Personalized web feeds based on ontology technologies

I-Ching Hsu

Published online: 13 December 2011# Springer Science+Business Media, LLC 2011

Abstract The rapid development of the social Web hasresulted in diverse Web 2.0 applications for accessing vari-ous Web feeds such as Weblogs, news headlines, businessproducts, real time information and Podcasts. Due to themasses of Web feeds available on the Internet, a major chal-lenge is how to access them efficiently. Conventional methodsof manually finding and matching keyword for Web feeds aretime-consuming and inaccurate. This study addresses thisissue by defining a Personalized Web Feeds Framework(PWFF) for integrating ontology technologies into Web feedsand user profiles. The proposed PWFF is used to develop anOntology-based Personalized Web Feed Platform (OPWFP)that provides customized Web feeds for personnel needs. Todemonstrate the feasibility of OPWFP, the experimentalresults illustrate the efficiency and effectiveness of the pro-posed approach.

Keywords Web feed . Ontology . Semantic web .Web 2.0 .

RSS

1 Introduction

Within the last two to three years, the Internet has greatlychanged our way of sharing resources and delivering infor-mation. As well known, Web 2.0 is recognized as the nextgeneration of Web applications proposed by T. O’Reilly(O’Reilly 2005). The main feature of Web 2.0 applicationsis that they provide a medium for the sharing and exchange

of resources (Murugesan 2007; Zhang et al. 2011). Theseresources, such as Web feed and Web API, allow Webdevelopers to take advantage of these resources to enrichtheir own applications or produce new integrated solutionsby integrating resources, which they could not have providedon their own. Many Internet companies have enabled easyaccess to the Web resources that they provide. Anyone cancreate a new integrated application with these Web resources.This new way of building applications has also led to a re-conceptualization of the Internet as a resource rather than as atechnology. An application that combines resources fromdifferent websites to produce a new Web application is calleda Web 2.0 Mashup (Murugesan 2007).

Most of the Web APIs are constructed based on the WebServices architecture (Bell et al. 2007; Sun et al. 2010a).They hide the detailed Web Services protocols from thedevelopers, making them easier for the developers to use.AWeb feed contains a structured information source, whichis written in XML to provide machine-readable content onthe Web. This means that Web feeds can be used toautomatically transfer information from one website toanother, without any human intervention. Web feedsallow websites to publish frequently updated contentsuch as Weblog, news headlines, real time informationor business production. Most of the existing Web APIsor Web feeds are based on XML standard. The mainlimitation of XML is that it focuses on syntax andformat rather than semantics and knowledge. Hence,even though XML has the advantage of data sharingand exchange, it lacks the semantic metadata to providereasoning and inference functions. These functions arenecessary for the computer-interpretable descriptions,which are critical in the area of dynamic Web resourcescomposition, Web resources reusability and autoexecpersonalized Web resources generation. This is why

I.-C. Hsu (*)Department of Computer Science and Information Engineering,National Formosa University,64, Wenhua Rd., Huwei Township,Yunlin County 632, Taiwane-mail: hsuic@nfu.edu.tw

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the existing Web resources can not dynamically providecustomized Web resources for personnel preference.

At present, many studies (Acuña et al. 2010; Kumarand Mastorakis 2010) explore how to use Semantic Webtechnologies to enhance the intelligent applications for WebAPIs. However, only a few papers (MahmoudiNasab andSakr 2009; Maio et al. 2010) study how to facilitate WebFeeds in intelligence application. This study addressesthese issues of Web feeds developing an Ontology-basedPersonalized Web Feeds Platform (OPWFP) based onontology technologies to describe user preference andannotate Web Feeds to facilitate the automatically generatedpersonalized Web feed.

RSS (Winer 2003) and Atom (Nottingham 2005) arecurrently the two main formats of Web Feed. RSS is afamily of Web Feed formats specified in XML standard.There are three different version of RSS, namely Rich SiteSummary, RDF Site Summary and Really Simple Syndication.The Really Simple Syndication (RSS 2.0) is the most widelyused. Unlike RSS, Atom is proposed RFC 4287 and is definedwith XML schema. In addition, the RDF Site Summary isdeveloped based on RDF model. RDF and related specifica-tions are designed to make statements about the resource on theWeb, without the need to modify the resource itself. Thisenables document authors to annotate and encode the semanticrelationships among resources on the Web. However, RDFalone does not provide common schema that helps to describethe Web feeds classes and represent the types of relationshipsbetween Web resources. A specification with more facilitiesthan those found in RDF to express semantics flexibly isneeded. The Semantic Web (Shadbolt et al. 2006)can helpsolve these problems.

