Generation of Personalized Ontology Based on Consumer Emotion and Behavior Analysis

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. , NO. , MONTH 2011 1

Generation of personalized ontology based onconsumer emotion and behavior analysis

A.C.M. Fong, Senior Member, IEEE, Baoyao Zhou, Siu C. Hui, Senior Member, IEEE,Jie Tang, Member, IEEE, and Guan Y. Hong

Abstract—The relationships between consumer emotions and their buying behaviors have been well documented. Technology-savvy consumers often use the web to find information on products and services before they commit to buy. We propose asemantic web usage mining approach for discovering periodic web access patterns from annotated web usage logs, whichincorporate information on consumer emotions and behaviors through self reporting and behavioral tracking. We use fuzzylogic to represent real-life temporal concepts (e.g. morning) and requested resource attributes (ontological domain conceptsfor the requested URLs) of periodic pattern-based web access activities. These fuzzy temporal and resource representations,which contain both behavioral and emotional cues, are incorporated into a Personal Web Usage Lattice that models the user’sweb access activities. From this, we generate a Personal Web Usage Ontology written in OWL, which enables semantic webapplications such as personalized web resources recommendation. Finally, we demonstrate the effectiveness of our approach bypresenting experimental results in the context of personalized web resources recommendation with varying degrees of emotionalinfluence. Emotional influence has been found to contribute positively to adaptation in personalized recommendation.

Index Terms—Emotion and behavior profiling, behavioral tracking, adaptation in mid- to long-term interaction, consumer habits,personalization, recommender system, web log mining, knowledge discovery, ontology generation, semantic web.

✦

1 INTRODUCTION

It is generally accepted that human emotions are amajor motivational factor of human behaviors, e.g.[1]. In a narrower context, the relationships betweenconsumer emotions and their buying behaviors alsoseem well documented, e.g. [2], [3]. In addition, withmore and more people connected to the internet,today’s technology savvy consumers are likely to usethe web to find information pertinent to products andservices before they commit to a purchase. Emotionshave been found to influence a person’s web surfingbehaviors e.g. [4]. Discovering and modeling con-sumers’ emotions and surfing habits and behaviorsare important for many web applications such aspersonalized web search and recommendation, e.g.for business applications [5], [6].

In [7], the authors use self-report and behavioraltracking to study how heartbeat communication canimprove interpersonal intimacy. Here, we use self-report to incorporate emotions into a personalized

• A.C.M. Fong was with the School of Computer Engineering, NanayngTechnological University, Singapore 639798. He is now with the Schoolof Computing & Math Sciences, Auckland University of Technology,Auckland, 1142, New Zealand.E-mail: [email protected]

• B. Zhou is with IBM Research - China, Semantic Integration, Beijing100193, China.

• S.C. Hui is with the School of Computer Engineering, NanayngTechnological University, Singapore 639798.

• J. Tang is with the Knowledge Engineering Lab, Tsinghua University,Beijing 100084, China.

• G.Y. Hong is with the Department of Computing, Unitec New Zealand.

consumer profile and web access patterns to modelthe consumer’s mid- to long-term web surfing be-haviors. Specifically, users are asked to record anychange in their emotional state at the end of eachweb access request. The information is used to guagethe emotional influence of the accessed resources onthe user. To capture consumers’ access patterns, onepromising approach is web usage mining [8] whichdiscovers interesting and frequent user access pat-terns from web usage logs. Many web usage miningtechniques [9], [10], [11] have been developed formining statistical information and user access patternsin terms of association and sequence of requestedresources.

With on-going development of the Semantic Web[12], some recent research has focused on mining webusage data for the Semantic Web. Known as semanticweb usage mining [13], the idea is to associate eachrequested web page with one or more ontologicalentities to better understand the pattern of web navi-gation. The discovered knowledge can potentially beused for semantic web applications, such as person-alized web content recommendation [14], [15], [16].

In this paper, we propose a semantic web usagemining approach for automatic generation of periodicpattern-based web usage ontology for the SemanticWeb. Most web usage mining techniques [10], [11],[15] focus mainly on mining common access patterns,which have occurred frequently within the entire du-ration of all access sessions. Our proposed approachmines periodic access patterns, which occur frequentlyin a particular period, e.g. every morning, directly

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from web usage logs that have been semanticallyenriched with information on topics and emotionalinfluence. Such periodic access patterns are very use-ful for mid- to long-term behavioral tracking. Withperiodic web access patterns of a user, we can deduceand prepare the resources that the user is most proba-bly interested in during a specific time period withoutknowing the user’s current web access information forweb personalization services.

