an automated approach to extract domain ontology 4.pdf · e-learning system e-learning is ......

Download An Automated Approach to Extract Domain Ontology 4.pdf · E-Learning System E-learning is ... Aware…

Post on 30-Aug-2018

212 views

Category:

Documents

0 download

Embed Size (px)

TRANSCRIPT

  • 34

    Chapter 4

    An Automated Approach to Extract Domain Ontology for

    E-Learning System

    E-learning is becoming a hot area in the field of both online and offline

    education. E-learning deals with the interaction between the teacher and learner on the

    basis of knowledge possessed by the learner. Aware about the learners knowledge

    level, the teacher can easily provide the required lessons to the student through the

    online medium such as Internet. Adaptive learning is such an educational method

    which uses computers as an interactive method. It also tailors the learning materials

    based on the learners knowledge level. In this chapter an automated approach to

    extract domain ontology is designed with the objective of the enhancement of the

    efficiency of adaptive e-learning.

    4.1 ONTOLOGY AND E-LEARNING

    Ontologies have become a key concept for providing more relevant lessons to

    the learner than other means. Ontologies are established for information sharing and

    are extensively used as a means for conceptually structuring domains of interest.

    Ontologies help us to describe, develop, annotate and relate the educational resources,

    which in turn will help in the retrieval of more relevant resources for the learners.

    Ontology can be created by a domain expert and embedded into an e-learning system

    or it can be automated and embedded in to e-learning system. Automation of

    ontologies will reduce the human intervention and also the time required for ontology

    creation. The chief advantage of the proposed approach is automated ontology

    construction through concept map extraction. It is effectively achieved through the

    use of association rule mining and sequential pattern mining algorithms. The

    constructed domain ontology is applied to the e-learning system so that the real-time

    application of the proposed approach is discussed.

  • 35

    Figure 4.1: Sample ontology for E-learning

    Database

    Active DB

    Cloud DB

    Data Structures

    DB languages

    DB systems

    DBMS

    DBA

    DB developers

    DB design

    DB modelling

    OO model

    DB model

    Data warehouse

    DB machines

    Hierarchical

    Relational

    Network

    Entity-relational

    QL

    DML

    DDL

    Is a

    Is a

    Is a

    Part of

    Union of Union of

    Has

    Has

    Part of

    Part of

    Is a

    Is a

    Has

    Has

    Has

    Has

    Is a

    Is a

    Is a

    Part of

    Union of

    Part of

    Has

    Part of

    Union of

    Part of

    Part of

    Part of

  • 36

    Figure 4.1 shows a sample structure of an ontology constructed by domain

    experts for the e-learning system. Though the structure is a basic graph like structure,

    we incorporate relations with each node present in the ontology. A node is a topic

    related to the domain that is considered for the construction of e-learning system.

    4.2 ONTOLOGY CONSTRUCTION

    The main objective of the proposed approach is to construct an ontology for an e-

    learning system which fulfills the needs of clients. The client mentioned in the

    approach is related to the student or person who makes use of the e-learning system.

    The ontology is listed with a detailed association between the nodes or the topics. The

    ontology construction undergoes a series of developing steps to ensure that the e-

    learning system is an effective one. The ontology is constructed from a text corpus,

    which contain a number of documents regarding a particular domain. So, the ontology

    has to be created based on the above specified domain. The main steps in the

    construction of an ontology are:

    Processing the documents

    Outline the domain ontology

    Concept Processing (Extraction of concepts from the domain)

    Creating concept maps

    The above four steps serve as the main components of the proposed approach.

    These processes have the virtue of producing an effective ontology for the learning

    system. Based on these steps, an automatic ontology construction method is provided.

    The proposed approach derives a specific algorithm to give weightage to all the nodes

    and to provide association between the nodes. The nodes are assigned their inter-

    relationships through a mutual association function. The different document

    processing methods will help to extract the key features from each document. The key

    features are then associated together to form the concepts and from the concepts, an

    effective concept map is created for the e-learning system. Thus, a query from a user

    is used to extract a concept map regarding that query.

  • 37

    Figure 4.2: Ontology construction

    Figure 4.2 depicts the block diagram for the construction of an ontology for

    the specified e-learning system in our proposed approach. In the succeeding sections,

    the proposed approach in discussed in detail.

    4.2.1 DOCUMENT PROCESSING

    The initial part of the ontology construction is to process the documents to

    extract the keywords from the documents. The text corpus is selected and the

    documents from the corpus are extracted for the processing which is done by applying

    two basic document processing steps. Initially a stop word removal process removes

    all the non-profitable words from the documents. Once the stop word removal is

    finished, a stemming algorithm (Willet, 2006) is applied to extract the keywords in

    their root form. The keywords from the documents are then stored in an array by

    making sure that no words are repeated words. The stored keywords are then

    transferred to the concept extraction phase.

    For example: Consider two statements from the text corpus

    Database is a collection of related information. Data in a database are stored in

    the form of tables.

    Text corpus

    Process

    documents

    Outline domain

    ontology Concept extraction

    Concept map

  • 38

    The stop words are: is, a, of, from, are, in, the.

    Keywords extracted: Database, Collection, Related, Information, data, database,

    stored, tables.

    Stemming: Collection - Collect

    Related Relate

    Tables - Table

    4.2.2 OUTLINING DOMAIN ONTOLOGY

    The procedure of the ontology construction should be specific and transparent

    as we define the e-learning system as a user friendly one. In this section, the different

    steps that are needed for the efficient construction of the ontology are defined. The

    basic structure of the domain ontology can be presented as in Figure 4.3.

    Figure 4.3: Outline of domain ontology construction

    Concept extraction

    Redundancy check

    Dimensionality reduction

    Deriving associations

    Creating concept map

    Ontology construction

  • 39

    The outlining of the structure of the ontology should be precise, because

    ontology is a domain specific one. The main concentration is needed in the concept

    extraction phase. The concept should be associated to more concepts and it should

    possess an individual existence. So, the redundancy in the concept should be

    identified to ease the process of execution. The other major part is regarding the

    dimension of the concept set. For high dimensional concept set, the dimension should

    be reduced to make the associations more rigid and precise.

    4.2.3 CONCEPT PROCESSING

    A concept is defined as a keyword or set of keywords that defines a common

    topic as reference. So, the purpose of concept processing step is to identify such

    concepts from the set of keywords, which is already extracted. Let K be the set of n

    keywords defined by,

    The set K includes the keywords from all the documents. Now we process

    each keyword to find the concept. Each keyword is selected and processed with other

    keywords to find the association between them. Initially, a sorting process is applied

    to the set of keywords based on their frequency. The most frequent keywords are

    selected as top priority keywords. These top priority keywords are processed initially

    for concept extraction. The frequency of each keyword k is calculated based on their

    presence in the document present in the text corpus.

    The frequency is calculated as the number of keywords ( present in the

    document ( to the total number of keywords (N(k)) in . Now the set K is

    reformatted with the most frequent keywords in the descending order of their

    frequency values. We adopt a sentence level windowing process, in which the

    window moves in a sliding manner. The text window formed is four term window

  • 40

    which enclosed in a sentence. As the window slides, the words enclosed in the

    window are selected for association calculation. The association is calculated as,

    ( ) |

    The association between two keywords is obtained through the probability of

    occurrence of the keywords. A conditional probability is adopted for finding the

    relation between the keywords. The value of the association between the keywords is

    used to extract the concept. If the association value is high, it is considered as a

    concept. The process is continued upto the last document in the text corpora. A

    threshold value is set for making

Recommended

View more >