Proc. of National Conference on 195 Advances in Knowledge Management 2010
Topic Map: An Ontology Framework for Information Retrieval
Rajkumar Kannan Department of Computer Science Bishop Heber College(Autonomous) Tiruchirappalli, TN, India. www.bhc.edu.in [email protected]
Abstract - The basic classification techniques for subjects to be arranged in a hierarchy, but organizing information are thesauri, taxonomy also allowing other statements to be made and faceted classification. Topic map is about the subjects. It includes properties such relatively a new entrant to this information as broader term, scope note, synonym term, space. Topic map standard describes how root (top most) term and related term. In complex relationships between abstract short, thesauri provide a much richer concepts and real world resources can be vocabulary for describing the terms than represented using XML syntax. This paper taxonomies do, and so are much more explores how topic map incorporates the powerful tools. As can be seen, using a traditional techniques and what are its thesaurus instead of taxonomy would solve advantages and disadvantages in several several practical problems in classifying dimensions such as content management, objects and also in searching for them. indexing, knowledge representation, constraint specification and query languages in the context Facets can be thought of as different axes of information retrieval. The constructs of topic along which documents can be classified, and maps are illustrated with a use-case each facet contains a number of terms. In implemented in XTM. faceted classification the idea is to classify documents by picking one term from each Keywords: Topic Maps; XML Topic Map; facet to describe the document along all the Topic Map Query Language; Topic Map different axes. This would then describe the Constraint Language document from many different perspectives. Ontology is a model for describing the I. INTRODUCTION world that consists of a set of types, Unable to find the information which properties, and relationship types. Controlled exists in a document object or collection of vocabulary, taxonomy, thesauri, and facets objects is a traditional problem in information are fixed vocabulary languages where as retrieval. There are age-old traditional ontology is a open vocabulary language. techniques that find the result by classifying Topic maps emerged as a way of merging objects through its subjects. The important of electronic indexes. A topic map consists of subject-based classification methods [1] are a collection of topics, each representing some as follows: real world thing or concept, as for example Controlled vocabulary is a set of indexing terms in an index of a book refer concepts terms or subjects used for classification. inside a book through page numbers defined Taxonomy is a subject-based classification in the book index. Topics are related to each technique that arranges the terms in the other by associations, which are typed n-ary controlled vocabulary into a hierarchy. combinations of topics. A topic may also be Thereby, related terms can be grouped related to any number of resources by its together and categorized for efficient search. occurrences. The important benefits [2] are But, search is very limited here as it cannot Separation of topic space from capture several relationships and synonyms, resource space which are required to reduce the search space. No fixed ontology XML syntax Thesauri basically extend taxonomy to describe the world by not only allowing Flexible schema
ISBN 978-81-909732-0-5 Proc. of National Conference on 196 Advances in Knowledge Management 2010
Flexible extension XTM syntax [3] for topics, Professor and In this paper, we will introduce the rajkumar-kannan. fundamental constructs of topic map which are topic, association and occurrences and
II. TAO OF TOPIC MAP Further, different topics in a topic map can Topic map is a network of nodes, not a tree have same name (i.e., duplicates), which hierarchy, representing a collection of topics, taxonomies and thesauri do not allow. To associations, occurrences and scope. (see. resolve this, topic maps use its type, Figure1). occurrences and associations. For example, home page of a web site can be identified by its URI and also through the menu, such as About menu. For example, the topic name, Dr Rajkumar Kannan is constrained to the topic, university only as shown in figure3.
ISBN 978-81-909732-0-5 Proc. of National Conference on 197 Advances in Knowledge Management 2010 results about the city in India by name vocabulary feature does not exist in Tirupathi and all persons whose names are traditional techniques. Tirupathi. If we are interested only about the city, not the persons, we have no way to VI. ASSOCIATION BETWEEN TOPICS describe in these classification systems. Through topic types we can specify An association is a relationship between “Tirupathi” is a “city” where city is a topic topics. Each association plays a role as a type of a topic Tirupathi. Thereby users can member of that association. The roles a topic perform a search such as “find ‘thirupathi’, plays in associations can be typed and scoped but show only ‘places’”. Also, remember [7, 8]. There is no directionality inherent in since the types of topics are themselves topics an association. For instance Figure5 the designer of the topic map can choose illustrates the association “works-for” for the which types to use [6,] professor, rajkumar-kannan. Note that the role played by him is teaching. Traditional classification schemes provide only very little V. OCCURRENCES OF INFORMATION features such as broader-narrower terms. RESOURCES However, topic maps precisely define the Occurrences connect topics to information associations and hence users’ query will resources that contain information about correctly retrieve the required result. them. For example, page number in a book index indicates where the information about
ISBN 978-81-909732-0-5 Proc. of National Conference on 198 Advances in Knowledge Management 2010
REFERENCES [4] http://www.topicmap.org [5] http://www.ontopia.org [1] Lars Marius Garshol, Metadata, Thesauri, Taxonomies and Topic Maps-Making sense of it [6] Ahmed K., TMShare—Topic Map Fragment all, Journal of Information Science, 30(4):378-391, Exchange in a Peer-To-Peer Application. HTML 2004; ISSN 0165-5515. format. [2] Kal Ahmed and Graham Moore, An Introduction to [7] Ahmed K., Topic Map Design Patterns for Topic Maps, IBM Systems Journal, July 2005 Information Architecture. HTML format. [3] Moore G., Pepper S. (ed.), XML Topic Maps [8] http://www.isotopicmaps.org/ (XTM) 1.0 [online], TopicMaps.Org. HTML format.
ISBN 978-81-909732-0-5