud computing is normally provided over a web based service which hosts all the resources required. As,
the knowledge mining is used in many fields of study such as in science and medicine, finance, education,
manufacturing and commerce. In this paper, the Semantic Web addresses the first part of this challenge by
trying to make the data also machine understandable in the form of Ontology, while Multi-Agen
Text Detection and Recognition with Speech Output for Visually Challenged Per...IJERA Editor
Reading text from scene, images and text boards is an exigent task for visually challenged persons. This task has been proposed to be carried out with the help of image processing. Since a long period of time, image processing has helped a lot in the field of object recognition and still an emerging area of research. The proposed system reads the text encountered in images and text boards with the aim to provide support to the visually challenged persons. Text detection and recognition in natural scene can give valuable information for many applications. In this work, an approach has been attempted to extract and recognize text from scene images and convert that recognized text into speech. This task can definitely be an empowering force in a visually challenged person's life and can be supportive in relieving them of their frustration of not being able to read whatever they want, thus enhancing the quality of their lives.
Autonomous Educational Testing System Using Unsupervised Feature Learning.IJITE
With the increase of ubiquitous data all over the internet, intelligent classroom systems that integrate
traditional learning techniques with modern e-learning tools have become quite popular and necessary
today. Although a substantial amount of work has been done in the field of e-learning, specifically in
automation of objective question and answer evaluation, personalized learning, adaptive evaluation
systems, the field of qualitative analysis of a student’s subjective paragraph answers remains unexplored to
a large extent.
The traditional board, chalk, talk based classroom scenario involves a teacher setting question papers
based on the concepts taught, checks the answers written by students manually and thus evaluates the
students’ performance. However, setting question papers remains a time consuming process with the
teacher having to bother about question quality, level of difficulty and redundancy. In addition the process
of manually correcting students’ answers is a cumbersome and tedious task especially where the class size
is large.
In this paper, we put forth the design, analysis and implementation details along with some experimental
outputs to build a system that integrates all the above mentioned tasks with minimal teacher involvement
that not only automates the traditional classroom scenario but also overcomes its inherent shortcomings
and fallacies.
Today, the reactivity of the IT department and time to market become major objectives for businesses. Achieving these goals requires mechanically the enforcement of the quality of the delivered products: without this, acceleration would mean multiplication of incidents. Quality will also bring serenity for everybody.
Leveraging on our own achievements for major CAC40 businesses, we will explain you how automation of the whole delivery chain - along with other practices - will contribute to improve the Information System.
Automation projects can be long and painful while ROI is always difficult to evaluate as they usually imply major transformations for teams, processes and tools. In this session, we will debat on ways to increase the value of your project while demonstrating the benefit of automation for everyone in the organization.
Although of the semantic web technologies utilization in the learning development field is a new research area, some authors have already proposed their idea of how an effective that operate. Specifically, from analysis of the literature in the field, we have identified three different types of existing applications that actually employ these technologies to support learning. These applications aim at: Enhancing the learning objects reusability by linking them to an ontological description of the domain, or, more generally, describe relevant dimension of the learning process in an ontology, then; providing a comprehensive authoring system to retrieve and organize web material into a learning course, and constructing advanced strategies to present annotated resources to the user, in the form of browsing facilities, narrative generation and final rendering of a course. On difference with the approaches cited above, here we propose an approach that is modeled on narrative studies and on their transposition in the digital world. In the rest of the paper, we present the theoretical basis that inspires this approach, and show some examples that are guiding our implementation and testing of these ideas within e-learning. By emerging the idea of the ontologies are recognized as the most important component in achieving semantic interoperability of e-learning resources. The benefits of their use have already been recognized in the learning technology community. In order to better define different aspects of ontology applications in e-learning, researchers have given several classifications of ontologies. We refer to a general one given in that differentiates between three dimensions ontologies can describe: content, context, and structure. Most of the present research has been dedicated to the first group of ontologies. A well-known example of such an ontology is based on the ACM Computer Classification System (ACM CCS) and defined by Resource Description Framework Schema (RDFS). It’s used in the MOODLE to classify learning objects with a goal to improve searching. The chapter will cover the terms of the semantic web and e-learning systems design and management in e-learning (MOODLE) and some of studies depend on e-learning and semantic web, thus the tools will be used in this paper, and lastly we shall discuss the expected contribution. The special attention will be putted on the above topics.
