A Semantic-based Transcoding Mashup Server for Web 2.0 Sites

I-Ching Hsu Department of Computer Science and Information Engineering National Formosa University,

64, Wenhua Road, Huwei Township, Yunlin County 632, Taiwan hsuic@nfu.edu.tw

Abstract

One problem of Web 2.0 applications lacks intelligence to cope with the XML-based documents transcoding. This study attempts to integrate Semantic Web technologies into Web 2.0 Mashups by defining a semantic-based Web2.0 Mashups framework, which is developed based on Service-Oriented Architecture (SOA) to ruling out heterogeneous effects. Based on the proposed framework, Semantic-based Transcoding Mashup Server (STMS) is also developed to facilitate finding useful Web 2.0 Mashups to achieve Web 2.0 documents transcoding. To demonstrate the feasibility of STMS, an application scenario of the semantic-based mashup transcoding is implemented to convert map pages into various display platforms. Additionally, the study also realizes how Semantic Web and Web 2.0 Mashups can complement each other. 1. Introduction

Within the last two to three years, the Internet has greatly changed our way of sharing resources and information. As well known, Web 2.0 is recognized as the next generation of web applications proposed by T. O'Reilly [1]. The main feature of Web 2.0 applications is that they provide a medium for the sharing and exchange of resources [2]. These resources, such as Web 2.0 documents allow web developers to take advantage of these resources to enrich their own applications or produce new integrated solutions by integrating resources, which they could not have provided on their own. Many Internet companies have enabled easy access to the web resources that they provide. Anyone can create a new integrated application with these resources. This new way of building Web 2.0 applications has also created a new method of thinking of the Internet as a resource. When an application combines resources from different websites to produce a new web application it is called a Web 2.0 Mashup [3].

Mashup has become a major part of the Web 2.0 technologies. It requires shared meaning to enable the creation of new applications by combining or reusing different resources on the Web. However, most Web 2.0 applications share meaning based on XML-based metadata that still short of knowledge representation in handling computer-interpretable effects. Semantic Web provides intelligent capabilities can facilitate to find useful Web resources to complement Web 2.0 Mashups. A significant challenge of web resources rendering is how to automatically convert Web 2.0 documents (such as in format of XHTML, XML, RSS, KML, Atom, etc) into the desired XML-based format which can be accepted by a specific display platform (such as Web browser, RSS reader, Google Earth, Android, iPhone, WAP phone, etc). The Web 2.0 documents conversion is a type of transcoding, which is a technology used to adapt computer application displays and Web content so that they can be viewed on any of the increasing number of diverse devices on the market.

There are three traditional approaches for the generic transcoding of markup documents, including flat-based transcoding, template-based transcoding, and annotation-based transcoding. The existing transcoding systems are developed accordingly. However, they generally lack an intelligent, extendable, interoperable, and flexible functionality. Especially, metadata level interoperability is often hardwired in transcoding processes using traditional approaches, and process level interoperability is often maintained through manual configuration of workflows. This study proposes a semantic-based Web2.0 Mashups approach, called semantic-based mashup transcoding, based on Service-Oriented Architecture (SOA) [4] to address these drawbacks.

A mashup transcoding can invocate a transcoder, which is developed based on Web 2.0 Mashups technology, such as SOAP, XML-RPC or REST. The semantic-based mashup transcoding aims to generate and execute a composite process, which produces the

International Conference on Complex, Intelligent and Software Intensive Systems

978-0-7695-3575-3/09 $25.00 © 2009 IEEE

DOI 10.1109/CISIS.2009.99

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International Conference on Complex, Intelligent and Software Intensive Systems

978-0-7695-3575-3/09 $25.00 © 2009 IEEE

DOI 10.1109/CISIS.2009.99

59

expected data format by combining existing individual transcoder using their semantic metadata. These semantic metadata are described by ontology-based annotations, which provide a semantic modeling of mashup transcoding for dynamic integration of transcoders in Web 2.0 applications. Thus, the semantic-based mashup transcoding is developed based on SOA for combining Web 2.0 Mashups and Semantic Web technologies.

