SEMPL-A SEMantic PortaL for the LSDIS Lab
Authors
University Of Georgia
author's email address
ABSTRACT
Semantic web technology is intended for the retrieval, collection
and analysis of meaningful data with significant automation
afforded by machine understandability of data. As one
illustration of semantic web technology in action, we present
SEMPL, a semantic web portal for the LSDIS lab at the
University of Georgia. SEMPL uses an ontology driven
approach to provide semantic browsing and querying
information in the Semantic Web area and LSDIS lab. By using
the ontology based information integration technique, SEMPL
can specify the context of a particular piece of research
information, annotate web pages and provide links to
semantically related areas enabling rich contextual retrieval of
information.
domain of application (in our case it is the LSDIS lab domain.)
Whenever a new piece of information is encountered, it is
classified to the appropriate region in the ontology. Given a
query (for example, list all the publications in the LSDIS lab),
the query processor extracts the data from the metabase and
consults the ontology for possible context related information.
Thus with the help of the ontology the query is answered and
relevant information is retrieved. In addition to extracting
relevant information, the portal also provides information about
semantically related topics.
SEMPL has three prominent features.

Semantic Browsing: Semantic browsing enables a user
to browse the ontology. The user can select any
particular node in the ontology, and the related
information is displayed. For example, a user wants to
view information regarding the students in the LSDIS
lab. When a user selects students, a list of students is
displayed. When a user selects a particular student,
information about the student is retrieved. It should be
noted that this information is retrieved from the
ontology, so other relationships that are not directly
related are also retrieved. Therefore the user can not
only view the students profile (name, status, email,
homepage etc.) but also information about projects on
which the student is working, classes taken so far,
publications, and so forth. In addition to information
directly associated with resources, SEMPL also
provides semantically related information. In this case
SEMPL can provide links to other people who were
co-authors with the student on a publication, other
students who worked on the same project with the
student, etc.

LSDIS search: Here the user can specifically search
for a particular entity within the LSDIS domain. A
user can fill out a form that explains the details of the
search, and the search provides a detailed view of the
entity.

Web Search: The user can search for any entity in the
World Wide Web. SEMPL annotates the search,
marking the entity key word as well as other related
key words based on the relationships defined in the
ontology.
Through ontology-based browsing at the schema level, users can
see a clearly organized and easily traversable presentation of all
the content in the portal. Advanced searches based on domain
specific attributes defined in the ontology provide users with
more precise and relevant information than would be provided
with traditional keyword-based searches.
Also, when
documents are viewed, links to other relevant resources are
presented that are based on precisely defined relationship
instances in the ontology. This paper describes features of
SEMPL, its implementation details, and a brief description of
some of the technologies used.
SEMPL is designed in layers allowing for implementation of
future middleware tools such as semantic ambiguity resolution.
Keywords
Semantic Web, Semantic Portal, Ontology, Metadata.
1. INTRODUCTION
Semantic Web technologies are aimed for enabling higher
standards in information retrieval, data analysis, web-search and
navigation. Applying semantics to the data enables a meaningful
form of communication between the user and the provider.
A portal is a web access point. It consists of web pages that act
as a starting point, a gateway to the web, or a niche topic.
Portals traditionally gather information (collect web pages) from
disparate sources. The inherent problem in such aggregation is
that the resources are widely distributed and heterogeneous.
Merely gathering information does not state the context under
which the information is more useful. By adding semantics, the
portal can classify various resources, build relationships
between them and add context within which a given resource is
most useful.
SEMPL is a semantic portal mainly designed for providing
information about LSDIS lab. It uses the ontology driven
approach that has also been used earlier by OntoWeb [1] and
SEAL [2]. SEMPL starts out by extracting information from
various resources pertaining to the LSDIS lab. This extracted
information is in the form of metadata and thus forms the
metabase. An ontology is constructed that is based on the
In In this paper we present the architecture and the system
overview of SEMPL. In the following section we discuss related
work. Section 3. describes the functional blocks of the portal,
section 4 presents a brief overview of the various technologies
used. Finally we conclude with a discussion of future work and
some related issues in section 5.