Over time, the ontology will accumulate personalinformation on web access behaviors and habits, aswell as emotional influence of the accessed resources.So, those who have concerns about privacy maychoose not to use our system.

Many ontology generation techniques [17], [18],[19], [20], [21], [22], [23], [24], [25], [26] have beeninvestigated. These techniques focus mainly on gen-erating concept hierarchy for building ontology fromfree text documents or relational databases. Our pro-posed approach aims at extracting semantics fromsemantically enriched web usage logs automaticallyand generates personalized web usage ontology forthe Semantic Web.

The rest of this paper is organized as follows. In Sec-tion 2, we review the related work. Section 3 presentsour approach for automatic generation of periodicpattern-based web usage ontology using semanticallyenriched web logs. Performance evaluation based ona web personalization application is given in Section4. Finally, Section 5 concludes the paper.

2 RELATED WORK

2.1 Semantic Web Usage Mining

Since traditional web usage logs only record requestedURLs, but not the semantics of contents requested bythe users, it is difficult to use such logs for trackingthe users’ actual web access behaviors, emotions andinterests. In response, a number of semantic webusage mining techniques [13] have been proposed.Dai et al. [27] used domain ontology to enhance webusage mining for traditional web usage logs, but themapping from requested URLs to ontological enti-ties lacks reliability, especially for dynamic websites.Oberle et al. [28] proposed another framework forsemantic enrichment of web usage logs by mappingeach requested URL to one or more concepts from theontology of the underlying website. It clusters groupsof sessions with specific user interests from the se-mantically enhanced web logs, and applies associationrule mining to the semantically enhanced web logs.

Eirinaki et al. [29] obtained concept-logs (C-logs) byenriching each web server log record with keywordsfrom a taxonomy representing the semantics of therequested URLs. C-logs were analyzed in [30] withMINE RULE (a query language for association rulemining) for discovering access patterns. Also, Frater-nali et al. [31] created conceptual logs by combining

the server log data with the conceptual schema of theweb application.

Most semantic web usage mining techniques focusonly on discovering simple usage statistics and com-mon access patterns of user groups. Further, the dis-covered knowledge should be represented as ontologyto enable Semantic Web applications.

2.2 Ontology Generation

An ontology typically consists of a finite list of termsand the relationships between these terms. The termsdenote important concepts (classes of objects) of thedomain, while the relationships include hierarchiesof classes. Ontologies may also include other infor-mation, such as properties, value restrictions, dis-jointedness statements and specifications of logicalrelationships between objects. Ontology languages aresemantic markup languages for defining ontologies.We use OWL (Web Ontology Language) [32], whichwas proposed as W3C Recommendation, for ontologyspecification. OWL facilities greater machine inter-pretability of web content than XML [33], RDF andRDF Schema [34] by providing additional vocabular-ies along with a formal semantics.

Ontologies can be constructed manually using anontology editor, e.g. Protege [35] and OntoEdit [36],but the process can be tedious. The integration ofknowledge acquisition with machine learning facili-tates research toward automating the ontology gen-eration process. Many approaches have been inves-tigated for generating ontology [17]. These includeNatural Language Processing (NLP) techniques [18],association rule mining [19], hierarchical clustering[20], translation from relational databases [26] andFormal Concept Analysis (FCA) [21], [22], [23], [24],[25]. However, these techniques focus mainly on con-structing concept hierarchies from text documents orrelational databases.

2.3 Fuzzy Association Rule Mining

Association rules e.g. [37], [38] can help discoverrelationships between web resources accessed by auser that would otherwise be missed, especially if theresources are disjoint. They can also be used to findgroups of people with similar interests. A major prob-lem of traditional association rule mining techniquesis that each item in a transaction is considered onlyeither to exist or not. Thus, the user’s preference andinterest on each transaction item cannot be preciselyrepresented. Since the concepts of preference andinterest are fuzzy data, fuzzy logic [39] can be applied.For example, Wong et al. [40] combine fuzzy asso-ciation rule mining and case-based reasoning (CBR)[41] to improve the quality of web access patternprediction. The fuzzy rule set was found to performbetter in prediction accuracy and rule coverage thantraditional rule set.