This study develops a Semantic-based Transcoding Mashup Server (STMS) using Semantic-based Web2.0 Mashups approach [5]. The STMS plays as a service broker in SOA environment, and thus can support a flexible infrastructure to quickly deploy scalable Internet applications. With the STMS, various Web 2.0 display platforms are able to read the same web content by different renderings, and existing transcoder for the transcoding system can be easily integrated. The newly developed transcoders adopt technologies of semantic-based Web 2.0 Mashups approach.

The remainder of paper is organized as follows. The next section presents some related works. Section 3 is an introduction to the Semantic-based Web 2.0 Mashups Approach. A flow-oriented aspect to describe the STMS architecture is presented in Section 4. In Section 5, we demonstrate that the STMS can provide various Web 2.0-based platforms to display the same web content with different renderings. Finally, summary and concluding remarks are included. 2. Related Work

Generally, there exist threes approaches for the generic transcoding of markup documents. Followings are the detailed definitions of these classifications. The flat-based transcoding approach is that the transcoder directly converts a markup document from one format to another without referring to any external data. This transcoding approach is the simplest type of transcoding. HTML Tidy [6] is an example that can convert HTML documents to XHTML documents. With the template-based transcoding [7], one composes a template which is expressed in some transformation language to describe rules for transforming XML documents. Making use of template-based transcoding can solve the inflexibility problem of flat-based transcoding. The restrictions of template-based transcoding are the limited tags of the transformation language and the lack of knowledge representation.

The annotation refers to adding metadata to markup documents to provide a higher level interpretation of the documents. Existing annotation-based transcoding systems [8] are all based on RDF, which is the basic

technology for ontologies. However, RDF alone doesn't share some basic common structures that help to describe classes of resources and types of relationship between resources. Thus, we need more facilities for expressing meaning and semantics than what has in RDF. OWL builds on RDF and RDF Schema, and uses RDF's XML syntax. However, OWL is more expressiveness than RDF and RDF Schema.

In our previous research [9], we proposed an OWL-based Extensible Transcoding System (OETS) that adopted annotation-based transcoding approach and employed the OWL. The OETS is based on an extensible transcoding policy infrastructure, with a hierarchical structure of ontologies, and can automatically render an HTML document in WAP phones. Developers can exploit the extensible transcoding capability of OETS to satisfy various layout-specific structures of HTML pages by providing dedicated domain ontologies. The main different between OETS and STMS is that the former only focus on specific markup languages transcoding, such as HTML and WML. The latter offers a generalized solution to support Web 2.0 documents transcoding.

Web 2.0 Mashups use Web API technologies to facilitate data exchange between applications and allow the creation of new applications. XMLHttpRequest is a Web API that provides scripting languages to transfer XML or other text data between a client and a server. XMLHttpRequest is used to communicate asynchronously with a server-side component and dynamically update the source of an HTML page based on the response data. The data returned from XMLHttpRequest calls are often provided by third-party database servers. For example, Google Map API uses Javascript XMLHttpRequest objects to sent HTTP requests and receive responses from server. Google Maps API is one of the most widely known Web API among current Web 2.0 Mashup applications [10].

3. Semantic-based Web 2.0 Mashups Approach for Transcoding

The section introduces a semantic multiple-layered architecture that is corresponding to the semantic layer (i.e. ontology layer) of traditional Semantic Web stack. The study adopts ontologies to describe transcoding configuration, markup languages and display platforms enables automating Web 2.0 document transcoding tasks that would normally be accomplished through manual programming.

3.1. Semantic Multiple-Layered Architecture

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The section proposes a novel method that is a semantic-based Web 2.0 Mashups approach based on SOA for Web 2.0 documents transcoding. With semantic-based mashup transcoding, the transcoded document can adopt the different approaches in different phases of transcoding, and it becomes easier for flexible, accurate and efficient transformation. An ontology is commonly defined as an explicit, formal specification of a shared conceptualization of a domain of interest. The core ingredients of an ontology include a set of concepts, a set of properties, and the relationships between the elements of these two sets. It has been widely accepted that ontology and metadata are the core elements for the Semantic Web [11]. An ontology multiple-layered architecture is composed of four layers: Meta Layer, Language Layer, Ontology Layer, and Instance Layer, as shown in Figure 1. The semantic multiple-layered framework is corresponding to the semantic layer of traditional Semantic Web stack. The study adopts OWL to create an ontology multiple-layered architecture, which provides semantic-based metadata and knowledge for STMS.