An Ontology Model for Knowledge Representation over User ProfilesIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Approach to Owl Concept Extraction and Integration Across Multiple Ontolog...dannyijwest
Increase in number of ontologies on Semantic Web and endorsement of OWL as language of discourse for
the Semantic Web has lead to a scenario where research efforts in the field of ontology engineering may be
applied for making the process of ontology development through reuse a viable option for ontology
developers. The advantages are twofold as when existing ontological artefacts from the Semantic Web are
reused, semantic heterogeneity is reduced and help in interoperability which is the essence of Semantic
Web. From the perspective of ontology development advantages of reuse are in terms of cutting down on
cost as well as development life as ontology engineering requires expert domain skills and is time taking
process. We have devised a framework to address challenges associated with reusing ontologies from the
Semantic Web. In this paper we present methods adopted for extraction and integration of concepts across
multiple ontologies.
2015 03 19 (EDUCON2015) eMadrid UPM Towards a Learning Analytics Approach for...eMadrid network
2015 03 19 (EDUCON2015) eMadrid UPM Towards a Learning Analytics Approach for Supporting discovery and reuse of OER. An approach based on Social Networks Analysis and Linked Open Data
The e-learning contained many educational resources are generally used in learning systems like Moodle, It’s free open source software packages designed and flexible platform to create Learning Objects (LOs) and users’ accounts. The author demonstrates how to use semantic web technologies to improve online learning environments and bridge the gap between learners and LOs. The ontological construction presented here helps formalize LOs context as a complex interplay of different learning-related elements and shows how we can use semantic annotation to interrelate diverse between learner and LOs. On top of this construction, the author implemented several feedback channels for educators to improve the delivery of future Web-based learning. The particular aim of this paper was to provide a solution based in the Moodle Platform. The main idea behind the approach presented here is that ontology which can not only be useful as a learning instrument but it can also be employed to assess students’ skills. For it, each student is prompted to express his/her beliefs by building own discipline-related ontology through an application displayed in the interface of Moodle. This paper presents the ontology for an e-Learning System, which arranges metadata, and defines the relationships of metadata, which are about learning objects; belong to academic courses and user profiles. This ontology has been incorporated as a critical part of the proposed architecture. By this ontology, effective retrieval of learning content, customizing Learning Management System (LMS) is expected. Metadata used in this paper are based on current metadata standards. This ontology specified in human and machine-readable formats. In implementing it, several APIs were defined to manage the ontology. They were introduced into a typical LMS such as Moodle. Proposed ontology maps user preferences with learning content to satisfy learner requirements. These learning objects are presented to the learner based on ontological relationships. Hence it increases the usability and customizes the LMS. In conclusion, ontologies have a range of potential benefits and applications in further and higher education, including the sharing of information across e-learning systems, providing frameworks for learning object reuse, and enabling information between learner and system parts.
A Comparative Study of Recent Ontology Visualization Tools with a Case of Dia...IJORCS
Ontology is a conceptualization of a domain into machine readable format. Ontologies are becoming increasingly popular modelling schemas for knowledge management services and applications. Focus on developing tools to graphically visualise ontologies is rising to aid their assessment and analysis. Graph visualisation helps to browse and comprehend the structure of ontologies. A number of ontology visualizations exist that have been embedded in ontology management tools. The primary goal of this paper is to analyze recently implemented ontology visualization tools and their contributions in the enrichment of users’ cognitive support. This work also presents the preliminary results of an evaluation of three visualization tools to determine the suitability of each method for end user applications where ontologies are used as browsing aids with a case of Diabetes data
ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEWijait
Protégé is one of the most popular tools of the ontology visualization. The “Protégé” tools are being applied for further development in various disciplines for better understanding of knowledge. These tools commonly use four methods of ontology visualization, namely, indented list, node-link and tree, zoomable, and focus+context. The purpose of this work is to present a study on application of these four methods in the development of different kinds of protégé visualization tools and categorize their characteristics and features so that it assists in method selection and promotes further future research in the area of ontology visualization.
ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEW ijait
Protégé is one of the most popular tools of the ontology visualization. The “Protégé” tools are being applied for further development in various disciplines for better understanding of knowledge. These tools commonly use four methods of ontology visualization, namely, indented list, node-link and tree,
zoomable, and focus+context. The purpose of this work is to present a study on application of these four methods in the development of different kinds of protégé visualization tools and categorize their characteristics and features so that it assists in method selection and promotes further future research in
the area of ontology visualization.
Building a multilingual ontology for education domain using monto methodCSITiaesprime
Ontologies are emerging technology in building knowledge based information
retrieval systems. It is used to conceptualize the information in human
understandable manner. Knowledge based information retrieval are widely
used in the domain like Education, Artificial Intelligence, Healthcare and so
on. It is important to provide multilingual information of those domains to
facilitate multi-language users. In this paper, we propose a multilingual
ontology (MOnto) methodology to develop multilingual ontology applications
for education domain. New algorithms are proposed for merging and mapping
multilingual ontologies.
Association Rule Mining Based Extraction of Semantic Relations Using Markov ...dannyijwest
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.
An efficient educational data mining approach to support e-learningVenu Madhav
The e-learning is a recent development that has
emerged in the educational system due to the growth of the
information technology. The common challenges involved
in The e-learning platform include the collection and
annotation of the learning materials, organization of the
knowledge in a useful way, the retrieval and discovery of
the useful learning materials from the knowledge space in a
more significant way, and the delivery of the adaptive and
personalized learning materials. In order to handle these
challenges, the proposed system is developed using five
different steps of knowledge input such as the annotation of
the learning materials, creation of knowledge space,
indexing of learning materials using the multi-dimensional
knowledge and XML structure to generate a knowledge
grid and the retrieval of learning materials performed by
matching the user query with the indexed database and
ontology. The process is carried out in two modules such as
the server module and client module. The proposed
approach is evaluated using various parameters such as the
precision, recall and F-measure. Comprehensive results are
achieved by varying the keywords, number of documents
and the K-size. The proposed approach has yielded
excellent results by obtaining the higher evaluation metric,
together with an average precision of 0.81, average
Design a personalized e-learning system based on item response theory and art...eraser Juan José Calderón
Design a personalized e-learning system based on item response theory and artificial neural network approach
Ahmad Baylari, Gh.A. Montazer *
IT Engineering Department, School of Engineering, Tarbiat Modares University, Tehran, Iran
Design a personalized e-learning system based on item response theory and art...eraser Juan José Calderón
Design a personalized e-learning system based on item response theory and artificial neural network approach. Ahmad Baylari, Gh.A. Montazer*IT Engineering Department, School of Engineering, Tarbiat Modares University, Tehran, Iran
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IJCER (www.ijceronline.com) International Journal of computational Engineering research
1. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5
Web Personalization using Efficient Ontology Relations
Mohd. Sadik Ahamad 1, S. Naga Raju 2
1
Kakatiya University, Kakatiya Institute of Technology and Science,
Warangal, Andhra Pradesh, India.
2
Kakatiya University, Kakatiya Institute of Technology and science,
Assoc.Prof. Department of computer science engineering, Warangal, Andhra Pradesh, India.
