THE DESIGN AND IMPLEMENTATION OF A

SEMANTICS-BASED WEB CONTENT ADAPTATION

FRAMEWORK FOR MOBILE DEVICES

Chichang Jou and Cheng-Ze Ho

Department of Information Management, Tamkang University, Tamsui, Taipei, Taiwan

Keywords: Content adaptation, semantic web, CC/PP, Jena.

Abstract: With the rapid development of wireless communication technology, many users are accessing the internet

from mobile appliances, such as notebooks, PDAs, and cellular phones. These devices are miscellaneously

limited in computing resources, like CPU speed, memory, temporary storage, power supply, installed

software, and communication bandwidth. The web accesses in these mobile devices thus encounter distorted

user interface, broken images, slow responses, etc. To overcome this problem, we design and implement a

semantics-based web content adaptation framework to provide automatically adapted contents to

miscellaneous mobile devices. RDF Semantics of client device CC/PP configurations and web page tag

structures are extracted to determine the proper parameters for the format and layout of web contents.

Heuristic transcoding rules in the Jena Inference System are then applied to transform the web contents for

each particular device. The functionality of this framework is illustrated by its capability of adjusting the

layout of the main page of a shopping portal and of adjusting the parameters of images in the page.

1 INTRODUCTION

With the rapid development of wireless

communication technology, many users are

accessing the internet from mobile appliances, such

as notebooks, PDAs, and cellular phones. Many

emerging computation paradigms, such as pervasive

computing, have been proposed to embrace this

blooming portable computation trend. However,

mobile devices have various hardware limitations,

such as CPU speed, power, memory, and image

resolutions. They are also restricted in software

support, such as operating system, installed

programs, real-time processing capability, and

rendering functionality. These ad hoc limitations

have become barriers in human-computer

interaction. Especially, current internet contents,

such as web pages and images, are mainly in the

HTML format designed for desktop computers.

Without any modification, it is hard to render them

properly in most mobile devices.

In this paper, we propose a semantics-based web

content adaptation framework to overcome the

presentation inconsistency among mobile devices

and desktop computers. In this framework, web

pages and image files are automatically transcoded

according to semantics extracted from CC/PP

(Composite Capability/Preference Profiles) (Klyne

et al., 2004) client device configuration and web

contents. These semantics properties will be stored

inside the Jena Inference System (Carroll, et al.,

2004) as knowledge facts to infer proper transcoding

parameters for each particular device. The adapted

web pages with proper layout modification and

images with proper rendering parameters will be

constructed and delivered. Thus, this technology is

suitable for resource-limited devices to balance

information loss and information availability.

For the rest of this paper, we will review related

work in Section 2. In Section 3, we describe the

system architecture of this framework. Semantics

extraction and knowledge base construction for the

Jena Inference System are discussed in Sections 4

and 5. Section 6 demonstrates the content adaptation

function with respect to the generated transcoding

parameters. Section 7 explains the system

implementation and illustrates through three mobile

devices examples of rewriting one web page, and of

adjusting the width and height parameters of images

in that page. Section 8 concludes this paper.

106

Jou C. and Ho C. (2007).

THE DESIGN AND IMPLEMENTATION OF A SEMANTICS-BASED WEB CONTENT ADAPTATION FRAMEWORK FOR MOBILE DEVICES.

In Proceedings of the Third International Conference on Web Information Systems and Technologies - Web Interfaces and Applications, pages 106-113

DOI: 10.5220/0001291801060113

 SciTePress

2 RELATED WORK

Many researchers have tackled the incompatibility

issue of mobile device in accessing web pages.

Bickmore and Girgensohn (1999) designed a

“Digestor System” which is capable of automatic

filtering and re-authoring so that WAP-enabled

cellular phones could read HTML contents. Their

basic idea is to extract plain texts in the HTML

document by discarding all formatting elements and

unnecessary information. The result is then divided

into a navigation page and several plain text sub-

pages. They also utilize transcoding cache to

diminish the run-time overhead.