The meta layer contains rdfs:Class only. The rdfs:Class is the root class in the RDF data model. Any other class is regarded as an instance of the rdfs:Class. RDF, RDFS, DAML+OIL, and OWL are the

candidates in language layer. OWL has more facilities for expressing meaning and semantics than XML, RDF, and RDFS, thus OWL goes beyond these languages in its ability to represent machine readable content on the Web. These ontology languages are adopted to create top level ontology and domain ontology. In this study, based on OWL, Web 2.0 Document Taxonomy Ontology (WDTO) and Display Platform Annotation Ontology (DPAO) are developed in the ontology layer. The main advantages of using OWL rather than RDF alike are more efficient reasoning support, sufficient expressive power, and convenience. Another benefit of adapting the ontological approach is in its extensibility. DPAO and WDTO are developed in ontotlogy layer to provide the classification of display platforms and Web 2.0 documents, respectively. The instance layer is composed of the annotations of transcoders. Each annotation represents a domain specific individual recording the metadata of original transcoder. These ontology-based annotations are Semantic Web technical capabilities can facilitate to mashup different Web 2.0-based trsnascoders.

Figure 1. Semantic multiple-layered architecture 3.2. Web 2.0 Document Taxonomy Ontology

The class inheritance hierarchy of WDTO is depicted in Figure 2. The ml and owl are the namespaces that represents the WDTO and OWL respectively. In WDTO, we define ml:Web2Doc as an instance of the owl:Class to represent any Web 2.0

document. WDTO is a taxonomy ontology that can be used to classify the different types of markup languages to facilitate searching. The Web 2.0 documents include XHTML, KML, RSS, WML, VoiceXML, XForm, etc. Another feature that could be useful is the support for inheritance of default knowledge. Such knowledge is crucial for real semantic queries. For example, The X transcoder web

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service can convert A markup language to B markup language. If C markup language is new developed and is a subclass of A markup language, then the transcoder execution engine can infer X tanscoder web service which can convert C markup language to B markup language implicitly.

Figure 2. Semantic structure of the WDTO

3.3. Client Device Annotation Ontology

The current DPAO could be far from completeness, we rather aimed at capturing the most widely used concepts that can register the display platform profile and facilitate searching. A partial class semantic structure of DPAO is depicted in Figure 3. The dv, ml, and owl are the namespaces that represent the DPAO , WDTO, and OWL, respectively. In DPAO, we define dv:ClientDevice as an instance of the owl:Class to represent all the client devices. It provides the following information.

dv:type is the display platform type which denotes the code associated in the taxonomy.

dv:version offers the information of platform's version. dv:language denotes the platform-supported markup

language. The value of cado:language is limited to an instance of ml:Web2Doc class.

vd:information is a brief, human readable description of the display platform.

dv:vender provides the information of platform's vender.

Figure 3. Semantic structure of the DPAO

4. The Semantic-based Transcoding

Mashup Server

SOA enables the development of a new generation of dynamic or composite applications that are built by combining loosely coupled and interoperable services. The STMS plays as a service broker in SOA environment, as shown in Figure 4. It contains three main modules: Mashup Transcoding Agent, Transcoding Execution Engine and Transcoding Registry Engine.

Mashup Transcoding Agent (MTA)

MTA provides automatic discovery and matchmaking semantically-described transcoders using semantic web technologies. It listens to the client's request and interacts with other components of STMS. It enables an efficient and intelligent execution of the transcoding, which conforms to the specifications of the requesting client's device. When the front end sends a request URL with a device type, manager interface performs the following tasks:

(1) It passes the parameters, include of device type and source documents, to semantic search engine to query device profile repository to retrieve target markup language that can be accepted by the requesting client's device. (2) It passes the parameters, include of target markup language and source documents, to semantic search engine to query Ontology base to obtain semantically relevant resources, such as transcoder profiles, device profiles, and transcoding configurations. It accepts queries expressed by keywords, short descriptions or concepts with relevant ontologies, such as WDTO, or DPAO. These ontologies specify a knowledge domain using interrelated concepts, for each concept a set of lexical entities and links to related ontologies are defined.