Semantic Web is rapidly becoming a reality through thedevelopment of ontology markup languages such as RDF(Resource Description Framework), RDF Schema (Brickleyand Guha 2004), DAML+OIL, and OWL (Web OntologyLanguage) (Smith et al. 2004). The ontology is the mostimportant and core technology of Semantic Web (Brewsterand O’Hara 2007). According to Fig. 1, these ontologymarkup languages enable the creation of arbitrary domainontologies that support the unambiguous description of Web

content. RDF and RDF Schema are languages for resourcesdescription and their types, and thus are basic buildingblocks for the Semantic Web. OWL is essentially an XMLencoding of an expressive Description Logic, builds uponRDF and includes a substantial fragment of RDF Schema.OWL has more facilities for expressing meaning and seman-tics than RDF and RDFS, thus OWL goes beyond theselanguages in its ability to represent machine-readable contenton theWeb. The OWL is a revision of the DAML+OIL, and isintended to be standardized and is becoming a broadly accept-ed ontology language for the Semantic Web. Therefore, OWLshould integrate well with other W3C recommendations andnot change too much from DAML+OIL.

To enhance the knowledge representation of theXML-based markup language, the traditional SemanticWeb approach is to upgrade the original XML-based toontology-based markup language (Hsu 2009a; Hsu et al.2009b). The upgrade mentioned above from XML-basedRSS (i.e. Really Simple Syndication) to RDF-based RSS(i.e. RDF Site Summary) is an example. This approach islimited in that the original XML-based markup language hasto be replaced with a new RDF-based markup language,causing the compatibility problems with existing data appli-cations. This study proposes a Personalized Web FeedsFramework (PWFF) based on ontology technologies, whichcombines the Composite Capability/Preference Profiles (CC/PP) ontology (Kiss 2007), Friend of a Friend (FOAF) ontol-ogy (Brickley and Miller 2010), and domain ontology. InPWFF, ontology technologies can be integrated with WebFeeds to enhance computational reasoning, and the originalWeb Feeds can be retained to cooperate with ontologies andrules. That is, PWFF does not change the original schema ofWeb Feeds. Hence, the existing Web Feeds metadata docu-ments can continue to be used.

The CC/PP is developed by W3C to describe how themanagement of client devices and user profile information.It is an RDF-based language used to create profiles thatdescribe user client capabilities and preferences. This studyuses CC/PP ontology as the main structure of userpreference. The FOAF is developed based on RDFlanguage for the representation of personal profile infor-mation and social groups. It can be used to describevarious characteristics of people, such as name, title, age,topic_interest. The FOAF ontology can be extended so thatnew properties can be included in the description of userpreferences. This study employs the topic_interest propertyof FOAF to describe user preferences.

Based on the PWFF, this study implement an Ontology-based Personalized Web Feed Platform, called OPWFP,which is composed of user profile, ontology base, searchengine, and inference engine. OPWFP employs ontologytechnologies, including CC/PP ontology, FOAF ontology,and domain ontology, to facilitate metadata and knowledgeFig. 1 Layered approach to ontology language development

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development to provide customized Web feeds for personnelneeds. It supports ontology approach for finding dynamiccorrelations of a certain Web feed. Such dynamic customizedWeb feed is desirable for personalized needs. Firstly, it iscustomized for each individual user, based on what metadataand ontology the user profile has shown so far. Secondly,because the content or category of a user profile may keepchanging, dynamic customization provides more up-to-dateWeb feeds than a static design. Thirdly, as the masses of Webfeeds available on the Internet raise a challenging task over theeffective use. Lastly, it can also be used at run-time to help inthe decision of what Web feed content to deliver to the user.

This study mainly aims to use ontology technologies toprovide a customized Web feed for personnel needs. Thisaim was tackled by a three step approach. (1) The formaldefinitions of Personalized Web Feeds Framework (PWFF),including Web Resource, Web Feed, User profile, Ontology,and Semantic Mapping Mechanism, are presented as ametamodel. (2) The Ontology-based Personalized WebFeed Platform (OPWFP) is developed based on the meta-model to demonstrate the feasibility of PWFF. OurOPWFP can be associated with various domain ontolo-gies to offer customized Web feeds for personnel need.(3) The study realizes how ontology technologies can beintegrated with Web 2.0 applications.