A b s t r a c t — on the last decades, the amount of web- background knowledge discovery from the user local
based information available has increased dramatically. How information. Such a personalized ontology model should
to gather useful information from the web has become a produce a superior representation of user profiles for web
challenging issue for users. Current web information information gathering.
gathering systems attempt to satisfy user requirements by
capturing their information needs. For this purpose, user In this paper, an ontology model to evaluate this
profiles are created for user background knowledge hypothesis is proposed. This model simulates users’ concept
description. As a model for knowledge description and models by using personalized ontologies, and attempts to
formalization, ontologies are widely used to represent user improve web information gathering performance by using
profiles in personalized web information gathering. However, ontological user profiles. The world knowledge and a user’s
when representing user profiles, many models have utilized local instance repository (LIR) are used in the proposed
only knowledge from either a global knowledge base or user model. World knowledge is commonsense knowledge
local information. In this project, a personalized ontology acquired by people from experience and education; an LIR is
model is proposed for knowledge representation and a user’s personal collection of information items. From a
reasoning over user profiles. world knowledge base, we construct personalized ontologies
by adopting user feedback on interesting knowledge. A
Keywords— Ontology, Semantic Relations, Web Mining multidimensional ontology mining method, Specificity and
Exhaustivity, is also introduced in the proposed model for
1. INTRODUCTION analyzing concepts specified in ontologies. The users’ LIRs
Today, Global analysis uses existing global are then used to discover background knowledge and to
knowledge bases for user background knowledge populate the personalized ontologies. The proposed ontology
representation. Commonly used knowledge bases include model is evaluated by comparison against some benchmark
generic ontologies e.g., WordNet, thesauruses (e.g., digital models through experiments using a large standard data set.
libraries), and online knowledge bases (e.g., online
categorizations and Wikipedia). The global analysis 2. PREVIOUS WORK
techniques produce effective performance for user Electronic learning (e-Learning) refers to the
background knowledge extraction. However, global analysis application of information and communication technologies
is limited by the quality of the used knowledge base. For (e.g., Internet, multimedia, etc.) to enhance ordinary
example, WordNet was reported as helpful in capturing user classroom teaching and learning. With the maturity of the
interest in some areas but useless for others. technologies such as the Internet and the decreasing cost of
the hardware platforms, more institutions are adopting e-
Local analysis investigates user local information or Learning as a supplement to traditional instructional methods.
observes user behavior in user profiles. For example, In fact, one of the main advantages of e-Learning technology
taxonomical patterns from the users’ local text documents to is that it can facilitate adaptive learning such that instructors
learn ontologies for user profiles. Some groups learned can dynamically revise and deliver instructional materials in
personalized ontologies adaptively from user’s browsing accordance with learners’ current progress. In general,
history. Alternatively, analyzed query logs to discover user adaptive teaching and learning refers to the use of what is
background knowledge. In some works, such as, users were known about learners, a priori or through interactions, to alter
provided with a set of documents and asked for relevance how a learning experience unfolds, with the aim of
feedback. User background knowledge was then discovered improving learners’ success and satisfaction. The current
from this feedback for user profiles. However, because local state-of the- art of e-Learning technology supports automatic
analysis techniques rely on data mining or classification collection of learners’ performance data (e.g., via online
techniques for knowledge discovery, occasionally the quiz). [1]
discovered results contain noisy and uncertain information.
As a result, local analysis suffers from ineffectiveness at However, few of the existing e-Learning technologies can
capturing formal user knowledge. From this, we can support automatic analysis of learners’ progress in terms of
hypothesize that user background knowledge can be better the knowledge structures they have acquired. In this paper,
discovered and represented if we can integrate global and we illustrate a methodology of automatically constructing
local analysis within a hybrid model. The knowledge concept maps to characterize learners’ understanding for a
formalized in a global knowledge base will constrain the particular topic; thereby instructors can conduct adaptive
Issn 2250-3005(online) September| 2012 Page1329
2. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5
teaching and learning based on the learners’ knowledge information load on both the learners and the instructors. For
structures as reflected on the concept maps. In particular, our example, to promote reflexive and interactive learning,
concept map generation mechanism is underpinned by a instructors often encourage their students to use online
context-sensitive text mining method and a fuzzy domain discussion boards, blogs, or chat rooms to reflect what they
ontology extraction algorithm. have learnt and to share their knowledge with other fellow
students during or after normal class time. With the current
The notion of ontology is becoming very useful in practice, instructors need to read through all the messages in
various fields such as intelligent information extraction and order to identify the actual progress of their students.