2. Ontology Description
2.1.1 Purpose of the Ontology
An ontology enables two parties to agree on the basic meanings
of concepts as well as the relationships between them. This
agreement (ontological commitment) is the key to practically all
–of the current approaches to supporting semantics. An agreed
upon meaning for entities and relationships can also provide a
context in which resources may be viewed. In any attempt to
“semanticize” a portal, an ontology ties together the resources
through relationships. These relationships and the entities they
bind enable complex knowledge retrieval that not only can tell
about a resource but also how it is related to other resources in a
given context.
concepts in the ontology. By clicking on the nodes, the related
documents are displayed. Searching and querying are supported
by the query module. The query module receives the queries
from the users and communicates with the database via the
middle layer. In addition, users can search the web in the
context of the portal ontology via the web-search tool provided
by the portal.
3.2 Core Modules
2.1.2 Ontology for SEMPL
For this portal, we have chosen to use the ontology for the
semantic web research community that is available from
semanticweb.org [4]. This ontology provides all of the entity
and relationship types we feel are necessary to model the LSDIS
portal accurately and completely. In addition, we believe using
an existing ontology for this area reinforces the purpose of using
such a mechanism in the first place – agreement on the meanings
of things.
Key entity types of the ontology include:







Person
Organization
Topic
Event
Publication
Project
Product
Each of these entity types breaks down into one or more
instantiable subclasses that allow for more narrowly defined
entities and resources.
Each entity can participate in
relationships with other entities, and these relationships will be
exploited through multiple mechanisms to retrieve explicit and
implicit (more complex) knowledge in the portal.
3. SYSTEM OVERVIEW
In this section we elaborate the general architecture of SEMPL
and explain in detail the functionalities of different modules
involved.
3.1 Architecture
SEMPL architecture is designed in layers allowing for
implementation of future middleware tools such as semantic
ambiguity resolution. The overall architecture and environment
of this project is depicted in Figure 1. The backbone of the
system is the Knowledge Warehouse, i.e. the Ontology and
Database. The latter is the actual storage of the ontology and
instance information while the former is the communication tool
for storing, retrieving, and searching for specific information.
At the front end, general users and the administrator
communicate with the system through the web server. The users
can access the information contained in the portal either by
navigation, querying or web search. The navigation module uses
the browsing capabilities provided by TouchGraph [6] to
display the ontology. Each node of the TouchGraph represents
The first step in the construction of the portal is the development
of the Ontology. The second step is the extraction of
information relevant to the domain. From there the final step is
the semantic presentation of the material. This section describes
the functionalities of the core modules involved at different
stages in the development of the portal.
3.2.1
Freedom
Freedom software developed by Semagix Inc [3] forms the
backbone of the portal. Semagix Freedom combines proprietary
semantic technologies (ontology, link analysis, semantic
metadata management, semantic querying) that make it capable
of supporting robust enterprise-level applications.
The ontology management capability of freedom enables
domain-specific ontologies to be created and maintained with
minimal effort. Ontologies are populated using Freedom’s
unique Extractor technologies. The Extractors are easily
configured and allow knowledge from all types of sources
(internal / external, structured, semi-structured, unstructured) to
be automatically collected, normalized and stored within the
ontology.
In addition to ontology creation and extraction technologies
Freedom also supports metadata extraction and management
from extracted information. Freedom is able to examine content
of all types and utilize the knowledge stored within an ontology
to determine a set of semantic metadata to be associated with
any content item. The semantic metadata may consist of either
terms explicitly contained within the source content, or terms
that can be determined indirectly from the source, using the
relationships contained within the ontology. It is through this
unique, and configurable, semantic enhancement process that
Freedom brings its full power to the metadata extraction
process.
3.2.2
Extractors
SEMPL exploits the capabilities of the Freedom application to
draw together an integrated ontology, metabase, and linked
content sources. These extractors of content agents peruse
through source information and pull specific information from
those sources. In our case, regular expressions are heavily used
in semi-structured web pages to gather desired information.
Extractors have timers that are set to run either once or on a
consistent basis.
3.2.3
Semantic Enhancement Server
Semantic Web content is web content annotated according to
particular ontologies, which define the meaning of the words or
concepts appearing in the content [6]. In order to better tie
together the knowledge in the SEMPL portal with the
information from the Internet all web searches and information
retrieved through those searches are semantically annotated.
The Semantic Enhancement Server (SES) is an extensible
wrapper of several modules from Freedom allowing the user to
annotate and parse documents according to their needs. SES has
the power to annotate information based on concepts, phrase
structure and tags. It can also classify documents and resolve
concept ambiguity.