Personal Web Usage

Lattice

Semantic Web Usage

Logs

Constructing Personal Web Usage Lattice

Constructing Global Web

Usage Lattice

GlobalWeb Usage

Lattice

GlobalWeb Usage

Ontology

PersonalWeb Usage

Ontology

Generating Personal Web

Usage Ontology

Generating Global Web

Usage Ontology

Fig. 1. Automatic generation of Web Usage Ontology.

2.4 Periodic Pattern Mining

Discovering periodic patterns from time seriesdatabases is an important data mining task for manyapplications, such as behavioral tracking. Accordingto the type of patterns, periodic patterns can be di-vided into periodic association rules and periodic sequen-tial patterns. Periodic association rules are rules thatassociate with a set of events that occur periodically;such association rules hold only during certain timeintervals but not others. Periodic sequential patternmining can be viewed as an extension of sequentialpattern mining [42] by taking into account the peri-odic characteristics in the time series data.

2.5 Discussion

The combination of fuzzy sets with FCA is an interest-ing approach toward automatic generation of ontolog-ical domain knowledge. However, the focus has beenon text documents, e.g. [22]. Here, we incorporatefuzzy logic [43] into FCA directly from web usagelogs, which have been semantically enriched with in-formation on resource topics and emotional influence,to construct a personalized web usage lattice of webaccess activities. We then generate a periodic pattern-based usage ontology from the web usage lattice.

3 WEB USAGE ONTOLOGY GENERATION

We propose a process for automatic generation ofPersonal Web Usage Ontologies (PWUO) of indi-vidual users from semantically enriched web usagelogs. Figure 1 shows the proposed approach, whichconsists of four major steps: Personal Web Usage Lat-tice (PWUL) Construction, Global Web Usage Lattice(GWUL) Construction, Global Web Usage Ontology(GWUO) Generation and finally PWUO Generation.

Step 1. Using the semantic web usage logs as inputs,it first identifies a set of periodic attributes (i.e., tempo-ral concepts such as morning) and a set of resourceattributes (i.e., useful domain ontological concepts)enhanced with user-reported emotional influence torepresent periodic pattern-based web access activities.With the user’s web access activities, it constructs aPWUL from the access sessions of the user.

TABLE 1Semantically enriched web usage log.

UserID Timestamp URL ΔE TopicsUser1 21/May/2010 08:20:01 URL1 3 #Topic2, #Topic3,...User1 21/May/2010 08:22:32 URL2 3 #Topic7, #Topic5,...User2 21/May/2010 08:22:50 URL7 4 #Topic1, #Topic8,...User1 21/May/2010 08:27:30 URL3 2 #Topic3, #Topic1,...User3 21/May/2010 08:33:10 URL3 1 #Topic6, #Topic2,...User1 21/May/2010 09:10:02 URL5 4 #Topic7, #Topic3,...User3 21/May/2010 09:17:32 URL6 3 #Topic2, #Topic1,...User2 21/May/2010 09:26:17 URL4 5 #Topic3, #Topic7,...

Step 2. It constructs a GWUL to represent all pe-riodic pattern-based global web access activities andhierarchical relationships between these activities. AsGWUL contains a large number of global web accessactivities to cover all possible user web access activ-ities, it can be very large. Generally, PWUL is just asmall sub-lattice of GWUL.

Step 3. It generates GWUO from GWUL by map-ping global web access activities and their hierarchicalrelationships into activity classes and their properties.

Step 4. It generates PWUO for a user by mappingthe personal web access activities in PWUL into activ-ity instances of the corresponding activity classes inGWUO. Written in OWL, PWUO is the knowledgebase that can subsequently provide personalizationfacility to the users.

The following subsections will discuss the entirePWUO generation process in detail.

3.1 Semantic Web Usage Logs

Web usage logs can be semantically enriched byassociating each requested URL with one or moreontological entities such as concepts, attributes andrelations to better describe the patterns of web naviga-tion. We adapt the framework in [28] and assume thateach requested URL can be annotated with pertinentsemantic information (topics, concepts, etc.) manuallyor semi-automatically. We annotate each web serverlog entry with one or more predefined topics (e.g.News, Sports, etc.) and an emotional influence scoreΔE reported by the user. Specifically, at the end ofeach web access activity (just before the next request)the user is asked to score how the web contenthas influenced their emotions on a scale of 1 to 5(highly negative, negative, neutral, positive or highlypositive). Set at 3 (neutral) by default, if no newΔE is recorded, it is assumed to be the same as themost recent score. Table 1 illustrates an example of asemantically enriched web log.