Ardon et al. (2003) prototyped a proxy-based

web transcoding system based on network access

control, user preferences, and displaying capability

of equipments. Since all transcoding procedures

were finished in the content provider’s server, this

server-centric framework avoided potential

quality-of-service oriented decision engine for

content adaptation. They designed flows for content

negotiation and processing for multimedia contents.

Hua et al. (2006) integrated content adaptation

algorithm and content caching strategy for serving

dynamic web content in a mobile computing

environment. They constructed a testbed to

investigate the effectiveness of their design in

improving web content readability on small displays,

decreasing mobile browsing latency, and reducing

wireless bandwidth consumption.

Buyukkokten et al. (2001) used an “accordion

summarization” transcoding strategy where an

HTML page could be expanded or shrunk like an

accordion. The HTML page is restructured as a tree

according to the semantic relationships among its

textual sections. All textual sections are split into

several Semantic Textual Units, which are

automatically summarized. Users can check each

summary to expand the node for detailed

information. However, this framework only works in

the browser they designed for digital libraries.

Hwang et al. (2003) also treated web page layout

as a tree according to its tag hierarchy. They defined

a grouping function to transform such tree into sub-

trees, and introduced a filtering mechanism to

modify the sub-trees for adequate display in the

target device. They analyzed specific web page

layout structure and re-authored, according to

heuristics, web pages for several mobile devices.

Each of their transcoding method could handle only

specified layout structures of web pages and did not

consider mobile device characteristics.

Huang and Sundaresan (2000) tried the

semantics approach in transcoding web pages to

improve web accessibility for users. Their system

was designed to improve the interface of e-business

transactions and to extend interoperable web forms

to mobile devices. They used XML/DTD to specify

the semantic and grammatical relationship among

web contents, so that web forms could achieve

consistency, simplicity and adaptability. The

advantage of this system is its ability to provide

concept-oriented content adaptation, but it is hard to

extend.

DELI (Butler, 2002), an HP Semantic Lab

project, adopted simple negotiation algorithms for

rewriting web pages based on context information,

like user preference and device capabilities. Glover

and Davies (2005) used heuristic algorithms to find

proper pre-defined web page templates according to

device attributes. Their focus is in applying

XML/XSLT styles to database contents retrieved in

dynamic web pages.

In our framework, we follow the guideline of the

“Device Independent” (Bickmore et al., 1997)

principle and utilize the device contexts described in

CC/PP.

3 SYSTEM ARCHITECTURE

In this section, we explain the system architecture

and the data flows of our framework in Figure 1.

The adaptation mechanism of our framework, called

Content Adaptation Proxy Server, resides behind

web servers. The Proxy Listener is responsible for

receiving HTTP requests (message 1) from

miscellaneous mobile devices and for dispatching

these requests to the Content Fetcher. The client’s

device information as well as user’s personal

preferences will be embedded inside these requests

through CC/PP diff, which is a modified version of

predefined CC/PP profile from the hardware

manufacturers.

CC/PP is a two-layered user preferences and device

capabilities description based on XML/RDF

(Manola and Miller, 2004). CC/PP consists of the

following three categories: hardware platform,

software platform, and browser user agent. Figure 2

shows example detailed attributes in the XML/RDF

format of one mobile device. (Klyne et al., 2004) In

our framework, all predefined CC/PP profiles are

stored within a profile directory. A CC/PP diff is

manually configured by the user, and is normally

used to reflect the user preferences. It is dynamic

and can be further modified in later sessions. Many

protocols have been proposed to enhance HTTP 1.1

THE DESIGN AND IMPLEMENTATION OF A SEMANTICS-BASED WEB CONTENT ADAPTATION

FRAMEWORK FOR MOBILE DEVICES

107

protocol to include CC/PP profile diff. Two of such

protocols are CC/PP-ex (Ohto and Hjelm, 1999) and

W-HTTP (OMA, 2001). We adopt CC/PP-ex in this

framework.

Figure 2: Example CC/PP for a mobile device.

When Proxy Listener accepts a request from a

client, it will spawn a working thread in the Web

Content Fetcher to handle the request (message 2).