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Transcoding Execution Engine (TEE) TEE is a high-performance service that manages reliable, scalable communications with distributed transcoders. It supports a XML parser to provide a runtime environment to execute a mashup transcoding workflow represented in WSDL document. A WSDL document contains actual implementation address of transcoders. The invocation interface executes the mashup transcoding workflow as defined, and makes the appropriate transcoder invocations. Finally, the invocation interface will complete the workflow and output a transcoded document to the manager interface. Transcoding Registry Engine (TRE) TRE supports interfaces for transcoder provider and display platform developer to register metadata. The registry is completely dynamic - a new transcoder or device can be added or existing information can be updated at any time, using the TRE. The current standards of Web Services, such as UDDI and WSDL,

do not provide a semantic capability. In the STMS, OWL-based UDDI registry is based on OWL-S to provide a semantic format for describing transcoders that facilitate the semantic search engine to discover transcoders. 5. An application with the STMS In order to demonstrate the feasibility of our STMS, an application scenario is developed to convert raw data into KML document for a Google Map or Google Earth, RSS document for a RSS reader, and into XML-based document for a Google Gadget. In this section, we demonstrate that the STMS can provide various Web 2.0-based platforms to display the same web content with different renderings, and offer the availability of existing transcoders for transcoding system developers.

Figure 4. The flow-oriented STMS architecture

5.1. Web 2.0 Document Taxonomy Ontology

We develop a Web 2.0 mashup transcoder, identified by TransKML, to convert raw data to KML. The TransKML transcoder is registered through the

registry interface of STMS. When creator submits the TransKML registration, the transcoding registry engine (TRE) creates a TransKML transcoder profile. The related information of this profile is recorded in an ontology-based annotation, as shown in Figure 5. This annotation is an instance of ml:KML class of WDTO and stored in the ontology base.

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<?xml version="1.0" encoding="UTF-8"?> ….. <!--******* The Service Profile Declaration ******** --> <ml:KML rdf:ID="Profile_KML"> <ml:approach rdf:resource="#annotated"/>

<ml:presentedBy rdf:resource="#KML"/> <ml:textDescription>

This service converts raw data to KML document </ml:textDescription> <ml:contactInformation> <ml:Actor rdf:ID="Bib2rdf-information"> ….. </ml:contactInformation> <ml:source> <ml:ParameterDescription rdf:ID="rawData"> <ml:parameterName> Raw data </ml:parameterName> <ml:refersTo rdf:resource="#HTMLFile_In"/> </ml:ParameterDescription> </ml:source> <ml:anntation> <ml:ParameterDescription rdf:ID="Annotation_File"> <ml:parameterName> Annotation_Doc </ml:parameterName> <ml:restrictedTo rdf:resource="&mfto;#OWL"/> <ml:refersTo rdf:resource="#AnnotationFile_In"/> </ml:ParameterDescription> </ml:annotation> </ml:KML>

Figure 5. Partial code of KML transcoder profile 5.2. Transcoding Workflow in the STMS

The steps of transcoding workflow in the STMS occur as follows. (1) The Web 2.0 site receives a request with a display platform type.

(2) The MTA receives a request with parameters: source URL, annotation URL, and display platform type. (3) The MTA queries ontology base, with parameters: source type being HTML, target type being Google earth, and annotation type being OWL. (4) The query result is a TransKML mashup tanscoder web service. The MTA passes TransKML profile to the TEE. (5) The TEE invokes the TransKML mashup tanscoder. (6) The TransKML mashup tanscoder completes the workflow and outputs a transcoded KML document to TEE. (7) The TEE responses the transcoded KML document to the MTA. (8) The MTA passes the transcoded KML document to the Web 2.0 site. (9) The Web 2.0 site responses the transcoded KML document to the requester.