Each entry in Table 1 can be interpreted as “User Naccessed specific resources at a specific time and wasemotionally influenced by a specific amount”. If theuser has accessed specific resources periodically, wesay that the user has a web access activity (i.e. periodicweb access pattern). We therefore use a set of periodic



attributes Mp and resource attributes Mr to representweb access activities. We also define eight real-lifetemporal concepts: Early Morning, Late Morning, Noon,Early Afternoon, Late Afternoon, Evening, Night and LateNight, as periodic attributes. More generally, we couldalso use days of week (e.g., Monday, Tuesday, etc.)or other real-life temporal concepts (e.g. weekdays,weekend, etc.) as periodic attributes.

3.2 Personal Web Usage Lattice Construction

The PWUL Construction step is presented as follows.In Subsection 3.2.1, we describe preprocessing per-formed on the web log data to extract the most rel-evant information for further processing. Next, sinceperiodicity information in crucial in our disccusion,we need to define the concept of a period in thepresent context. In Subsection 3.2.2, we define a fuzzyperiodic Web Usage Context K based on the prepro-cessed access sessions. From the definition of K , wecan further establish the following: the set of attributescommon to user access sessions, the set of user accesssessions having the same attributes, fuzzy supportof a set of attributes, and the notion of web accessactivity. In Subsection 3.2.3, we identify the activityrelationships so as to generate PWUL.

3.2.1 PreliminariesWe perform preprocessing tasks on the semanticallyenriched web logs similar to those for traditional webserver logs as discussed in [44], i.e. data cleaning, useridentification and session identification. The purposesare to discard unsuccessful requests, unnecessary data(e.g. scripts), and to identify all personal access ses-sions for each individual user.

A user’s web access session S =〈(URL1, t1), (URL2, t2), · · · , (URLn, tn)〉 is a sequenceof requested URLi with timestamp ti (1 ≤ i ≤ n).We do not insist that URLi �= URLj for i �= j in S,so repeat of requested URLs is allowed, because thesame URLs may have different contents and differentdegrees of emotional influence at different requesttimes. In general, the time spent by a user for a URLmay indicate the level of interest that the user has inthe content of that URL. The duration di of URLi canbe estimated simply as di = (ti+1 − ti). For the lastrequested URL URLn in each user access session thatdoes not have “tn+1”, we use the average durationof the current session for estimating its duration, i.e.,dn = (d1 + d2 + · · ·+ dn−1)/(n− 1) = (tn− t1)/(n− 1).To compute dn, we need n > 1, i.e., we only retainuser access sessions that contain more than onerequested URL. Furthermore, we can evaluate thestart time and end time of S as t1 and (tn + dn)respectively. For subsequent processing, we ignorethe date information in the session start time andsession end time, and convert them into a valuewithin the interval [0, 24]. For example, the session

start time “21/May/2010 08:20:01” is converted to8.33. Next, we define the period of the user accesssession S as follows.

Definition 1. A period of a user access session S isdefined as a continuous time interval with a sessionstart time ts(S) ∈ [0, 24] and a session end time te(S) ∈[0, 24], denoted as

p(S) ={

[ts(S), te(S)], if ts(S) ≤ te(S)[0, te(S)] ∪ [ts(S), 24], otherwise .

Suppose that each URLi in the user access sessionS is associated with a set of resource attributes Mri ⊆Mr for representing the semantics of the content ofURLi. Thus, each user access session can be treatedas a sequence of sets of resource attributes Mri insteadof a sequence of individual URLi (1 ≤ i ≤ n),and is denoted as S = 〈(Mr1, t1, d1), (Mr2, t2, d2), · · · ,(Mrn, tn, dn)〉. The total duration for each resourceattribute mk ∈ Mr, which can be used for estimatingthe level of user interest in that resource during theuser access session S, can be computed as

d(S, mk) =∑n

i=1 αkidi, where

αki ={

1, if mk ∈Mri

0, otherwise for 1 ≤ i ≤ n.

3.2.2 Web Usage ContextWe now construct the Web Usage Context for a userfrom his preprocessed user access sessions.

Definition 2. A fuzzy periodic Web Usage Context isK = (G, Mp, Mr, I), where G is a set of user accesssessions for a user, Mp is a set of periodic attributes, Mr

is a set of resource attributes and I = R(G× (Mp∪Mr))is a fuzzy set on the domain of G × (MP ∪ Mr) torepresent fuzzy relations between user access sessionsg ∈ G and attributes m ∈Mp∪Mr. Each fuzzy relationR(g, m) ∈ I is represented by a membership valueμ(g, m) ∈ [0, 1], where

μ(g, m) ={

μp(g, m), if m ∈ Mp

μr(g, m), if m ∈ Mr.