The rest of this paper is organized as follows. The nextsection mentions some related works. Section 3 describesthe personnel Web feed framework. The architecture ofOPWFP is introduced in Section 4. In Section 5, a concreteexample to illustrate how domain ontology, user profile andcustomized Web feed can be developed based on the PWFF.Section 6 demonstrates how the prototype OPWFP works.Section 7 presents a preliminary experimental study. Finally,concluding remarks are given in Section 8.

2 Related work

The Semantic Web is an extension of the current Web inwhich information is given well defined meaning, betterenabling computers and people to work in cooperation.Many studies (Hsu 2009a,b; Hsu et al. 2009a,b; Sun et al.2010b) adopt Semantic Web t to build intelligent applica-tions in various domains. One major feature of Web 2.0 is toadopt Web feeds to build a more maintainable and cooper-ative Web. Web feeds can be considered as resources thatare accessible over the Internet. Therefore, Semantic Webcan be used to enhance the intelligence, reusability, andinteroperability of Web feeds. In recent years, severalresearch studies have focused on adopting ontology toenhance the interoperability of Web 2.0 applications, such asWeb services composition (Talantikite et al. 2009; Sbodio etal. 2010), semantic Weblog (MahmoudiNasab and Sakr

2009; Maio et al. 2010), and Semantic Web 2.0 (Greaves2007; Hsu 2009a). But, they do not address the issueof how ontology technologies can be integrated intoWeb feeds and user profiles to provide customized Webfeeds for personnel needs. The study proposed OPWFP is firstto address the issue.

In (Maio et al. 2010), the authors analyze RSS Feeds todiscovery the structural similarity and then to automaticallygenerate an fuzzy ontology for the activities of classification.This approach achieves a semantic aggregation of Web feedsthrough the analysis of the Web feed content. The SemanticWeb technologies based on ontologies can improve differentaspects of the management of Web resources. Indeed,ontologies are a means of specifying the concepts andtheir relationships in a particular domain of interest. Thepaper (MahmoudiNasab and Sakr 2009) proposed theFeedRank system as a semantic-based manager of Webfeeds. The FeedRank system can automatically annotatethe received Web feeds as well as rank the Web feeds basedon user queries. In (Samper et al. 2008), the authorsdeveloped the NectaRSS based on the well-known vectorspace model for user profiles and new documents. TheNectaRSS provided an adaptive recommendation model tohelp recommend RSS feeds. In the above papers, researchersemployed various approaches and technologies to improve theshortcomings of current Web feed readers. However, theyhave to ignore the user preferences may change at any time.

3 Personalized web feed using ontology

Formal definitions of a Personalized Web Feed Framework(PWFF), which include Web Resource, Web Feed, UserProfile, Ontology, and Semantic Mapping Mechanism, aredeveloped for user preferences. The framework of person-alized Web feed is display in Fig. 2. An illustrative exampleof personalized Web feed in accordance with the PWFF isshown in Fig. 3.

Definition 1. (personalized web feeds framework) ThisPersonalized Web feeds can be formally defined, and iscalled the PWFF.

The framework consists of a tuple PWFF ¼ <WR;WF;UP;O; SMM> where

WR denotes a collection of Web Resources (refer toDefinition 2 for WR).

WF denotes a collection of Web feeds (refer toDefinition 3 for WF).

UP denotes a collection of user profiles (refer toDefinition 4 for UP).

O denotes a collection of ontologies (refer toDefinition 5 for O).

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SMM denotes the semantic mapping mechanism thatdefines how WF and UP can be combined withontology-based knowledge (refer to Definition 6 forSMM).

The formal definitions of Personalized Web FeedsFramework (PWFF), including Web Resource, Web Feed,User profile, Ontology, Semantic Mapping Mechanism arepresented as a metamodel. The proposed PWFF allowspersonalized Web Feed developers to specify the domainthat provides the relevant structural properties of Web 2.0applications to be represented at conceptual level. A con-crete example of PWFF is given in Example 1.

Definition 2. (web resource) A Web resource is a tupleWR ¼ <Rid; Uid; AWR; TWR>, where:

Rid denotes an unique Web resource identifier, such asURI.

Uid denotes an unique identifier of Web resource owner.AWR denotes a list of Web resource attributes.TWR denotes a type of Web resource.

Web resources are Web-accessible entities, such asWeb pages, Weblog, website, news, real-time informa-tion, etc. They are distributed in the Internet and areidentified by URI.

