nithyothsava

Case study of GEM

2006-11-07T02:53:00.000-08:00

a controlled vocabulary into an ontology: the case of GEM

Abstract

The prevalance of digital information raised issu
ereregardinthe
suitability of conventional library tools for organizing information. The multi-dimensionality of digital resources requires a more versatile and flexible representation to accommodate intelligent information representation and retrieval. Ontologies are used as a solution to such issues in many application domains, mainly due to their ability explicitly to specify the semantics and relations and to express them in a computer understandable language. Conventional knowledge organization tools such as classifications and thesauri resemble ontologies in a way that they define concepts and relationships in a systematic manner, but they are less expressive than ontologies when it comes to machine language. This paper used the controlled vocabulary at the Gateway to Educational Materials (GEM) as an example to address the issues in representing digital resources. The theoretical and methodological framework in this paper serves as the rationale and guideline for converting the GEM controlled vocabulary into an ontology. Compared to the original semantic model of GEM controlled vocabulary, the major difference between the two models lies in the values added through deeper semantics in describing digital objects, both conceptually and relationally.
Introduction
Ontologies, as a form of knowledge representation, are defined as a systematic account of existence, a specification of a conceptualization ( Gruber, 1993a). They represent a domain of discourse, and allow for relationships such as the definition of classes, relations, and functions. Despite their high level of specification of these classes and relationships, ontologies also allow a great deal of flexibility. Among the more flexible possibilities are the sharing and reuse of ontologies, and the ability to accommodate varying descriptive terms. In the context of metadata for digital library objects, ontologies are specified in the form of definitions of representational vocabulary. Conventional representational vocabularies used in libraries include cataloguing codes as embodied in MAchine Readable Cataloguing (MARC), thesauri or subject heading lists, and classification schemes. There is a clear functional distinction between such vocabularies. Thesauri and classifications, on the one hand, are used to represent the subject content of a book, journal article, a film, or any form of recorded knowledge, though they also contain a limited number of terms for document formats and genre. Semantic relationships among different concepts are reflected through broader terms, narrower terms, and related terms in thesauri, and a hierarchical structure in classification schemes. On the other hand, because a thesaurus is a subject content rich vocabulary, it does not handle descriptive data (title, author, publisher, etc.) object well. Libraries use separate representational vocabularies for the descriptive data such as Anglo-American Cataloging Rules 2nd ed. (AACR2) to meet the need for descriptive representation

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The prevalance of digital information resources raised several issues for conventional representational vocabularies. Firstly, digital objects, as those in the Gateway to Educational Materials (GEM, http://www.thegateway.org/) collections, encompass multiple dimensions of characteristics and such characteristics often play important roles for users in search for precise information in an efficient manner. For example, a lesson plan of arithmetic for fifth graders may contain information on pedagogy, class activities, math games, test samples, educational standards for fifth grade mathematics curriculum; as a digital object, it may include text, images, audio/video clips; and it may be a parent object of a collection of child objects or a child object of a parent object. A conventional cataloguing code will be inadequate to describe these details in a lesson plan, for many of these elements do not even exist in the vocabulary. Secondly, different metadata schemes are used to represent digital objects in different domains. When an information repository has heterogeneous digital objects, interoperability becomes the first obstacle to provide access to these resources. A library catalogue system may not be able to translate a record from a museum or a library MARC record can not be recognized by an Internet-based information gateway system. Finally, more and more systems today use agents and other intelligent applications to improve information search performance. While such capabilities are hard to implement directly within the metadata records, it seems to make more sense if we create agents that bear the concept models with deeper semantics. Such effort does not have to start from scratch, but rather, can make use of the knowledge structure and vocabularies in classifications and thesauri and add values to these representations.