This study implements a map-based traffic query system that is a Web 2.0 application using Web 2.0 mashup technologies, including Google Map API, XML-based traffic Web feed, AJAX, and XHTML. The map-based traffic query system is used to demonstrate how STMS supports the semantic-based mashup transcoding to convert map pages into various display platforms. It provides visually geographic information, as shown in Figure 6. Additionally, the query results can convert to various XML-based documents, such as Google gadget, RSS, KML, and Google mapplet, to display in iGoogle portal (A), RSS reader (B), Google Earth (C), and Personal Google Map (D) , respectively.

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Figure 6. STMS adopts Web 2.0 mashup transcoding to support various display platforms

6. Summary and Concluding Remarks

This study presents a Semantic-based Transcoding Mashup Server (STMS) for Web 2.0 documents transcoding by adopting technologies of Web 2.0 Mashups and Semantic Web. The STMS plays as a service broker in SOA environment, which offers a distributed computing environment in the aspect of the mashup transcoders. This enables the distribution of transcoders on different servers, the integration with the forthcoming Semantic Web 2.0, and the cooperation of different transcoding policies. Novel Semantic Web 2.0 solutions, integrated with different types of high-level OWL-based metadata, can dynamically tailor the semantic-based mashup transcoders to meet client's specific needs while hiding low-level mechanisms and implementation details from service developers and system administrators.

The main aim of this research was to investigate how Semantic Web and Web 2.0 technologies can be used to develop Semantic-based Transcoding Mashup Server (STMS) in the SOA environment. This aim was

tackled by a four steps approach, as articulated in the following objective: 1. Research the integration of Semantic Web and Web

2.0 Mashups for Web 2.0 documents transcoding. 2. Develop a formal framework for STMS in the SOA

environment, and demonstrate how a semantic-based mashup transcoding can be executed.

3. Establish WDTO and DPAO ontologies to describe Web 2.0 documents and display platforms that enable automating Web 2.0 document transcoding.

4. Implement a prototype STMS for converting raw data forms into various Web 2.0 display platforms.

Future study will be to extend the accessibility of the STMS towards the workflow QoS (Quality of Service) model for global optimization and composition of transcoders. For instance, a better cost estimation module could effectively add flexibility to the STMS for criteria selection.

7. References [1] T. O'Reilly. What Is Web 2.0. http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html

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[2] I. V. Yakovlev. Web 2.0: Is It Evolutionary or Revolutionary? IEEE IT Professional, 9 (6) (2007), pp. 43-45. [3] S. Murugesan. Understanding Web 2.0. IEEE IT Professional, 9 (4) (2007), pp. 34-41. [4] C. Schroth and T. Janner, Web 2.0 and SOA: Converging Concepts Enabling the Internet of Services. IEEE IT Professional, 9 (3) (2007), pp. 36-41. [5] A. Ankolekar, M. Kr?tzsch, T. Tran, and D. Vrandecic:. The two cultures: Mashing up Web 2.0 and the Semantic Web. Journal of Web Semantics, 6 (1) (2008), pp. 70-75. [6] D. Raggett, HTML TIDY. Available from: http://www.w3.org/People/Raggett/tidy/. [7] H. Ishikawa, H. Suzuki, H. Ueno, T. Gotoh. Experiment on and analysis of mobile content transformation using XSLT. Software-Pract Exper 36(7) 2006, pp.761-783. [8] H. Hori, Annotation-based Web Content Transcoding, in Proc. Ninth Int'1 WWW Conf. 2000, Elsevier: Amsterdam. pp. 197-211. [9] I.C. Hsu and S.J. Kao, An OWL-based extensible transcoding system for mobile multi-devices. Journal of Information Science,31(3) 2005, pp. 178-195. [10] Y.-J. Wu, Y. Wang, and D. Qian. A Google-Map-Based Arterial Traffic Information System. Proceedings of the 2007 IEEE, Intelligent Transportation Systems Conference, Seattle, WA, USA, 2007, pp. 968-973. [11] N. Shadbol, T. Berners-Lee, and W. Hall, The Semantic Web Revisited. IEEE Intelligent Systems, 21(3) 2006, pp. 96-101.

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