Definition 3. (web feed) A Web feed is a tuple WFMD ¼<WF; IT ;Caw;Cai; TWF ;Oth>, where

WF denotes a Web feed.IT denotes an item that defines an article or document

in a Web feed..Caw denotes a classification metadata that describes the

meanings or abstract concepts of Web feed (WF).Cai denotes a classification metadata that describes the

meanings or abstract concepts of item (IT).TWF denotes a type of Web feed.Oth denotes the other metadata in a Web feed.

In this paper, Web feeds were divided into four categories,including, Really Simple Syndication, RDF Site Summary,Really Simple Syndication, and Atom. The metadata model of

Fig. 2 Personalized Web feed framework

Fig. 3 Example of personalizedWeb feed in multiple-layeredsemantic framework

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Web feed can be broken up into three categories. The threecategories of metadata elements are: Channel category, Itemcategory, and Classification category. AWeb feed consists of achannel element with more than one item element. This studyfocuses on Classification category. The Classification catego-ry can be used to describe where the Web feed channel anditem fall within a particular classification system. A concreteexample of RSS-based Web Feed is shown in Fig. 15.

Definition 4 (user profile) A RDF-based metadata docu-ment of a user profile is a tuple UPMD ¼ <UP;PO;Oth>;

where

UP denotes a user profile.PO denotes a profile ontology that describes the semantic

concepts of user agent.Oth denotes the other metadata in the RDF-based meta-

data document.

Several user profile standards have developed based onontology technologies to enhance the interoperability ofWeb-based applications, such as CC/PP, UAProf, FOAF,and UPOS (Sutterer et al. 2010). A concrete example ofUser Profile is shown in Fig. 13.

Definition 5. (ontology) A Web-based ontology O∈K is atuple O ¼ <C;P; a; b; g;Σ;Π>, where

C denotes a set of concepts representing classes in anontology.

P denotes a set of relations representation properties in anontology.

Α denotes the hierarchical relation function for classes.α: C→C, where aðc1Þ ¼ c2 means that c1 is a subclassofc2:

This hierarchical relation can be used to determine if twoclasses have subclass/superclass relationship (Guarino andWelty 2004).

β denotes the hierarchical relation function for properties.β: P→P, where bðp1Þ ¼ p2 means that p1 is a sub�property of p2:

γ denotes the attribute relation function between classes.γ: P→C x C, where gðp1Þ ¼ c1;c2ð Þmeans that domainof p1 is c1 and range of p1 is c2:

Σ denotes a set of ontology axioms, expressed in anappropriate description logic.

Π denotes a set of RDF-based ontology language, such asRDF schema, DAML+OIL, or OWL.

An ontology is commonly defined as an explicit, formalspecification of a shared conceptualization of a domain ofinterest (Studer et al. 1998). It describes some application-relevant part of the world in a machine understandable way.

The reasoning capabilities of OWL will be discussed in thesection 5.2.

Definition 6. (semantic mapping mechanism) A semanticmapping mechanism is a tuple SMM ¼ <WFMD;WS; IT ;E;UPMD;U ;C;Λ>, where

WFMD denotes a Web feed metadata document(as defined in Definition 3).

UPMD denotes a user profile metadata document(as defined in Definition 4).

WS denotes a Web site that is mainly describedby the WFMD.

IT denotes an item in a Web feed.E denotes a Web-based entity that is mainly

described by the IT.U denotes a user that is mainly described by theUPMD.C denotes a set of concepts representing classes in

an ontology.Λ denotes a classification mapping function.

Λ: {WS, E, U}→C

The classification mapping function can make a classifi-cation tag (element) to refer an ontology class and acquirean additional semantic knowledge about the Web resource.

Λ lo1ð Þ ¼ c1 means that the classification of theWeb resource

lo1 is set to class c1

In the PWFF, category and topic_interest elements areused to provide extra semantic information for Web feed anduser profile, respectively. The category element can indicatea character of the Web feed has, while the topic_interestelement describes the user interests. The content of bothcategory and topic_interest elements are URI references thatidentify the Web resource of the intended property. EachWeb feed can refer to an additional semantic through acategory element to set a specific ontology class. Similarly,each user profile can refer to additional semantics through atopic_interest element to set a specific ontology class.

Example 1 Consider the concrete example given in Fig. 3,where WR is an HTML page on the Internet and WF is aRSS feed. The Computer Ontology, O, is developed basedon OWL, and UP is a CC/PP document. Additionally, theSMM is a set of red dotted lines. Each red dotted line canrefer to additional semantics through a semantic element toset a specific ontology class or property.