Ontologies are in a right position to address these issues. The fundamental difference between an ontology and a conventional representational vocabulary is the level of abstraction and relationships among concept. In this paper, we report the conversion of the GEM controlled vocabulary into an ontology, which is the first step in a larger project towards an ontology-driven information gateway for educational materials. The current study discusses the preliminary planning and steps involved in converting the existing GEM vocabulary to an ontology. The purpose of the conversion is not only to reduce the duplication of effort involved in building an ontology from scratch by using the existing vocabulary, but also to establish a mechanism for allowing differing vocabularies to be mapped onto the ontology. Soergel (1999) drew connections between ontologies and other methods of classification, pointing out that ontological and lexical structures underpin much scientific and scholarly work. He criticises scholars in scientific communities for not communicating more effectively with each other about their classifications, and laments the fact that the intellectual capital of classification schemes and thesauri is often ignored. This project also attempts to avoid this problem. It uses the existing capital represented by the GEM scheme, and makes it available not just for automated processing at GEM, but also to other communities who choose to take up the structure of the ontology. The following sections include an introduction to GEM's background, a literature review of formal ontologies and applications, conversion considerations, and the constructs of GEM ontology.
Background of GEM
GEM is an initiative of the US Department of Education's National Library of Education (NLE) to expand educators' capability to access Internet-based lesson plans, curriculum units and other educational materials ( Sutton, 1999). There are over 14,500 metadata records in the GEM database as of this writing. Its resources come from more than 100 collections, including AskERIC Virtual Library, Math Forum, Microsoft Encarta, North Carolina Department of Public Instruction, and U.S. Department of Education. GEM acquires indexing records through distributed cataloguing that uses a proprietary cataloguing system. The software contains an interface that allows GEM cataloguers to enter metadata under the 15 Dublin Core Metadata Elements (short for Dublin Core or DC) from the Online Computer Library Center (OCLC) and 8 local elements that are designed specifically for the resources covered by GEM (Table 1). Elements with an asterisk in Table 1 describe the subject content of a resource, among which the Subject element allows two types of vocabulary: the GEM controlled vocabulary and the keywords selected by metadata creators. Box 1 shows a typical GEM cataloguing record. Note that the Description, Subject, and Title elements contain information on what the resource is about. In the Subject element, GEM requires metadata creators to include a level one subject from the GEM Subject Vocabulary ( Morgan, 1999). Other elements such as Audience, Grade, and Pedagogy include information on what and whom the resource is for, which can play important roles in narrowing down a search when querying the database. Box 1 shows an example of a GEM metadata record, where the two-levelled subject terms were selected from the GEM Controlled Vocabulary, and the keywords extracted by metadata creators from the resource.

Elements from Dublin Core Elements from GEM
DC.Contributor DC.Coverage DC.Creator DC.Date DC.Description* DC.Format DC.Identifier DC.Language DC.Publisher DC.Relation DC.Rights DC.Source DC.Subject* DC.Title* DC.Type GEM.Audience GEM.Cataloging GEM.Duration GEM.EssentialResources GEM.Grade GEM.Pedagogy GEM.Quality GEM.Standards
Table 1: Data elements for GEM indexing records

·
· Indicates elements that are used to tell what a resource is about.

Title: Eyes on the Sky, Feet on the Ground: Hands On Astronomy Activities for Kids Description: This resource contains hundreds of fun explorations into astronomy as a classroom tool for learning how to theorise, experiment, and analyze data. The activities are fully illustrated and contain detailed, step-by-step instructions as well as suggested discussion topics. Grade Levels: 2 3 4 5 6 * GEM Subjects: Science--AstronomyScience--PhysicsScience--Space SciencesScience--TechnologyKeywords:Orbit, Time, Calendar, Maps, Solar system, Moon, Rotation GEM Audience: Tool For: Teachers Beneficiary: Students Pedagogy: Teaching Method: Hands-on learning Multiple activities Resource Type: Collection Format: text/HTML Relation:Rights Management: Cost: free Date: Record Created: 2000-04-13T01:45:39-5:00 Publisher: Name: Harvard-Smithsonian Center for Astrophysics, High Energy Astrophysics Division Role: onlineProvider Homepage: http://hea-www.harvard.edu/ cataloguing Agency: Name: GEM Version: 3.2 Homepage: http://www.geminfo.org
Box 1. A sample GEM metadata record
The GEM controlled vocabulary is resource-oriented (Figure 1), i.e., it defines the categories (classes) and subcategories (subclasses) of information that are included in educational resources. Among other classes that describe the many physical/medium attributes for a resource, the subject class encompasses a typical curriculum in the US elementary and secondary schools, which has a two level subdivisions--subclass and concept names. Currently, the GEM controlled vocabulary is used as a metadata scheme for representing the multiple dimensions for digital educational objects. The GEM system provides access to these multi-dimensions by using Boolean operators through the search interface. Though this controlled vocabulary integrates the descriptive data elements with subject categories, it is difficult to achieve a higher level of representation of these digital objects (such as games, test samples, and classroom activities in a math lesson plan) and the relationships among and in the digital objects. This difficulty manifests a need for a more systematic abstraction that will explicitly specify the concepts as well as the relationship between these concepts.

