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Ontology Development Through Idea Maps Using Text message Indices

Abstract- Ontology operates as a program for knowledge sharing and explanation to represent a particular website in the form of customized web information gathering. While producing those ontologies for a specific domain, it emphasizes the knowledge bottom over the global information than the local in information. On this project represent a personalized ontology system for a specific area. Data mining is chosen as a area to represent its ideas and scope internationally. This technique is developed by comparing the pre-existing ontologies on Data Mining and merging the essential features associated with it. Finally the ontological model developed for the area Data Mining is symbolized as conceptual map using protege. The conceptual map helps in identifying the associations between the concepts based on the semantics of the conditions. Theory map can maintain various different forms. Among that we represent the idea map for data mining in Onto-Graph.

Index Terms- Concept Map Mining, Strategy Map generation, Text message Mining.

1. INTRODUCTION

Various tools and techniques are being used in the improvement of the education field to attain the higher results and quality. Data Exploration is the practice of using visualization ways to find unforeseen romantic relationships between data tips or pieces of factors in a large databases. Visualization techniques may also be put on information that has already been known. The purpose of any visualization to be used within an educational framework is to assist in the training of some knowledge (idea, concept, fact, algorithm, relationship). In order to attain visualization it must make connections between your knowledge learner and the knowledge being shown. Conceptual

structures such as principle maps, topic maps and conceptual graphs offer with organizing, producing and visualizing the domain knowledge in Web based educational systems (WBES)[1].

Concept maps are anticipated in order to beat the shortcomings of head map. Concept Maps are graphical representation of knowledge that are comprised of ideas and the associations between them. Usually principles are encapsulated in group or boxes. The relationship between concepts is articulated in linking phrases, e. g. , "gives go up to", "ends up with", "is required by, " or "contributes to". Concept map uses the triple form concept-link-concept. Concept mapping is a examined, intuitive, low entry-cost way of knowledge capture and composition. In Idea map principles are symbolized in a hierarchical fashion with inclusive, most basic concepts at the top of the map and the more specific, less standard concepts arranged at the bottom level.

Concept and web ontology terms signify the same domain name knowledge. Strategy map includes the nodes and product labels and Web Ontology Terminology (OWL) have classes, situations and properties.

Figure 1. 1 Correspondence of Main Ontology Elements To Notion Map

The article is further organized the following. In section 2 related works on conceptual map are narrated in a nutshell. In section 3 the keyword removal and strategy map era is presented. In Section 4 the analysis and implementation methods are explained. The results and discussions and the conclusion are briefly discussed in section 5.

2. RELATED WORKS

The main aim of this chapter is to spell it out the theoretical foundations and relevant qualifications of principle map creation. It also brings out the various meanings of ontology an overview of keyword extraction, ontology creation, and principle maps the key aim of the task is to develop the site ontology for Data Mining in order to supply the knowledge base in that domain. At this time it is vital to have a glimpse about the pre-existing similar kind of ontology and grasp the knowledge bottom part on that ontologies. There are various studies done on the concept of website ontology especially on Data Mining.

Ontology

Ontology is a "formal, explicit specification of a shared conceptualization"[2]. It really is formally represents knowledge as a couple of concepts in just a site, and the connections between pairs of principles. It could be used to model a domain and support reasoning about principles. It provides a distributed vocabulary, which is often used to model a area, that is, the kind of objects and/or concepts which exist, and their properties and relationships.

Visualization

A good visualization certainly must do more, but these requirements are useful to attract the brand between lots of things that tend to be called visualization and that which you consider visualization in this field[3].

  1. Based on (non-visual) data. A visualization's goal is the communication of data. That means that the info must result from something that is abstract or at least not immediately noticeable (like the within of our body). This rules out photography and image handling. Visualization transforms from the unseen to the apparent[4].
  2. Produce an image. It may appear obvious that a visualization has to produce an image, but that's not always so clear. Also, the aesthetic should be the primary method of communication, other modalities can only just provide more information. If the image is merely a small area of the process, it isn't visualization[5].

Concept Map

concept map is a diagram that depicts suggested relationships between concepts. An idea map typically presents ideas and information as boxes or circles, which it links with labeled arrows in a downward-branching hierarchical structure. Concept maps are ways to develop reasonable thinking and study skills by revealing connections and supporting students observe how individual ideas form a larger whole. Concept maps were developed to enhance important learning in the sciences. A well-made theory map grows within the context frame described by an explicit "focus question", while a mind map often has only branches radiating out from a central picture[6].