4 Architecture of OPWFP

The OPWFP can be associated with various domain ontol-ogies and metadata. The basic function of a Web feed is to

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provide metadata for Web resources. The OPWFP serves asa Web feed broker that supports ontology-based reasoningto automatically generate customized Web feed based onpersonalized needs. The ontology-based reasoning approachprovides OWL-based ontologies based on description logicsto provide sound and decidable reasoning, including sub-class, class intersection, class union, class complement,class disjoint, and class equivalence. Therefore, OWL-based ontologies can enhance the semantic reasoning capa-bilities of user profiles and Web feeds.

The core components of OPWFP include the ManagerInterface, Document Repository, Customized Module andSyndication Engine. The flow-oriented OPWFP architectureis depicted in Fig. 4.

Manager Interface: listens to the user’s request andaccesses to User Profile Base. The user registry iscompletely dynamic—a new user can be added orexisting user profile can be updated at any time, usingthe Manager Interface.Document Repository: consists of User Profile Base,Ontology Base, and Web Feed Base. The User ProfileBase is an annotation database that consists of RDF-based documents. Each RDF-based document is an userprofile to describe the user’s preferences. OntologyBase is composed of OWL-based ontologies that pro-vide semantic reasoning, and plays the same role as theknowledge base in a traditional expert system. WebFeed Base contains of various Web feed, such as RSS2.0, RSS 1.0, or Atom.Customized Module: is comprised of three compo-nents: Jena (McBride 2002), Search Engine, and Web

Feed Generator. Jena is an inference engine that providesan ontology-based reasoner for semantic Web-based lan-guage, including RDF, RDFS, OWL, and rule. SearchEngine supports XPath query on an XML-based metadatadocument collection within theWeb FeedBase.Web Feedgenerator is a composer to create a personalizedWeb feed.Syndication Engine: is a clawer which collects theWeb feeds from distributed Web 2.0 application web-sites, such as Weblog, news website, government web-site, and business website.

The information flow of the OPWFP occurs as follows.

1. This step is the user registration.

1.1 The user inputs personal information, includinginterested topics, through the Manager Interface.

1.2 The Manager Interface creates a user profile andsaves to User Profile Base.

2. The step is a pull-based interaction scheme that accom-plishes the following tasks.

2.1 The Syndication Enginepulls the Web feeds from distributed Web 2.0application websites.

2.2 The Syndication Engine parses the Web feeds tofilter the available Web feed items, and then savesinto the Web Feed Base.

3. This step is ontology-based reasoning approach to cre-ate personalized Web feeds.

3.1 The Jena inference engine accesses the UserProfile Base to retrieve the user profile and extractthe user interested topics.

Fig. 4 The flow-orientedOPWFP architecture

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3.2 The Jena inference engine loads the domain ontol-ogy, and then executes ontology-based reasoningbases on the interested topics to derive potentialinterested topics or constrains. The capabilities ofontology-based reasoning are described inSection 5.2.

3.3 The result of ontology-based reasoning is passedto the Search Engine.

3.4 The Search Engine query the Web Feed Base toretrieve relevant Web feed items.

3.5 These Web feed items is passed to the Web FeedGenerator.

3.6 The Web Feed Generator packages these Web feeditems into a personalized Web feed, and thenpushes this personalized Web feed to user.

5 Domain ontology development

This section develops Computer Ontology to demonstratethe ontology-based reasoning capabilities of OWL Class.

5.1 Ontology development

The core ingredients of an OWL-based ontology include a setof concepts, a set of properties, and the relationships betweenthe elements of these two sets. The Computer Ontology offersthe computer classification in a high abstraction level and is

used to describe the semantic-based relation between classes,such as Computer, MobileDevice, PC, Server, Printer, etc,involved in the computer domain. Figure 5 shows the seman-tic structure of Computer Ontology as a UML class diagram.The UML class diagram has as goal to give a graphicaloverview of the domain concepts and the subclass relationsamong them.

5.2 Reasoning capabilities of OWL class

The following six constraints present some partial code ofthe Computer Ontology to illustrate OWL-based descriptionlogics for class, including subclass, class intersection, classunion, class complement, class disjoint, and class equiva-lence, respectively.

5.2.1 Constraint 1. subclass

The subClassOf property of OWL declares a subclass rela-tion between two OWL classes. If the class A is defined as asubclass of class B, then the set of objects in the class Ashould be a subset of the set of objects in the class B. Thatis, class B is a superclass of class A.