Construction of thesaurus

2006-04-29T20:49:00.001-07:00

Construction of thesaurus

In a construction of thesaurus include 8 section this are as follows

1. What is a Thesaurus?

2. Collecting Terms

3. Modifying and Inventing Terms

4. Preferred Terms and Non-preferred Terms

5. Semantic Relations

6. BT, NT, and RT References

7. Scope Notes

8. Thesaurus Displays

Section 1 -What is a Thesaurus?
A thesaurus is a tool for vocabulary control. By guiding indexers and searchers about which terms to use, it can help to improve the quality of retrieval.
Usually, a thesaurus is designed for indexing and searching in a specific subject area. Examples of subject areas covered by thesauri are education, metallurgy, and art and architecture. A thesaurus gives several types of information to indexers and searchers.
Preferred Terms
Obviously, the thesaurus has to indicate which terms indexers and searchers are allowed to use. These terms are called preferred term.
This is a major part of vocabulary control - restricting the vocabulary so that it is easier to predict what words might have been used to index a concept.
Non-preferred Terms
In addition to preferred terms, a thesaurus also needs to indicate some terms that indexers and searchers are not to use.
These terms are called nonpreferred term.
It should be possible to look up a non-preferred term and see what preferred term should be used instead. This will save time and make it less likely that the best preferred term will be missed.

Section 2- Collecting Terms
Sources from which terms can be collected include
Existing lists of terms:
Other thesauri, indexes, dictionaries, glossaries, etc.
Texts from which terms can be extracted
Titles, abstracts, or full texts of indexed items
queries by patrons
People
subject specialists, etc.

Section- 3
Modifying and Inventing Terms
Standardizing the Form of Words
Terms collected should already be nouns or noun phrases. Here are some further guidelines for the form that terms should take in your final thesaurus.
Guidelines examples

Plural for things that can be counted "TUBES"

Singular for "mass" nouns " WOOD"

Section -4 Preferred Terms and Non-preferred Terms
Equivalent Terms
After collecting terms for your thesaurus, you need to decide which are equivalent terms. For purposes of indexing and searching, a set of equivalent terms will all be treated as though they meant the same thing and will be represented by a single preferred term.
Spelling and Synonyms
Sometimes, equivalent terms really do mean the same thing. So, it obviously makes sense to use a single preferred term to represent that one meaning.
A word may have more than one spelling; for example, "AESTHETICS" and "ESTHETICS".
Two different words may have essentially the same meaning; for example, "AUTOMATION" and "MECHANIZATION".
Preferred Terms
Preferred terms serve as focal points where all the information about a concept is collected.
Non-preferred Terms
Non-preferred terms are included in a thesaurus mainly to help users find the appropriate preferred terms. Non-preferred terms may also help to define the scope of preferred terms.
USE/UF
A non-preferred term is normally linked to a corresponding preferred term by a USE reference. The corresponding reference in the opposite direction if UF ("Used For").
For example,
PERIODICALS USE SERIALS
SERIALS UF PERIODICALS
Here the preferred term is "SERIALS" and the corresponding non-preferred term is "PERIODICALS".

Section- 5 Semantic Relations
Why Indicate Semantic Relations?
Indicating semantic relations helps in several aspects of information management:
checking whether a term should be used in indexing a given item or in formulating a given search specification
choosing the correct level of generality in indexing and searching
searching in response to broad inclusive queries
sharing indexing by facilitating translation from one scheme to another
Semantic Relations between Terms
The main semantic relations indicated between preferred terms in a thesaurus are hierarchical relations and non-hierarchical relations.
BT and NT Links
BT and NT links are used to indicate . hierarchical relation In a hierarchical relation, one term is viewed as being "above" another term because it is broader in scope.
In developing a thesaurus, it is often a good idea to work out the hierarchical relations first.