Web Content Fetcher performs the standard task of

proxy servers. If the requested web content is

already in the cache, it will be fetched from Cached

Web Content (messages 3.3 and 3.4). If not, then it

will be fetched from the source through the internet

(messages 3.1 and 3.2), and then be saved in the

Cached Web Content. The working thread is also

responsible for resolving the CC/PP profile diff.

The web content and CC/PP diff will then be

sent (message 4) to the Semantics Extractor to

acquire the implicit RDF semantic information

within the CC/PP diff, HTML web pages and

parameters of image files. RDF describes resource

information in the internet by the three components:

resource, property, and statement. A RDF statement

is represented by a triple of (subject, predicate,

object).

These semantics information will be sent

(messages 5.2) to the Jena Inference System as basic

facts. Jena Inference Engine will combine these facts

with the knowledge base of CC/PP UAProf RDFS

model (WAP, 2001), Transcoding Rules and Web

Page Auxiliary Vocabulary to determine the proper

transcoding parameters in the format of sequential

RDF predicates for the requests.

The web content and transcoding parameters will

then be passed (message 6) to the Transcoder.

cac

content request

Proxy Listene

Client

2. Dispatched Request

Jena Inference

System

5.3transcoding

parameters

content

7. HTTP response

Web Content Fetcher

Semantics Extractor

5.2 semantics properties

6. Web Content and Transcoding Service Request

Transcoder

4. Web Content and CC/PP diff

Cached web

content

content request

Device Info DB

3.1 Content Re

uest

Content Adaptation Proxy Serve

1. HTTP Request

Interne

Figure 1: System Architecture of Content Adaptation Proxy Server.

WEBIST 2007 - International Conference on Web Information Systems and Technologies

108

Besides the layout rewriting mechanisms, the

Transcoder is equipped with transcoding toolkits,

such as ImageMagic (Still, 2005) for image

resolution adjustment. The results of the Transcoder

processing consist of web pages with modified

layout as well as images with proper resolution and

parameters for the requesting mobile device. They

will be returned to the client (message 7) and

displayed in its browser.

4 SEMANTICS EXTRACTOR

The Semantics Extractor is responsible of collecting

information of the fetched web content by checking

its file type, information of analyzing the structure of

web pages, and information of constructing facts

about the requested web contents and device

characteristics.

Since most HTML pages are not well-formed, it

is hard to extract semantics from them directly. This

module first transforms HTML pages into the well-

formed XHTML format through the Tidy toolkits

(Raggett, 2000). The following two file types of the

requested URL will first be handled in this

framework: (1) XHTML files: Their metadata are

about layouts of the document, possibly with

hyperlinks to external textual or binary files. (2)

Image files: These are binary files with adjustable

parameters, like color depths and resolution.

Currently, the system could handle JPEG, PNG, and

GIF images. For files encoded in indestructible

formats, like Java applets, since they could not be

adjusted, the system directly forwards them to the

Transcoder for delivery. To extend this web

adaptation framework to new file types, we just need

to specify the metadata about the new file type, and

have to build the semantics extraction component

and transcoding rules for web contents of the new

file type.

For XHTML files, the semantic extractor module

collects the following schema information: the

identification of each XHTML element, and the

layout of the XHTML page. We apply XHTML

DOM (Document Object Model) (Le Hégaret et al.,

2005) tree nodes scanning to extract node

information and relationships among XHTML

elements. We solve the element identification

problem in an XHTML page by XPath (Clark and

DeRose, 1999) so that each node in the DOM tree

could be specified accurately.

Statistical or inferred semantics data for the

following Web Page Auxiliary Vocabulary will be

extracted for each XHTML DOM tree node:

1. NumberOfWords: This data indicates number

of words in a paragraph. It is used to determine

whether the paragraph corresponding to the

XHTML node should be split.

2. NumberOfImages: This data indicates number

of the <IMG> tags in a specific XHTML node. It

is used to decide whether a tabular cell is an

advertisement banner.

3. AverageLink: This is the quotient of the number

of links and the number of words within a

XHTML node. In web contents with useful

information, this value tends to be very high, and

all contents in the node should be preserved.

