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Table 1. Data model in topic maps
Statement: The author of http://www.w3.org/RDF is Tim Berners-Lee
Topic
Association
Topic/Type
Tim Berners-Lee
Creates
http://www.w3.org/RDF
show where information about a topic can be
found (similar to an index). Occurrences can also
have types, such as user-created content (UCC),
podcasts, wikis, music videos, blogs, tutorials,
etc. about http://www.w3.org/RDF.
Topic Maps and RDF are two available data
models in the Semantic Web. As illustrated in
Table 2, Topic Maps and RDF are different from
user, information, and system perspectives.
Both Topic Maps and RDF use URI as an
identifier. When systems use the same URI to
refer to different resources, it creates confusion.
For example, systemA uses http://www.ibm.com/
company to refer to IBM's home page, while sys-
tem B uses the same URI to refer to IBM. When
these two systems try to exchange data, they cannot
because of their different usages of the same URI
(Pepper & Schwab, 2003). While RDF does not
have a mechanism to cope with this confusion,
Topic Maps provide a subject identifier and subject
indicator to resolve this confusion. Users cannot
rely on names because of synonym, homonym,
and multiple language problems. To resolve these
issues, users need to use identifiers that are clear
both to humans and machines. A subject identifier
is an URI used by a machine to identify a subject,
and a subject indicator is information used by hu-
mans to identify a subject. The topic “apple” can
be identified by a machine using http://psi.fruit.
org/#apple. A subject indicator about “apple” can
be used for a human to identify it. Both subject
identifier and indicator refer to the same subject
in the real world.
Topic Maps provide rich representations of
a topic by using three different kinds of topic
characteristics: topics, associations, and occur-
rences. RDF has only one way to make assertions
about things: triple (subject, predicate, object),
and triplet notation is not expressive enough
(Schaffert, 2001).
One of the main differences between Topic
Maps and RDF is the structure of the representation
Table 2. Differences between topic maps and RDF
Topic Maps
RDF
User
Search and Browse
based on explicitly
shown semantic
relationships
Search based on implicit semantic relation-
ships
Information
Thing
Subject
Resource
Symbol
Topic
Node
Structure
Topic, Association,
Occurrence
Subject, Predicate, Object
System
Syntaxes
XTM, HyTM, LTM
RDF/XML,N3
Data Models
Topic Maps
RDF
Constraints
TMCL
RDF Schema,
DAML+OIL, OWL
Note: XTM (XML Topic Maps), HyTM (HyTime Topic Maps), LTM (Linear Topic Map Notation), N3 (Notation 3)
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