SEMPL chose an early approach that annotates only on concept
instances. When a user retrieves a web search, all highlighted
results are concept instance names or synonyms. By clicking the
highlighted link the user can view the portal knowledge base of
all related information. As more of the previous LSDIS portal
knowledge is transferred into SEMPL, the ability to better
provide the user with a higher variety of Semantic Information
exists.
3.2.4
Knowledge APIs
With the use of the Freedom APIs, SEMPL has the capability to
access and query the ontology and metabase. Freedom scores the
results of the queries according to configurable measure of
semantic relevance. In order to semantically enhance the
presentation of the data for the user, the Freedom software
allows for hooks into their KnowledgeAPI to pull various
entities, relationships, and their attributes out to use them as
desired.
3.2.5
Knowledge Base
The knowledge base serve as a repository for data represented in
the ontology and the metadata, and it is a necessary and intricate
part of the Freedom system. The Freedom software implements
an architecture that maintains both volatile and non-volatile data
storage. For this reason, it is wrapped by Freedom, and all calls
are done through the software. In order to maintain scalable
efficiency, the non-volatile data storage is a relational database.
The volatile storage held in main memory pares down the data
storage to store only information that can be retrieved and used
by the user. Fast CGI is used to query information. Freedom
allows the developer the choice of strictly using the volatile,
non-volatile, or both storages. There are advantages and
disadvantages from all three options, and they should be
weighed according to the application. When executing main
memory requests the Freedom module Semantic Enhancement
Server (SES) Engine is used.
The storage of information is based on a tree structure where all
Entities, Relationships, and Attributes are classes. Attribute and
Relationship classes then describe entities. Entities, Attributes,
and Relationships are all defined with cardinality constraints.
Entities maintain cardinality constraints. By this is an Entity
class is assigned a cardinality of “one” then there can be at most
only one Entity with that classification. As an example at any
one time only one person could be an Instance under the class
“President of the United States”. By default and in almost all
situations Entity classes have “many” constraints allowing any
number of Entity instances. Attributes also by default maintain
a “many” constraint. If an attribute is set to “one” the only any
Entity with that attribute is only allowed one value for that
attribute.
“Name”, a default attribute, has a cardinality
constraint of “one” to ensure each entity has at most one
“name”. If the Entity class to which the attribute belongs has a
cardinality constraint of “one”, then no two entities can share
the same value for that attribute.
Relationships have the cardinality constraints of “1-1”, “1many”, “many-1” or “many-many”. With a “1-1” relationship
each entity in the relationship is related to exactly one other
entity. As the president of the United Stated there is only one
Vice President. For a “1-many” relationship an entity on left
side of the relationship has many related entities but an entity on
the right side has only the one entity on the left it is related too.
Here, a company has one manager of a facility, but many
workers in that facility. “Many-one” relationships are similar to
“one-many”, but the left side is many, while the right side is
one. To illustrate this more than one employee can manage the
same department but none of the employees manages more than
one department. Finally, a “Many-many” relationship removes
all restrictions on the number of entities any one entity is related
too. Figure 10 provides a screenshot of the freedom knowledge
modeler.
3.2.6
Portal Knowledge Engine
For a modular and robust architecture, we have added our own
Java middle layer called the PortalKnowledgeEngine. It
provides a precisely defined communication layer between the
Freedom knowledge base and the Servlets and other web
components of the portal. PortalKnowledgeEngine makes use
of Freedom’s Java API and its two HTTP-based query engines,
SQS and SES, to access the information in Freedom. All
methods in the middle layer which are available to web
components return the desired information serialized as XML
according to our DTDs. These helps to simplify our web code
by eliminating XML serialization and by reducing many calls to
Freedom’s
APIs
into
one
simple
call
to
PortalKnowledgeEngine. Also, all methods in the middle layer
are completely independent of the specifics of the underlying
Ontology and therefore robust to changes in it.
3.2.7
Navigation Module
In developing a portal, consideration for two key types of users
must be taken into consideration: administrators of data and
browsers of data. Administering data is a continuous process of
reviewing existing information for value, editing existing data,
and entering new information. Browsing the data is the more
restrictive process of querying and reviewing existing
information.