4. Title: For XHTML nodes with the <H1> or

<H2> tag, or with texts surrounded by pairs of

the <B></B> or <STRONG> </STRONG> tags

and followed by <BR> immediately, the

collected content is treated as a title. This could

be used as the title of the sub-page

corresponding to this node.

5. Layout: This information indicates whether the

node is used for layout composition. For

example, to determine whether a <TD> is a

layout element or an actual tabular cell, we

calculate the number of words for the element. If

its number of words exceeds a specific threshold,

we mark such a <TD> element as a layout

element.

Consider the following simplified XHTML page:

<TR><TD>Gentoo Linux is a totally new linux distribution.

</TD></TR>

</TABLE></BODY></HTML>

We can describe the <TD> tag in the above page

with RDF, XPath and Web Page Auxiliary

Vocabulary as follows:

<rdf:Description rdf:about="/HTML/BODY/TABLE/TD[1]">

<rdf:type rdf:resource="html:ELEMENT_NODE" />

<html:NumberOfWords>8</html:NumberOfWords>

<html:IsLayout>false</html:IsLayout>

<html:NumberOfImage>0</html:NumberOfImage>

<html:NodeName>TD</html:NodeName>

<html:ChildNodeNumber>0</html:ChildNodeNumber>

<html:ParentNode rdf:parseType=”Resource”

rdf:resource=”_:/HTML/BODY/TABLE/TR[1]”/>

</rdf:Description>

Semantics of device characteristics will be

collected through CC/PP diff. The CC/PP semantics

of device configurations are already RDF

compatible. They could be sent to the Jena Inference

System as facts. The total customized device

description can be translated into a graph model

within the Jena Inference System.

THE DESIGN AND IMPLEMENTATION OF A SEMANTICS-BASED WEB CONTENT ADAPTATION

FRAMEWORK FOR MOBILE DEVICES

109

5 JENA INFERENCE SYSTEM

We use Jena (Carroll et al., 2004), a semantic web

toolkit of “Device Independent” (Bickmore et al.,

1997) ideal, to determine the transcoding

parameters, which are represented as sequential RDF

predicates. The Jena Inference System, displayed in

Figure 3, has three main components. These

components are utilized in our framework as

follows:

Figure 3: Jena Inference System.

Knowledge Base – It contains the acquired

knowledge and rules in deciding the content

adaptation parameters. XHTML schema is derived

by mapping from XHTML XML schema to

RDF/RDFS as one knowledge base. Transcoding

Rules contain rules using web content ontology and

device characteristics ontology for transcoding.

Auxiliary Vocabulary for transcoding parameter

decision are also described by RDF/RDFS and

serialized into Jena knowledge base. All RDF

knowledge is serialized in the XML format to

provide more flexibility and interoperability in

content adaptation.

Jena Inference Engine – This is the decision engine

to inference and to generate transcoding parameters.

We make use of the engine without any

modification.

Facts – These are facts supported in the form of

instantiated predicates. In our framework, they are

the semantic data collected by the Semantic

Extractor.

We apply heuristics to design the transcoding

rules in the “IF…THEN…” format. If the precedent

parts of a rule are all true, then the consequent part

of the rule would be added as a statement of

transcoding parameters into the knowledge base. We

provide rules regarding device characteristics by

defining restriction rules using first order predicate

logic. Rules are categorized to back up each other.

For example, if rules for HP 6530 PDA are not

sufficient, then rules for PPC Pocket PC could be

used. In the rules, names prefixed with ‘?’ are

variables. The following example rule is for image

resizing:

[ScaleImageByWidth:

(system:Content content:Width ?image_width),

(system:HardwarePlatform ccpp:DisplayWidth ?display_width),

lessThan( ?display_width, ?image_width ) ->

(system:Content content:ScaleImageByWidth ?display_width)]

The rule is named ScaleImageByWidth

. The three

namespaces “system”, “ccpp”, and “Content” point

to the knowledge bases regarding the content

adaptation proxy server, the standard UAProf

Schema (WAP, 2001), and web content under

transcoding, respectively. The above rule means that

if the width of the device (?dispaly_width) is less

than the width of the image (?image_width), then set

the width of the image as the width of the device,

and set the height of the image proportionally with

respect to the adjustment ratio of the width.