Fig. 5 The UML diagram for the computer ontology

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Semantic meaning The MobileDevice is a subclass of theMobilePC. All objects in the MobilePC class are also instan-ces of the MobileDevice class, as shown in Fig. 6.

Semantic meaning The PrinterServer is a subclass of theServer and is a subclass of Printer. That is, a PrinterServeris a server that offers a Printer. However, by using multiplesubClassOf elements there may be Servers which provide aPrinter that are not PrinterServer. Logic in the OWL, theconjunction of two subClassOf statements is a subset of theintersection of the classes, as shown in Fig. 7.

5.2.2 Constraint 2. Class intersection

The intersectionOf property of OWL provides a link from aclass to a list of class statements. The intersectionOf property ofOWL can be regarded as representing the AND operator onclasses. An intersectionOf statement defines a class to includeall objects that are members of the all class statements in the list.

Semantic meaning The TouchKeyboard class is the inter-section of the TouchPanel class and Keyboard class, asshown in Fig. 8.

5.2.3 Constraint 3. Class union

The unionOf property of OWL offers a relation from a classto a list of class statements. The unionOf property of OWL

can be regarded as representing the OR operator on classes.A unionOf statement defines a class to include all objectsthat occur in at least one of the class statements in the list.

Semantic meaning The MobilePhone class is the union ofthe TouchPanel class and Keyboard class, as shown inFig. 9.

5.2.4 Constraint 4. Class complement

The complementOf property of OWL provides a link from aclass to a list of class statements. The complementOf prop-erty of OWL can be viewed as representing the NOT

Fig. 6 Single subclass

Fig. 7 Multi-subclasses

Fig. 8 The intersection of TouchPanel class and Keyboard class

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operator on classes. A complementOf statement defines aclass to include all objects that are not members of thecomplement classes in the list.

Semantic meaning The RackServer class description con-tains all objects that do not belong to the TowerServer class.,as shown in Fig. 10. Additionally, the Server class is justcomposed of RackServer class and TowerServer class.

5.2.5 Constraint 5. Class disjoint

The disjointWith property of OWL declares a disjoint rela-tion between two OWL classes. If the class A is defined as adisjointWith of class B, then the object in the class A should

be not a member of the class B. That is, class A and class Binvolved have no individuals in common.

Semantic meaning This definition of Desktop class indi-cates that a Desktop instance is a PC and cannot also be aLaptop, Tablet, or Notebook. The Desktop class definitiononly states that there are no instances of Desktop whichoverlap with Laptop, Tablet, or Notebook. It does not pre-scribe that all classes are disjoint, as shown in Fig. 11.

5.2.6 Constraint 6. Class equivalence

The equivalentClass property of OWL specifies that a classis equivalent to another class. The equivalentClass propertyimplies the classes have the same concept rather than classequality.

Semantic meaning The MobilePhone class is equivalent toPDA class. The concept of MobilePhone is related to, butnot equal to the concept of the PDA.

6 Prototype system demonstration

In order to demonstrate the feasibility of PWFF, a prototypeWeb-based OPWFP is implemented to provide customized

Fig. 9 The union of TouchPanel class and Keyboard class

Fig. 10 The TowerServer class is a complement of the RackServerclass

Fig. 11 The Desktop class is disjoint with Laptop, Tablet, or Notebookclass

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Web feeds for personnel preferences. The demonstrationshows how customized Web feeds can be automaticallygenerated based on user profile.

6.1 User profile

Users must be able to provide the OPWFP with informationabout their own some interested topics. These interestedtopics are selected through the interface of OPWFP, asshown in Fig. 12. This interface provides the classificationtree of Computer Ontology, where the user’s topics of inter-est are selected.

When user submits the Web form, the OPWFP createsX120149 user profile. The partial code of X120149 userprofile is shown in Fig. 13. The user profile is described

Fig. 12 The Create user profile page

Fig. 13 The partial code of X120149 user profile

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with ontologies, including CCPP ontology,1 FOAF ontology,and domain ontology. This study adopts CC/PP profileas the main structure of user profile. The FOAF (Friendof a Friend) language is used to describe the personnelinformation of user. The user profile contains one ccpp:component statement, which are represented by thePerson class of FOAF. The Person class contains infor-mation about the user and the interest topics. The foaf:topic_interest statement references a specific ontologyclass through the rdf:resource property. The user speci-fies the topics he is interested in. Only the topics thatcover exactly the subjects he selects or any of its sub-topics will comply with user wishes.