SEMPL’s current design separates those roles in a clear and
distinct pattern. Administrators have access to the Freedom
software directly and use its GUI for all necessary administrative
tasks. Without getting too deep into the capabilities of the
Freedom software, administration of software agents, ontology
editing, instance manipulation, and control of the database
backend.
For users wanting to browse and search the SEMPL portal there
is a two-way approach for success. When browsing and
searching data through the a browser a Touchgraph [5] applet is
used in the upper portion of the page while html code is
presented in the lower portion of the page
Touchgraph is used to visualize all is-a relationships of the
ontology. Touchgraph is used as it provides an “easy on the
eyes” approach of visualizing interrelated concepts. Using
visual images of interconnected nodes allows the user to
quickly, and efficiently traverse through a network of data. In
order to maintain a very simple view of information SEMPL
only shows the nodes directly related to the selected node. The
history trace is kept in a side panel to move backwards as
needed. Figure 2 shows the screenshot of the SEMPL main page
SEMPL approaches entity instance viewing differently. Specific
instance information is presented in the lower portion of the
browser. As the user traverses the ontology path instances for
each selected node are presented to the user as well as the ability
to search within that selected node. Instance visualization is a
combination of all attributes, all defined relationships, and any
semantic relationships found. From this links are maintained for
a user to mover to other related instance information.
In data-intensive portals traversing through the flow of
information can be a tedious process. For this reason, SEMPL
includes a search engine that allows querying for information in
the ontology as well as the web itself. Figures 3 and 4 provide a
screenshot of the browse mode in SEMPL.
3.2.8
Query Module
The portal enables a user to search for resources within the
portal’s knowledge base through dynamically created forms that
are specific to the type of resource for which he or she is
searching. For example, if a user is searching for a person, he or
she is presented with a form consisting of fields for name, email,
title, and so forth. All matching entities of that type where the
fields are matched on a “like” comparison are returned in a list
similar to that of an entity list in the browsing section. Figure 5
shows the screen shot of the search form.
Because the number of instances in a given class can get large
enough to make it to difficult to find instances by browsing, we
have added a semantic query module to this portal. The
function of this type of search is to allow the user to browse the
ontology through the Touchgraph interface to select the class
they would like to search on. When the class is selected, a
servlet retrieves the class schema information from the freedom
system. This information gives the class name as well as the
name and all attributes of the class. This information is returned
as XML, which is formatted into a form and displayed to the
use. The user would then fill out the form with the values for
each field and submit it to another servlet. This will take the
information that is given and search the specified class for any
instances that make that query true. This is accomplished
through a call to the Knowledge Engine API to search for
entities by class.
If more than one field is containing data, then the query is an
“and” query and both criteria must be met in order for an
instance to be in the final result set. For example, if msStudent
class is selected and “Joe” in the name field, then the system will
find all instances of the msStudent class that contain the string
“Joe” in the name field. This search will find the string if it is a
substring of another instance. Once the list is compiled by the
servlet, it is formatted in XML and returned to the user in the
same way that any list of entity instances is viewed.
There are several capabilities that could be easily added to this
method. The Knowledge engine API method that is called to
search the instances supports either an “AND” query or an “OR”
query. This functionality could be added to the form and servlet
to give the user more flexibility in searching the data. Also, in
many cases, the attribute that the user might want to search on is
not an attribute, but a relationship. A relationship search could
be added by giving a button on the form to add the fields for the
class that is related to this one to the form. By doing this, the
users search capabilities would become much more powerful
because this type of search would give them the ability to
traverse any number of arcs in the ontology to get the data that
they want to see.
3.2.9
Web Search Module
While our portal contains lots of relevant information pertaining
to Semantic Web research, we cannot expect it to contain all the
information of interest to a user. What we do expect is to have
the ability to annotate extranet information. Consequently, we
have implemented a Web Search component to allow users to
search for information outside of our portal while still being
provided a Semantic view of the information. This module is
built from two main components: Freedom’s Semantic
Enhancement Engine and the search engine MetaCrawler.
MetaCrawler is the search engine used by our Web Search
component to execute searches. One search to MetaCralwer
simultaneously searches various other search engines, then
combines and reranks the results. For this reason, its creators
consider MetaCrawler meta-search engine. Some of the search
engines used is Google, AltaVista, FindWhat, and LookSmart.