Section- 6 BT, NT, and RT References
What Is the Relationship Between BT and NT?
Normally, BT and NTare "inverse" links. In other words, if X is a broader term to Y, then Y is a narrower term to X, and vice versa.
For example, if a thesaurus contains the entry
PENS BT WRITING MATERIALS
you would expect it also to have the entry
WRITING MATERIALS NT PENS
How Many BT References Can a Term Have?
A thesaurus is usually "polyhierarchical"; this means that a term can have more than one immediately broader term and more than one BT reference. For example,
SOCIAL PSYCHOLOGY BT PSYCHOLOGY BT SOCIOLOGY
Polyhierarchy avoids futile arguments about the "best" broader term to choose.
Some terms in a thesaurus have no broader terms and so no BT references. Such terms are usually fairly broad in meaning, at least within the subject area covered by the thesaurus. For example, in a sports thesaurus, "SPORTS" might have no broader terms.

When To Use an RT Reference
An RT reference is used for non-hierarchical Semantic ralations in a thesaurus. To decide whether there should be an RT reference between two preferred terms X and Y that do not have a hierarchical relation, you can use the following test:
Should an indexer or a searcher considering using X be reminded of the existence of Y?

What Is the Relationship between RT and RT?
Normally, RT is its own "inverse" link type. In other words, if X has an RT reference to Y, then Y should have an RT reference to X. For example, if a thesaurus contains the entry
PENS RT CALLIGRAPHY
you would expect it also to have the entry
CALLIGRAPHY RT PENS

Section -7 Scope Notes
The most common type of guide to applying terms in a thesaurus is the scope note. A scope note is normally preceded by the notation SN.
Scope notes take a variety of forms.

Section- 8 Thesaurus Displays
For any thesaurus display, you may need to make several decisions. These decisions are likely to include
which types of terms will have entries
how to indicate special types of terms
what types of links will be shown to other terms
how many levels of linking will be shown
how to indicate link types
where the linked terms are placed
relative to the entry term
relative to each other

Construction of thesaurus

2006-04-29T20:49:00.000-07:00

Comparison of the Information Retrieval System with the Google website.

2006-01-28T04:16:00.000-08:00

Comparison of the Information Retrieval System with the Google website.
When seeing the model of simple flow of information retrieval system along with the Google website. First we compare the population of documents with the number of websites carrying information all over the world. In the next stage the selected documents are those which we want to select whichever is most easier and most informative carrying with information. In this stage the websites will be indexed first according to their addresses and then it will be compared and analyzed the information which one has came to first and which one came in the last.
The Database of documents representation is the Google website. In this the needy of information will collect the information through the Google website.
From the point of view in taking into account of the user the requests whichever he wants the information. For Ex: We want information on the Information Retrieval System. This will consider as query. In the next stage it will compared the query with the information it had. And then it will give the results in the form of valued information that information will satisfied the user or not but it gives the information. If the user will satisfied with those information the procedure will stopped. If he not satisfied the procedure of retrieving the information will continued by changing the query or information need. This procedure will continued upto the user satisfied with the information.

Canon decreasing extention

2005-11-29T03:22:00.000-08:00

Canon decreasing extention
The canon directs that ‘while moving doun in a chain from its first link to its last ,the extension of the classes or ranked isolates, as the case may be , should decrease and intension should increase at each step’.
Extension and Intension Meaning
The term extension in general means anact of extending or the magnitude, range or distance over which a thing extend . in library classification the term relates to the universe of entities it may said, therefore, that extension of class has for its measure the number of entities or the range comprised in the class or in the ranked isolates. A basic class covers a wide range of subject field which comprisesof alarge number of entities. This range of entities is called .extention while the term intension indicates intensity of the sum of the qualities implied by a general name or class. In library classification the term intension indicates the number of characteristics used in deriving the class or isolate idea from its original universe if the class is of small range the numbers of characteristics that can be predicted would increase. This means extension is quantitative measureof a class and intension is qualitive measure of it.
The canon of decreasing extension is, thjerefore, applied to achain of classes or ranked isolates in one and same chain heaving lineal kinship. Petroleum product agood example of the lineal kinship of decreasing extension – from crude oil to petrol.