The following description illustrates how ontology canbe adopted to annotate user profile using the concrete ex-ample in the Fig. 13. The user profile is annotated with thefollowing metadata.

& User ID “X120149” represents the “I-Ching Hsu” user.

& The ccpp:component element addresses the “I-ChingHsu” is an instance of Person class. Therefore, “I-Ching Hsu” can inherit semantic relationships fromPerson class of FOAF ontology.

& An interest topic is associated with an URL “http://140.130.34.170/sdl/onto/Computer#MobilePC”, whichis the MobilePC class of Computer Ontology. It is im-plied that user will be interested in all subclasses ofMobilePC class, such as Laptop class, Tablet class, andNotebook class.

& Another interest topic is associated with an URL“http://140.130.34.170/sdl/onto/Computer#TouchPanel”,which is the TouchPanel class of Computer Ontology.Similarly, user will be interested in all subclasses ofTouchPanel class.

6.2 Personalized web feed

This study implements an Ontology-based PersonalizedWeb Feed Platform (OPWFP) that serves as a Web feedbroker that supports ontology-based reasoning to auto-matically generate customized Web feed based on

Fig. 14 The personalized Web feed based on X120149 user profile

1 http://www.w3.org/2006/09/20-ccpp-schema

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personalized needs. Furthermore, the proposed OPWFPprovides sharing information on two different schemes,including push-based scheme and pull-based scheme.The pull-based scheme allows OPWFP to pull the shar-ing information from the heterogeneous and distributedWeb feeds. The push-based scheme allows OPWFP topush the personalized Web feed to user in real-time.According to the X120149 user profile described inthe previous section, the Jena inference engine ofOPWFP can execute ontology-based reasoning to infer

the hidden interest topics, including Laptop, Tablet, andNotebook. The Web Feed Generator of OPWFP creates thepersonalized Web feed, as shown in Fig. 14. The partial codeof the personalized Web feed is shown in Fig. 15.

7 Experimental results

After describing the framework for enhancing the reasoningcapabilities of Web feeds through OPWFP, a preliminary

Fig. 16 An illustration of a Webfeed item

Fig. 15 The partial code ofX120149 personalized WebFeed

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experiment is performed to test the expressiveness of thePWFF and the reasoning capabilities of personalized Webfeeds. This study uses three indexes, Precision, Recall andF1 Measure, to evaluate the performance of the proposedOPWFP. Precision measures the capability of our approachof finding Web feed items considered relevant by the user,whereas Recall measures the capability of our approach ofnot missing Web feed items relevant to the user. They areformally defined as follows:

NRDQ 0 number of relevant Web feed items retrievedby Search EngineTNRD 0 total number of existing relevant Web feeditems in the datasetTNDQ 0 total number of Web feed items retrieved by aSearch EnginePrecision 0 NRDQ / TNDQRecall 0 NRDQ / TNRDF1 measure 0 (2 * Precision * Recall) / (Precision +Recall)

The test dataset contained 3270 Web feed items. EachWeb feed item contains at least one category element asso-ciated with a domain attribute to refer to a specific class ofComputer Ontology. The content of category element isregarded as a keyword of the Web feed item. An illustration

of a Web feed item in the data set is given in Fig. 16. ThisWeb feed item contains the keyword “HP NB 331”. Also, itis annotated with the ontology class “MobilePC” and“Keyboard”.

The experiment is performed by 5 volunteers hav-ing different preferences, shown as in Table 1. Thepotential interest topics and related semantic constrains aresummarized in Table 2. The potential interest topics areobtained by subclass relationship. The related semantic con-strains are described in Section 5.2, including class intersec-tion, class union, class complement, class disjoint, and classequivalence. This study has implemented and compared twoapproaches of personalized Web feeds—ontology-basedreasoning and keyword matching. The test results of usingthe two approaches are summarized in Table 3. Figure 17illustrates the results in diagrams.