Through MetaSearch, we give our Web Search component a
broader reach than it would have through only one search
engine.
The purpose of using the SEE in the web search is for its
Semantic Annotation capabilities.
Through Semantic
Annotation we are able to give our users a Semantic view of the
Web with respect to our Ontology. Freedom allows for various
flavors of Annotation through the creation of configuration files.
For this particular application we identify entities from our
Knowledge Base in the HTML file the user is viewing, then we
highlight the entitiy and embed a link to the entity’s Entity View
page in the Portal. This annotation makes the entity quickly
identifiable in the document and provides a quick link to further
information about the entity.
The Web Search pieces these two components together in the
following way. First, a user is presented with a search box in
which he can enter his keyword search. A query url for
MetaCralwer is created, and the search is sent. The resulting
HTML page from MetaCrawler is parsed and the link url, link
text, and link summary for each result is extracted and serialized
as XML. The XML is converted into HTML, Annotated by
Freedom, and then presented to the user. Thus, the user sees an
enhanced version of his search; he can quickly identify and find
more information on the recognized entities. Also, the result
urls are modified so that the link points to an annotated version
of the result page. Figures 7, 8, 9 provide a screenshot of the
browse mode in SEMPL.
3.2.10
Related Links Module
The related links component of our Portal is intended to provide
users with information relevant to the data they are viewing.
Related links are provided at the entity instance level. They are
links to relevant entities which are not directly related to the
current entity being viewed, meaning the two entities are not
directly linked in the Ontology. For example, when viewing a
publication, a user is assumed to be interested in the topic of the
paper and may want to know of experts on the topic. The portal
can traverse the following 3-edge path to find other people who
have written papers on this topic:
3.3
Presentation and Interface
An important feature in the portal architecture is the separation
of the dynamically generated content from its presentation. This
delineation is essential to maintain data independence as the
design and presentation of a web site are often handled by an
entirely different person or group. The portal maintains this
separation through the use of various cascading style sheets and
other transformations with XSL sheets.
Cascading style sheets ensure that all content delivered by the
servlets and JSP are standardized in their presentation. Easily
maintained and altered, these types of style sheets are a common
mechanism that enables rapid change to the style of the various
pages without rewriting any of the core content delivery pages.
The underlying knowledge base and its API deliver content
serialized as XML, and this is transformed through XSL sheets.
These sheets provide certain processing capabilities that can
transform well-formed XML into HTML for presentation.
Changes in the appearance of a page, therefore, do not require
changes in the backend of the XML delivery. Combined with
cascading style sheets, XSL allows for the separation of
dynamically generated content from its presentation to improve
both efficiency and maintenance.
1.
publication  isAbout  researchTopic
3.5
2.
publication  isAbout  researchTopic
3.
person  publishes  publication
SEMPL is fairly easy to maintain. Maintenance for the portal is
handled by Freedom. The maintenance of the portal can be done
by regularly running the extractors. The Freedom extractors can
be programmed to run in regular time intervals. In this way even
if the information of the resources change, they can be extracted.
The administrator can make changes to the database using
freedom’s maintenance tool and the changes are reflected in the
portal.
Then through our ranking mechanism we prune these candidate
experts to a small list of relevant people.
We have implemented a simple, effective, and configurable
ranking mechanism for these related links. This can be best
explained with the previous example. Suppose we are traversing
the three-edge path above. Any unique entity (person in this
case) which has written x papers about the research topic in
question can be reached by x distinct paths from our original
publication instance. We sort our list of reachable entities based
on the number of distinct paths. Then we return the y best
entities where y is an administrator-defined number of entities to
return.
The code for this component is completely generic and
independent of the Ontology. The administrator can configure
these semantic paths for related links by defining all paths used
in an XML file. A Java component called SemanticLinks reads
and parses the XML file and creates a List of Path objects. Path
is a Java class we have defined which holds information to
identify the path in the Ontology. This List, along with the
unique id for the base entity, can then be passed to a method in
PortalKnowledgeEngine called getSemanticLinks, which uses
other methods in the middle layer to traverse each path starting
from the base entity and serialize the resulting entity links as
XML for display. Figure 6 provides a screenshot of the related
links obtained by SEMPL.
Maintenance of the Portal
We feel that maintenance handled by Freedom is self-sufficient
for the portal. We therefore did not feel the need to have
additional features for maintenance of the portal.