Concept Map History

Concept Maps (CM) were created by Joseph Novak in an effort to assess children's knowledge of science with visual tools to organize and represent knowledge (Novak & Gowin, 1984)[7]. Inside a CM, principles are represented in containers that are linked by labeled romantic relationships; two related ideas (including their website link) form a proposition or semantic device. Principles are also established hierarchically such that more general concepts are located higher on the map and specific ideas such as good examples can be found lower. Novak defines a concept as "a recognized regularity in occasions or objects", or "information of incidents or objects" designated by way of a label. A thought by itself does not provide meaning, but when two principles are connected using linking words or phrases, they form a significant proposition.

Figure 2. 1 Concept Map Using Tools

Kuo-En Chang et. al. , [8] are suffering from the Effect of Idea Mapping to improve Text Understanding and Summarization. Graphic strategies, such as visual organizers and knowledge maps, have proved helpful for words learning, certain important program issues such as surface control and cognitive overload have yet to be resolved. The authors tested the learning ramifications of a concept-mapping strategy. They designed three concept-mapping approaches-map correction, scaffold fading, and map generation-to determine their effects on students' word comprehension and summarization expertise. The experimental results exhibited that the map-correction method improved text comprehension and summarization skills and that the scaffold-fading method facilitated summarization capability.

Nian-Shing et. al. , [9]. Chan have developed the Mining e-Learning site notion map. Recent researches have demonstrated the importance of concept map and its own versatile applications especially in e-Learning. For example, while building adaptive learning materials, designers need to make reference to the concept map of a subject domain. Furthermore, concept maps can show the complete picture and key knowledge about a subject domain name. Research from books also shows that visual representation of domains knowledge can decrease the problems of information overload and learning disorientation for learners. However, engineering of concept maps typically relied upon domain experts before; it is a time eating and high cost task. Concept maps creation for appearing new domains such as e-Learning is even more difficult because of its ongoing development dynamics. The aim of this newspaper is to create e-Learning domain idea maps from academics articles. The authors have adopted some relevant journal articles and meeting papers in e-Learning site as data options, and applied text-mining techniques to automatically construct concept maps for e-Learning domains. The constructed principle maps provides a useful reference point for research workers, who are new to the e-Leaning field, to review related issues, for teachers to create adaptive learning materials, and for learners to understand the whole picture of e-Learning site knowledge.

A system is developed to understand the whole process of automatic principle map engineering for e-Learning website. These processes are needed only one time for constructing notion map database.

Clariana. B et, al. , [9] have developed A Computer-Based Approach For Translating Word into Principle Map-Like Representations. Essays, strategy maps give a visual and all natural way to spell it out declarative knowledge connections, often providing a clear measure of student understanding & most strikingly, highlighting university student misconceptions. This article reveals a computer-based methodology that uses concept-map like Pathfinder network representations to make visual students' written words summaries of biological content. A software utility called ALA-Reader was used to translate students' written content material summaries of the heart and circulatory system into organic proximity data, and then Pathfinder PCKNOT software was used to convert the proximity data into visual PFNets. The validity of the ensuing PFNets as sufficient representations of the students' written content material was considered by simply requesting the students and also by evaluating the correlation of human being rater scores to the PFNet agreement-with-an-expert results. The concept-map like PFNet representations of texts provided students (and their instructor) with another way of thinking about their written words, especially by highlighting accurate, incorrect, and lacking propositions in their wording. This paper provides an overview of the strategy and the pilot experimental results. The exact poster session will in addition demonstration the free ALA-Reader software and will also how to procure and use PCKNOT software.

Method and Tools

Twenty-four graduate students who are experienced doing teachers signed up for an educational assessment course used Ideas software to generate concept maps on the structure and function of the individuals heart while researching this issue online. Later beyond class, using their notion map they had written content material summaries as a precursor for the in-class activities of credit scoring the idea maps and text summaries (essays). In class, students reviewed multiple scoring solutions and then working in pairs, scored all of the text summaries by using a 5-point rubric that focused on three areas, content, style, mechanics, and overall.

Tools:

  1. ALA-Reader software
  2. PCKNOT software
  3. Comparing text ratings (from human being raters)to the ALA-Reader/PFNet content material scores.