6 TRANSCODER

The Transcoder is composed of several transcoding

modules corresponding to file types of XHTML,

JPEG, etc. It could be extended to handle other file

types. According to the transcoding parameters, it

performs content adjustment and filtering. For image

files, currently the system not only transforms image

files into the same format with different parameters,

but also transforms image files into different

formats.

The Transcoder dispatches the web contents

according to their file type to the corresponding

adaptation component. To perform the required

content transcoding operation, it will query the

inferred RDF model by the RDF query language

RDQL to obtain the required transcoding parameters.

The use of RDQL could prevent tight coupling of

the transcoding components. An example RDQL

query is demonstrated as follows:

SELECT ?predicate, ?object

WHERE ( system:Content, ?predicate, ?object)

USING system FOR http://www.im.abc.edu/~def/proxy.rdfs#

In RDQL, USING is to specify the name space. This

query could obtain all RDF statements with subject

WEBIST 2007 - International Conference on Web Information Systems and Technologies

110

system:Content, where system is the name space and

Content represents web content currently under

transcoding. Transcoding query results are

represented as instantiated predicates. For example,

if the transcoding predicate for an image file is

ScaleImageByWidth, then the Transcoder would

adjust the image width and height proportionally.

After all RDQL query results are handled, the

resulting web content would be returned to the client.

7 SYSTEM IMPLEMENTATION

We implement the content adaptation proxy server

in the Fedora Linux 2.6.18 operating system by the

Java Language J2SDK 1.5.0. We use the package

org.w3c.dom as the class for handling XHTML

DOM trees for web pages. The JPEG, PNG, and GIF

image files are handled by the Java class

java.awt.image.BufferedImage. We make use of the

inheritance mechanism, so that the Content Fetcher

interface only specifies one method:

public Object getContent( ).

Table 1: Transcoding rules for image files.

Transcoding Rule Comment

[

ExtractColor:

(system:Conten

content:Width ?image_color_depth),

(

system:HardwarePlatform

cpp:ScreenColorDepth ?device_color_depth),

essThan( ?device_color_depth,

?image_color_depth )

>(system:Content content:ReduceColorDepth

?device_color_depth),

(system:ReduceColorDepth

system:TranscodeType "text/jpeg")]

Modify image

color depth to

match the

display

capability of

the device.

[

PngToJpeg:

(system:Content content:Type “image/png”),

(

system:SoftwarePlatform

ccpp:CcppAccept ?Bag),

oValue(?Bag ?li "image/png"),

(

?Bag ?li "image/jpeg")

> (system:Content system:TransformTo

"image/jpeg"),

(system:TransformTo system:TranscodeType

"text/jpeg")]

Transform

PNG files to

JPEG.

[

JpegTo

lainText:

(

system:Content content:Type “image/jpeg”),

(

system:SoftwarePlatform

ccpp:CcppAccept ?Bag),

oValue(?Bag ?li "image/jpeg"),

(

?Bag ?li "text/plain")

> (system:Content system:TransformTo

"text/plain"),

(system:TransformTo system:TranscodeType

"text/jpeg")]

Transform

JPEG to plain

text.

Table 2: Transcoding rules for web pages.

Transcoding Rule Comment

[ExtractTableContent:

(?node content:NodeName "table"),

(system:BrowserUA ccpp:TablesCapable

"No")

->(system:Content

system:ExtractTableContent ?node) ,

(system:ExtractTableContent

system:TranscodeType "text/html")]

If the

browser in

the mobile

device does

not support

table, then

extract the

content.

[FilterCSSScript:

(?node content:NodeName "style"),

( system:BrowserUA

ccpp:StyleSheetCapable "No" )

-> (system:Content

system:RemoveNode ?node)]

[FilterCSSScript:

(?node content:NodeName "style"),

(system:SoftwarePlatform

ccpp:CcppAccept ?Bag),

noValue(?Bag ?li "text/css")

-> (system:Content

system:RemoveNode ?node) ,

(system:RemoveNode

system:TranscodeType "text/html")]

If the

browser in

the mobile

device does

not support

CSS, then

filter the

CSS

content.