retrieval, semantic Web, electronic commerce, and
knowledge management. Although there is not a universal 3. PROPOSED SYSTEM
consensus on the precise definition of ontology, it is A. Ontology Construction
generally accepted that ontology is a formal specification of The subjects of user interest are extracted from the WKB
conceptualization. via user interaction. A tool called Ontology Learning
Environment (OLE) is developed to assist users with such
Ontology can take the simple form of a taxonomy of interaction. Regarding a topic, the interesting subjects consist
concepts (i.e., light weight ontology), or the more of two sets: positive subjects are the concepts relevant to the
comprehensive representation of comprising a taxonomy, as information need, and negative subjects are the concepts
well as the axioms and constraints which characterize some resolving paradoxical or ambiguous interpretation of the
prominent features of the real-world (i.e., heavy weight information need. Thus, for a given topic, the OLE provides
ontology). Domain ontology is one kind of ontology which is users with a set of candidates to identify positive and
used to represent the knowledge for a particular type of negative subjects. For each subject, its ancestors are retrieved
application domain. On the other hand, concept maps are if the label of contains any one of the query terms in the
used to elicit and represent the knowledge structure such as given topic. From these candidates, the user selects positive
concepts and propositions as perceived by individuals. subjects for the topic. The user-selected positive subjects are
Concept maps are similar to ontology in the sense that both presented in hierarchical form. The candidate negative
of these tools are used to represent concepts and the semantic subjects are the descendants of the user-selected positive
relationships among concepts. [1] subjects. From these negative candidates, the user selects the
negative subjects. These positive subjects will not be
However, ontology is a formal knowledge included in the negative set. The remaining candidates, who
representation method to facilitate human and computer are not fed back as either positive or negative from the user,
interactions and it can be expressed by using formal semantic become the neutral subjects to the given topic. Ontology is
markup languages such as RDF and OWL, whereas concept then constructed for the given topic using these users fed
map is an informal tool for humans to specify semantic back subjects. The structure of the ontology is based on the
knowledge structure. Figure shows an example of the owl semantic relations linking these subjects. The ontology
statements describing one of the fuzzy domain ontologies contains three types of knowledge: positive subjects, negative
automatically generated from our system. It should be noted subjects, and neutral subjects.
that we use the (rel) attribute of the <rdfs:comment> tag to
describe the membership of a fuzzy relation (e.g., the super- B. Semantic Specificity
class/sub-class relationship). We only focus on the automatic The semantic specificity is computed based on the
extraction of lightweight domain ontology in this paper. structure inherited from the world knowledge base. The
More specificially, the lightweight fuzzy domain ontology is strength of such a focus is influenced by the subject’s locality
used to generate concept maps to represent learners’ in the taxonomic structure. The subjects are graph linked by
knowledge structures. semantic relations. The upper level subjects have more
descendants, and thus refer to more concepts, compared with
With the rapid growth of the applications of e- the lower bound level subjects. Thus, in terms of a concept
Learning to enhance traditional instructional methods, it is being referred to by both an upper and lower subjects, the
not surprising to find that there are new issues or challenges lower subject has a stronger focus because it has fewer
arising when educational practitioners try to bring concepts in its space. Hence, the semantic specificity of a
information technologies down to their classrooms. The lower subject is greater than that of an upper subject. The
situation is similar to the phenomenon of the rapid growth of semantic specificity is measured based on the hierarchical
the Internet and the World Wide Web (Web). The explosive semantic relations (is-a and part-of) held by a subject and its
growth of the Web makes information seekers become neighbors. The semantic specificity of a subject is measured,
increasingly more difficult to find relevant information they based on the investigation of subject locality in the
really need [1]. taxonomic structure. In particular, the influence of locality
This is the so-called problem of information overload. comes from the subject’s taxonomic semantic (is-a and part-
With respect to e-learning, the increasing number of of) relationships with other subjects.