4.
RELATED WORK
This section provides a brief overview of the current work
involved in the area of Semantic Portals. We try to position our
work in the context of existing web portals.
4.1
OntoWeb
OntoWeb [1] is an ontology-based system for information
exchange, knowledge management and electronic commerce. Its
main purpose is semi-automated creation of portal using
metadata.
OntoWeb maintains domain specific ontologies that are applied
to structure domain-specific knowledge. It maintains a Metadata
conforming to the ontology in a central knowledge base.
OntoWeb also provides the facility for the users to provide
information thus enabling comprehensive Content Management.
The ontology presentation engine in OntoWeb exploits the
ontology to browse and query the portal. The querying are of
two types one is by term based and other is template based using
annotations from ontologies.
Ontoweb provides information about wide variety of topics.
SEMPL on the other hand is more focused on research in LSDIS
lab and Semantic Web area. SEMPL provides additional
features like automated annotation of web pages, information
about related topics. SEMPL has a very user-friendly graphical
user interface supported by touch-graphs providing the user a
convenient way to browse and navigate the LSDIS web site.
4.2 Mind Swap
Mind Swap [2] is a semantic portal that allows creators to
submit pages to their website. These pages are then indexed by
their semantic markup. It supports search wherein the users can
select an ontology and term, and run a search for any page that
is indexed by that term. The portal returns a list of marked up
web pages that describe the term, giving the user the ability to
see how other people relate to the same concept in World Wide
Web.
As these links are added, queries are made to various web backends that contain similar pointers from other documents,
databases, image archives, etc. The results are displayed to the
user, allowing a constant, dynamical web portal to be created.
This portal contains pointers to documents that are on similar
topics, databases that can answer queries about conceptually
related science, and images and other multimedia resources.
SEMPL provides a more sophisticated portal than MIND
SWAP. SEMPL‘s web search module searches pages in the
world wide web. These pages are annotated based on SEMPL’s
ontology. This is unlike MIND SWAP’s work, where in the
annotate pages that are already indexed. SEMPL thus supports
automated annotation. SEMPL also provides provision for
providing related links. Thus when a user browses/queries,
SEMPL not only provides information about a particular topic
but also about related topic. SEMPL has very easy and visual
graphic tool for browsing, search and navigation.
4
CONCLUSION
As seen SEMPL provides an extensive semantic web portal for
the LSDIS lab with in build features for meaningful gathering of
information.
5
FUTURE WORK
For future work we envision number of important topics to be
included in our work.

We would like to extend our work for constructing a
portal that supports different domain knowledge
including LSDIS lab domain.

We would like to include measures for semantic
similarity and semantic ranking.

We would like to provide user interactive feature,
where in users can submit their ontologies enabling
extensive content management.
7
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[4] http://www.semanticweb.org
[5] http://www.touchgraph.com/
In this paper we have presented a comprehensive approach
SEMPL for building semantic portal. SEMPL uses the ontology
driven approach for knowledge systems. Ontology ties together
the resources through relationships. These relationships enable
SEMPL to retrieve more focused, relevant and semantically
related knowledge entities.
SEMPL incorporates Freedom software; this enables it to have
powerful ontology creation and resource extraction features.
With the aid of Freedom, SEMPL is able extract and manage
metadata from content. In addition, SEMPL provides visual and
user friendly browsing and querying capabilities. The querying
supports retrieval of semantically related information.
[6] van Har Harmelen F, Patel-Schneider PF, Horrocks I
(2001). Annotated DAML+OIL (March 2001) Markup
Language.
Technical
Report.
http://www.daml.org/2001/03/daml+oil-walkthru.html
Figure 1. System Architecture
Figure 2: Screenshot of the SEMPL main page
Figure 3: Screenshot of Browsing Entity instance class fullProfessor
Figure 4: Screenshot of the browse results for Amit Sheth
Figure 5 : Screenshot of the search form in the LSDIS search mode
Figure 6: Screenshot of the semantically related links obtained for publication
Figure 7: Screenshot of the Web Search mode
Figure 8: Screenshot of the search results for “Semantic Web Languages”in the web search mode
Figure 9: Viewing a result page in web search mode and showing the results of clicking the RDF
annotation
Figure 10: Freedom knowledge modeler