[FilterFlash:

(?node content:NodeName "object"),

(system:SoftwarePlatform

ccpp:CcppAccept ?Bag),

noValue(?Bag ?li "x-application/flash"),

(?node content:InlineDocumentType

“x-application/flash”)

-> (system:Content

system:RemoveNode ?node) ,

(system:RemoveNode

system:TranscodeType "text/html")]

If the

browser in

the mobile

device does

not support

Flash, then

filter the

Flash

content.

The Semantics Extractor is implemented by a Java

interface, a factory for semantic extraction, and one

class for each supported file type. Some of the

transcoding rules for images are listed in Table 1,

and some of the transcoding rules for web pages are

listed in Table 2.

To demonstrate the functionalities of this

framework, we tested three client mobile devices:

HP iPAQ hx2400, Symbian S80 Simulator, and

Panasonic EB-X700. We would like to show the

effect of the following two CC/PP parameters:

supported file types and display size. The goal of the

adaptation is to avoid the use of horizontal scroll bar,

so as to increase the readability of transcoded pages

and images. The related specifications and

restrictions of these devices are listed in Table 3.

THE DESIGN AND IMPLEMENTATION OF A SEMANTICS-BASED WEB CONTENT ADAPTATION

FRAMEWORK FOR MOBILE DEVICES

111

Table 3: Specifications and restrictions of mobile devices.

Device HP iPAQ

hx2400

Symbian

S80

Panasonic

EB-X700

Category PDA

Smart

Phone

Smart

Phone

Operating

System

Windows

Mobile 5.0

Symbian

Series80

Symbian

Series60

Browser IE Mobile Built in Built in

Supported

file types

text/html

text/css

image/jpeg

image/png

image/gif

text/xhtml

text/css

image/jpeg

image/png

image/gif

text/chtml

image/jpeg

Display size 480 x 320

(pixels)

220 x 640

(pixels)

176 x 148

(pixels)

Connection Bluetooth WLAN GPRS

Figure 4 shows the upper part of the tested web

page (http://www.amazon.com) in a Microsoft IE

6.0 browser in a desktop computer. The upper parts

of the transcoded pages in the built-in browser for

the three tested mobile devices are displayed by two

screen shots in Figures 5 to 7. All resulting

transcoded web pages satisfy the goal of avoiding

the use of horizontal scroll bar by adjusting the page

layout, image size, and image resolution.

Unsupported CSS, Javascripts, flashes, div’s and

tables are filtered out.

8 CONCLUSIONS

We designed and implemented a flexible and robust

infrastructure for web content adaptation using RDF

semantics from CC/PP device characteristics,

XHTML web pages, and JPEG, PNG, and GIF

image files. Past researches in this area either did not

take device characteristics into consideration, or

were not a general purpose solution for

miscellaneous mobile devices. We made use of the

Jena Inference System to obtain the transcoding

parameters through the fact and knowledge base

built from the collected semantics.

Figure 4: Test web page in a desktop computer.

Figure 5: Result of test page in HP iPAQ hx2400.

Figure 6: Result of test page in Symbian Series80.

WEBIST 2007 - International Conference on Web Information Systems and Technologies

112

Figure 7: Result of test page in Panasonic EB-X700.

In this framework, a single copy of the web

pages could serve many different mobile devices.

Previous tedious web page rewriting labour for

mobile devices could be saved. Our framework

could be easily extended to new file types by

importing related semantics and transcoding

modules.

We plan to incorporate support of style sheet and

web form specifications, such as CSS and XForm,

into this semantics-based content adaptation

framework. By supporting these dynamic web pages,

the increased user interactivity would accelerate user

acceptance of pervasive computing.

ACKNOWLEDGEMENTS

This work was supported in part by Taiwan’s

National Science Council under Grant NSC 95-

2221-E-032 -027.

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