educational resources deployed online and the huge number
of messages generated from online interactive learning (e.g., C. Topic Specificity
Blogs, emails, chat rooms) also lead to the excessive
Issn 2250-3005(online) September| 2012 Page1330
3. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5
The topic specificity of a subject is performed, based on
the user background knowledge discovered from user local
information. User background knowledge can be discovered
from user local information collections, such as a user’s
stored documents, browsed web pages, and
composed/received emails. The ontology constructed has
only subject labels and semantic relations specified. we
populate the ontology with the instances generated from user
local information collections. We call such a collection the
user’s local instance repository. The documents may be
semistructured (e.g., the browsed HTML and XML web Fig. 1 Displaying Constructed Ontology
documents). In some semistructured web documents,
content-related descriptors are specified in the metadata
sections. These descriptors have direct reference to the
concepts specified in a global knowledge base. These
documents are ideal to generate the instances for ontology
population. When different global knowledge bases are used,
ontology mapping techniques is used to match the concepts
in different representations. The clustering techniques group
the documents into unsupervised clusters based on the
document features. These features, usually represented by
terms, can be extracted from the clusters. The documents can
then be classified into the subjects based on their similarity. Fig. 3 Displaying Constructed Ontology
Ontology mapping techniques can also be used to map the
features discovered by using clustering and classification to
the subjects, if they are in different representations.
D. Analysis of Subjects
The exhaustivity of a subject refers to the extent of
its concept space dealing with a given topic. This space
extends if a subject has more positive descendants regarding
the topic. In contrast, if a subject has more negative
descendants, its exhaustivity decreases. Based on this, we
evaluate a subject’s exhaustivity by aggregating the semantic
specificity of its descendants where Subjects are considered
interesting to the user only if their specificity and Fig. 4 Displaying Constructed Ontology
exhaustivity are positive. A subject may be highly specific
but may deal with only a limited semantic extent. 5. SYNONYM HANDLING
When we handle the retrieved ontology keywords we
4. RESULTS would drag the semantic relationships between the instance
The concept of this paper is implemented and sets of subject headings. Although we get the results related
different results are shown below, The proposed paper is to user knowledge but there may be a chance of losing data
implemented in Java technology on a Pentium-IV PC with 20 because of the synonyms. Sometimes synonyms of
GB hard-disk and 256 MB RAM with apache web server. keywords may give us the results better that user expected.
The propose paper’s concepts shows efficient results and has
been efficiently tested on different Datasets. The Fig 1, Fig 2, For this reason synonyms of keywords retrieved and
Fig 3 and Fig 4 shows the real time results compared. maintained later by using all these words we form the
instance sets and retrieve more subject headings from LCSH
and add to LIR.
We can derive the probability of the results before
synonym handling case and after synonym handling case. For
example if we got M words without synonym case, and the
probability is P1. For the synonym case if we got N words,
and calculated probability is P2. If we compare these two
probabilities, definitely
P1 < P2 (M<N)
Fig. 1 Computation of Positive and Negative Subjects.
Issn 2250-3005(online) September| 2012 Page1331
4. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5
Finding the right direction for searching ontology [2] T. Tran, P. Cimiano, S. Rudolph, and R. Studer, “Ontology-
related words is difficult. Ontology is vast and there could be Based Interpretation of Keywords for Semantic Search,” Proc.
many directions the user may not able to find the relevant Sixth Int’l Semantic Web and Second Asian Semantic Web
results of interest according to him. The problem becomes Conf. (ISWC ’07/ ASWC ’07), pp. 523-536, 2007.
bigger if we consider the synonyms of the words. To find the [3] C. Makris, Y. Panagis, E. Sakkopoulos, and A. Tsakalidis,
“Category Ranking for Personalized Search,” Data and
more related suitable synonyms and words we find the Knowledge Eng., vol. 60, no. 1, pp. 109-125, 2007.
probability of result set for each synonym and compare them [4] S. Shehata, F. Karray, and M. Kamel, “Enhancing Search
with the existing results. We consider only the synonyms that Engine Quality Using Concept-Based Text Retrieval,” Proc.
gives more results according to user and the user interest. IEEE/WIC/ ACM Int’l Conf. Web Intelligence (WI ’07), pp.