Petroleum product (raw material)
Crudeoil
Vaseline
Diesel
Karocine
Petrol

list of librarian

2005-11-10T05:51:00.000-08:00

UNIVERSITY OF MYSORE LIBRARY LIBRARIANS

Name Period
Prif.Nagappa 1917-1918
Prof. Nagasundaramurthy 1918-1933
Hanumantharao G 1942-1952
Murthy K.S 1953-1959
Sharma Rao K.S 1960-1961
Prof. Annaiahgowda 1962-1963
Balasundaraguptha 1964-1965
Deshapande k.S 1965-1969
Prof.Patil P.K
Khan H.A
Raju K.N
Achuthya Rao
Dr.R.T.D. Ramesh gandhi 2002

hi friends

2005-11-09T18:30:00.000-08:00

Today i awaked at 7.30 am. I came to the class at 8.30 and type my seminor topics in lab ,by the time our university students made strike about their problms than iam also participated with then as a representive, so that will not complete my seminar work.so feel very sad

Goodevening

2005-11-07T04:42:00.000-08:00

Today I have wake up at 6.30 am. At 9.30 I visited lab and do some work. I attend all the classes today. Upto 7.30 P. I was in the library. I search some information for my seminor topic of YV sir and NSH sir.

HI FRIENDS

2005-11-06T15:04:00.000-08:00

Hi friends
Today I am very happy because these day no classes, so awaked
6.30 am and I read the colon classification book then 8.30 I went to Saraswatipuram library and read the national library books and some magazines. Afternoon I went to my brother house. In these way pass the Sunday.

LISTENING SKILLS

2005-11-05T04:35:00.000-08:00

To day I come to class at 9.30 then working the seminor work typying in our lab upto 11.30 and next conducting N.S.H Class and next afternoon rejoin the typing work 2.30 to 6.30. Previous night I read one book the topic is Listening skills. so I said the listening skills.

listening skills

The listening skills rating is a self evaluation of the current level of our listening skills as well as what you perceive your partner's listening skills to be.

how to improve our listining skills

The average college students spend about 14 hours per week in class listening co-perhaps, I should say hearing to letchure see if you can our listening skills by following some of the strategies are there such as

1. Focus on content no delivery

2. Maintaince eye contact with the instructor

4. Avoid emotional involvement

5. Avoid distruction

6. Treat listening as a challenging mental task.

OPEN LIBRARY FOR ALL

2005-11-04T03:17:00.000-08:00

COMING: THE OPEN LIBRARY FOR ALL
It will soon allow people to print out genuine-looking pages from a vast on line archive.
Imagine a library where you can find all the books in the first place you look. Imagine you can search, google-style, over their text, and then feel the pages between your fingers, over their text, and then feel the pages between your fingers or see the tea spotches of the first readers perhaps long dead. And imagine doing all of this in your own home. The plan is a book lover’s dream; and the particular book lover intent on creating this open library is Brewster Kahle, known as the digital librarian of the internet.
It is an ambitious project, but he has allies among other technologists and the support of companies such as Microsoft and Yahoo. A consortium of technology companies, libraries and academic institutions has formed the open content alliance, working together to create the open library the future home of these works. Book – scanning itself is a sophisticated technical challenge. The latest-generation scanner used by the project produces books of 16 magapixel pages for a little under 10 cents (about RS. 4.50) a page.
A cradle holds the book open in a 90 degree V-shape while cameras perched overhead and controlled by the scanners central software shoot the high-resolution images Software creates a textversion and the images themselves are collated and use to create beautiful digital books which you can print from any where with the images of the text intact. All that is missing is the smell of old books.
The data is conveyed to the archive’s petaboxes- a bright red computer tall and deeper than a book shelf is filled with pizzabox-shaped computers each box stores 80 tera bytes or the equivalent of about three libraries of Congress.
At the equivalent t of re 4.50 a page the money and support needs to be committed but Mr. Kahke’s supporters have deep pockets and an enthusiasm for the project.

happy ramjon

2005-11-03T08:17:00.001-08:00

I come to hostel very late at the yesterday night. therfore today Iwake up at 9.00 P M . Yesterday I did not writte blog, so Iam very dull today and collected some sources of Y.V.sir seminar .Then tommorow i have read the editoriel of vijaja karnataka [oid newspaper] .editor vishvewara bhattas noorentu mathugalu very fentastic he write to the article of modern city tokiyo. Itis good article .Ilike it

happy ramjon

2005-11-03T08:17:00.000-08:00

hai friends

2005-10-26T06:25:00.000-07:00

Iam created a blog so Iam very happy because enter the online diary