In this pilot analysis, graduate students used Ideas software to make strategy maps while exploring the structure and function of the human heart and soul online, these concept maps were used to create content material summaries, and then your content material summaries were translated into notion map-like representations using computer-based software tools. The findings suggest that this process captures some aspects of knowledge content and/or process knowledge within the students' word summaries. The concept-map like PFNet representations of texts provides students (and their teacher) with another thought process about their written word and their research content knowledge, especially by highlighting appropriate, incorrect, and absent propositions. Given a little thought, there are multiple ways that this process can be utilized instructionally. For example, one of the next to term goals is to embed the text-to-map system into writing software and to use the procedure for answer judging (in accordance with an expert) of expanded constructed response items in online instruction[10].

3. KEYWORD Removal AND Strategy MAP GENERATION

A good concept map has only relevant concepts (a recognized regularity in incidents or items, or documents of situations or objects, designated by a label), linked by linking words into coherent propositions. On deciding what concepts relating to a thought map, and on linking them properly the author's reflection is necessary. Concept maps have been used to aid reading and writing activities, what is known as Text message Principle Mapping (TCM). The activities usually consist on summarizing the key ideas in a bit of text message, and there are 3 ways of doing it: Creating a idea map from scratch, fixing a recently built idea map and studying a concept map. In the first activity the students build a concept map with no support, in the second activity the professor builds a map that has some errors and/or absent information that the students have to fix, and in the final activity the students study a concept map built by the educator which summarizes the text. All activities have been shown to enhance the students' understanding on the readings' subject areas. Concept Map Mining:

Amount 3. 1 CMM Process

Concept Map Mining is thought as the extraction of concept maps from text that are useful in educational framework. Its purpose is to provide new ways to visualize the data expressed in the text for human usage. The CMM process consist on identifying the concept in a bit of content material and the linking words that hook up them. It has three sub-task that are: Concept Extraction, Relationship Extraction and summarization. The first job aims to recognize every possible strategy in the next goals to find all possible relationships between the previous concepts and the 3rd step is made up on creating a reduced version of the map that summarizes this content, preventing redundancy and making the most of coverage.

This Idea Map Mining (CMM) is: The automated extraction of strategy maps from essays for educational purposes, and offered the analysis of the gold standard constructed for the purpose of assessing the algorithms that will implement the task. The main goal of the examination is to get a knowledge on the characteristics of the concept maps produced by human being annotators when asked to create a summary of a bit of text. Such habits will inform the look of the automated algorithms that will apply CMM.

4. IMPLEMENTATION AND EVALUATION

The main objective of the paper is generating the idea map from the Web Ontology Terminology (OWL) ontologies. Existing ontologies which already are available on the web pages are used as the input. Web ontology Language (OWL) gets the classes and properties, data type properties and object properties.

The importance of automatic methods to enrich knowledge bases from free text message is recognized by the knowledge management and ontology areas. Creating a domain knowledge basic is an expensive and time consuming task, and static knowledge bases are difficult to maintain. This is especially true in the site of online training. Domains ontology is central of the data basic. This research focuses mainly on the site model and describes a semiautomatic technique and tool, to build domain ontologies from English text.

Concept maps are can make the structure of a body of knowledge much more significant for human users than other types of knowledge representation. Hence, easily validated and enriched with a area expert. Concept maps also foster important learning and index phrases at a fine-grained level, which is necessary for efficient indexing and retrieval. To be able to promote interoperability and reuse, principle maps go through an export process that outputs lightweight website ontology.

The aims of the research work are:

  • To present a overview of keyword extraction and ontology creation.
  • To extracting the keywords automatically from given word using java coding in eclips.
  • To inspecting the extracted keywords and build ontology physically.
  • To propose a automated theory map for ontology creation from text message.
  • To view the concept map using OWL API 3. 4. 2 (Protege).

Figure 4. 1 Overview of Keyword Extraction

Figure 4. 2 Using Keyword Generating the idea Map in OntoGraf Tool

5. CONCLUSION

A amount of improvements and extensions are possible. We wish to enrich the keyword removal with new buildings and explore different ways of expressing habits. Moreover, further complete ontology and concept map are need to build up automatically. Additionally, the different types or structure documents aren't only converting wording document into ontology in future other framework documents also convert into ontology automatically

The proposed construction for generating the idea map from the OWL ontologies to be able to generate the concept map in the very effective manner. This is the main advantage of this proposed platform. This framework is suitable to generate the concept map anyway amount nodes upto the utmost of fifty nodes. The number of nodes needs to be increased and make the possible to view the more details and the relationship between the ideas. In future, more refinement and augmentation will be added in the concept map generating software. OWL document could be changed independently using their company structure tool. The visualization of the idea map needs to be increased to enhance the clear visual demonstration of the ideas and marriage.

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