The experimental results are reported in Fig. 17. It can beobserved that the Precision, Recall and F1 Measure of ourapproach (i.e. ontology-based reasoning) have better perfor-mance than the keyword matching. This indicates that thepersonalized Web feed by ontology-based reasoning aremore accurate because these Web feed items have similarontology-based concepts. Thus, ontology-based reasoningcan improve the efficiency of personalized Web feeds.Additionally, use of ontology-based reasoning has increasedin Precision, F1, and particularly Recall. Therefore,

Table 1 The interest topics of users

User ID Classification : ontology#class Interest topics : ontology#class

X120149 http://xmlns.com/foaf/0.1/#Person http://140.130.34.170/sdl/onto/Computer#MobilePC

X120149 http://xmlns.com/foaf/0.1/#Person http://140.130.34.170/sdl/onto/Computer#TouchPanel

Z321091 http://xmlns.com/foaf/0.1/#Person http://140.130.34.170/sdl/onto/Computer#Server

P124628 http://xmlns.com/foaf/0.1/#Person http://140.130.34.170/sdl/onto/Computer#Desktop

A432990 http://xmlns.com/foaf/0.1/#Person http://140.130.34.170/sdl/onto/Computer#MobilePhone

Y921772 http://xmlns.com/foaf/0.1/#Person http://140.130.34.170/sdl/onto/Computer#TouchKeyboard

Table 2 The potential interest topics and related semantic constrains

User ID Annotated interest topics Potential interest topics Constrain

X120149 MobilePC, TouchPanel Laptop, Tablet, Notebook

Z321091 Server TowerServer, RackServer, PrinterServer Constrain 4

P124628 Desktop Constrain 5

A432990 MobilePhone TouchPanel, Keyboard, TouchKeyboard Constrain 3. Constrain 6

Y921772 TouchKeyboard Constrain 2

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ontology-based reasoning can enhance identification of Webfeed items relevant to the user.

8 Concluding remarks and future work

The Web feed was developed based on the XML/RDFstandard to facilitate the search, evaluation, sharing, andexchange of real-time information over the Internet. Themain problem of existing Web feed applications is theirlack of personalization features for user preferences.This study therefore defined a Personalized Web FeedsFramework (PWFF) based on ontology technologies thatenhance the semantics and knowledge representation ofWeb feeds and user profiles. The proposed PWFF isused to develop an Ontology-based Personalized WebFeed Platform (OPWFP) that provides customized Webfeeds to improve the performance and accuracy of Webfeed applications.

Ontology technologies can represent semantic implica-tions to provide decidable reasoning support, but they haveno mechanism for defining arbitrary, multi-element antece-dents. For example, description logics of ontology can’trepresent the complex non-monotonic rules (Matheus et al.

2003; Hsu 2009b). Further research will be to extend thePWFF with Semantic Web technologies to support addition-al intelligence in the developed Web 2.0-based applicationsby deducing new adaptation rules. By integrating logic rulesinto PWFF, this approach can describe additional semanticsof Web 2.0-based applications. Semantic Web Rule lan-guage (SWRL) (Horrocks et al. 2003) seems to be the mostappropriate language to further study, because it currently isthe main language for representing rules in the SemanticWeb.

In recent years, cloud Computing (Armbrust et al. 2010)has emerged as a new paradigm to facilitate for deploying,managing and offering services over the Internet. It providesthe advantages of low cost, high performance, and scalabil-ity. However, there is still no widely accepted definition anduniform standards for cloud computing. Context-awareness(Sutterer et al. 2010) is a very important feature for cloudcomputing to enhance current Web 2.0-based applicationsby finding right context information and right context serv-ices in the right place at the right time. Another futuredirection of development is to investigate how to integratecontext-aware technologies into user profile to developcontext-aware personalized Web feed applications for cloudcomputing environment.

Fig. 17 The test results of per-sonalized Web feed comparedwith different users

Table 3 Test resultsPersonalized Web Feed Ontology-based Reasoning Keyword Matching

Precision Recall F1 Measure Precision Recall F1 Measure

X120149 0.848 0.814 0.831 0.587 0.468 0.521

Z321091 0.851 0.827 0.839 0.663 0.589 0.624

P124628 0.826 0.804 0.815 0.608 0.492 0.544

A432990 0.859 0.823 0.841 0.691 0.595 0.639

Y921772 0.821 0.825 0.823 0.617 0.492 0.547

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I-Ching Hsu received a Ph.D. degree in the computer science depart-ment at the National Chung-Hsing University of Taiwan in 2007. Dr.Hsu has worked at Chung-Shan Institute of Science & Technology inthe area of information engineering technologies since 1991. He iscurrently an associate professor of Computer Science and InformationEngineering at National Formosa University, Taiwan. He has partici-pated and directed projects in the area of Web technologies and UMLmodeling applications. His current research aims at the creation andstudy of Web Technologies, Cloud Computing, IoT, and SoftwareEngineering.

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