With this approach we can refine the search. 26-32, 2007.
[5] A. Sieg, B. Mobasher, and R. Burke, “Web Search
If we could drag the relationship between the Personalization with Ontological User Profiles,” Proc. 16th
resultset and the no of synonym words added, through ACM Conf. Information and Knowledge Management
probability then we can predict the results for other cases. (CIKM ’07), pp. 525-534, 2007.
[6] D.N. Milne, I.H. Witten, and D.M. Nichols, “A Knowledge-
This helps to formulate the analysis. By taking some small Based Search Engine Powered by Wikipedia,” Proc. 16th
cases we can do that and it helps to solve and predict ACM Conf. Information and Knowledge Management
complex cases. (CIKM ’07), pp. 445-454, 2007.
[7] D. Downey, S. Dumais, D. Liebling, and E. Horvitz,
6. CONCLUSION “Understanding the Relationship between Searchers’ Queries
In this project, an ontology model is proposed for and Information Goals,” Proc. 17th ACM Conf. Information
representing user background knowledge for personalized and Knowledge Management (CIKM ’08), pp. 449-458, 2008.
web information gathering. The model constructs user [8] M.D. Smucker, J. Allan, and B. Carterette, “A Comparison of
Statistical Significance Tests for Information Retrieval
personalized ontologies by extracting world knowledge from Evaluation,” Proc. 16th ACM Conf. Information and
the LCSH system and discovering user background Knowledge Management (CIKM ’07), pp. 623-632, 2007.
knowledge from user local instance repositories. [9] R. Gligorov, W. ten Kate, Z. Aleksovski, and F. van
A multidimensional ontology mining method, exhaustivity Harmelen, “Using Google Distance to Weight Approximate
and specificity, is also introduced for user background Ontology Matches,” Proc. 16th Int’l Conf. World Wide Web
knowledge discovery. In evaluation, the standard topics and a (WWW ’07), pp. 767-776, 2007.
large testbed were used for experiments. The model was [10] W. Jin, R.K. Srihari, H.H. Ho, and X. Wu, “Improving
compared against benchmark models by applying it to a Knowledge Discovery in Document Collections through
common system for information gathering. The experiment Combining Text Retrieval and Link Analysis Techniques,”
Proc. Seventh IEEE Int’l Conf. Data Mining (ICDM ’07), pp.
results demonstrate that our proposed model is promising. 193-202, 2007.
Sensitivity analysis was also conducted for the ontology AUTHORS
model. In this investigation, we found that the combination Mohd. Sadik Ahamad received the B.E.
of global and local knowledge works better than using any degree in computer science Engineering
one of them. In addition, the ontology model using from Muffakham jha College of
knowledge with both is-a and part-of semantic relations Engineering and Technology, Osmania
works better than using only one of them. When using only University in 2008 and M.Tech. Software
global knowledge, these two kinds of relations have the same engineering from kakatiya institute of
contributions to the performance of the ontology model. technology and science, affiliated to
While using both global and local knowledge, the knowledge Kakatiya University in 2012. During 2009-2010, he worked
with part-of relations is more important than that with is-a. as an Asst. Prof in Balaji institute of technology and science.
The proposed ontology model in this project provides a
solution to emphasizing global and local knowledge in a
single computational model. The findings in this project can S. Naga Raju Assoc. Prof. of Department
be applied to the design of web information gathering of computer science engineering, kakatiya
systems. The model also has extensive contributions to the institute of technology and science,
fields of Information Retrieval, web Intelligence, Warangal, researcher in web mining,
Recommendation Systems, and Information Systems. member of ISTE, B.Tech. from Amaravati
Synonyms will give us more directions to choose user University, M.Tech from JNTUH.
interests. It refines the search.
REFERENCES
[1] R.Y.K. Lau, D. Song, Y. Li, C.H. Cheung, and J.X. Hao,
“Towards a Fuzzy Domain Ontology Extraction Method
for Adaptive e- Learning,” IEEE Trans. Knowledge and
Data Eng., vol. 21, no. 6, pp. 800-813, June 2009.
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