From this definition, each user access session g ∈ Gcan also be denoted as a fuzzy set on the domain ofMp ∪Mr, i.e., g = {m, μ(g, m) | m ∈ Mp ∪Mr}.

The membership value μp(g, mp) for a periodicattribute mp ∈ Mp in a user access session g ∈ Gcan be computed using the period of g, i.e., p(g).Here, the member function is defined as μp(g, mp) =maxt∈p(g){μp(t, mp)}, where μp(t, mp) is defined inFigure 2, which is modified from [45].

The membership value μr(g, mr) for a resourceattribute mr ∈ Mr in a user access session g ∈ Gcan be computed using the total duration of mr, i.e,d(g, mr). We define the member function as

μr(g, mr) =

⎧⎪⎪⎨⎪⎪⎩

0, if z(g, mr) < 12Z(mr)

2z(g,mr)Z(mr) − 1, if 1

2Z(mr) ≤ z(g, mr)≤ Z(mr)

1, if z(g, mr) > Z(mr)

,



0 15141312111098765432 16 17 18 19 20 21 22 231 24

1.0

Late Night EarlyAfternoon

Evening0.5

EarlyMorning

Morning

Noon LateAfternoon Night

0.0

p (t, mp)

t (hours)

Fig. 2. Member function μp(t, mp).

0 Z(mr)Z(mr)/2

1.0

0.5

0.0

μr (g, mr)

z(g, mr)

Fig. 3. Member function μr(g, mr).

where z(g, mr) = d(g,mr)te(g)−ts(g) er and

Z(mr) =

∑gk∈G

d(gk,mr)∑gk∈G

(te(gk)−ts(gk)).

Z(mr) is the proportion of the total duration ofaccessing the resource mr in all web access sessionsof the user, which indicates the user’s global interestof the resource mr. z(g, mr) is the proportion of theduration of accessing the resource mr within the useraccess session g, weighted by an emotional influencefactor er derived from the corresponding ΔE usingone of the following rules:Rule 1, denoted −E. This is the baseline situationwhere er = 1, i.e. ignoring emotional influence.Rule 2, denoted +E1. If ΔE < 3 then er = 0.5,otherwise er = 1, i.e. we only suppress (penalize)resources with negative emotional influence.Rule 3, denoted +E2. We derive er using the formulaer = 0.1ΔE + 0.7, thus assigning a larger value to er

with increasing ΔE, i.e. er = 0.8, 0.9, 1, 1.1, 1.2.z(g, mr) thus indicates the user’s local interest andemotional influence of the resource mr. Figure 3shows the member function μr(g, mr).

We also investigated a fourth rule using the formulaer = 0.25ΔE + 0.25, i.e. er = 0.5, 0.75, 1, 1.25, 1.5. Dueto the increased gradient of er against ΔE, this rulewould be more discriminating than Rule 3. However,we abondoned this rule because it had a tendency ofpushing μr(g, mr) to either extreme i.e. 0 or 1, therebyreducing the effects of fuzzy membership values. Infuture research, we shall investigate the impact ofchanging the relationship of er and ΔE, and moregenerally the definitions of the membership functionsμp(t, mp)and μr(g, mr).

Table 2 shows an example Web Usage Context ofa user, which consists of five user access sessions,three periodic attributes “P1 (Late Afternoon)”, “P2(Evening)” and “P3 (Night)”, and three resource at-tributes “R1 (Sports)”, “R2 (Games)” and “R3 (Chat)”.

TABLE 2A cross table of an example fuzzy periodic Web

Usage Context of a user.

SessionID P1 P2 P3 R1 R2 R3S1 0.6 0.5 0 0.8 0 0.6S2 0 0.4 0.8 0 0 0.9S3 0 0 1.0 0 0.7 0.5S4 0 0.8 0.6 0.8 0.6 0.5S5 0 0 1.0 0 0.9 0

Before we can construct the PWUL, we still needto define the following: the set of attributes commonto user access sessions, the set of user access sessionshaving the same attributes, fuzzy support of a set ofattributes, and web access activity.

Definition 3. Given a Web Usage Context K =(G, Mp, Mr, I), we define the set of attributes commonto user access sessions in A ⊆ G as A∗ = {m ∈Mp ∪Mr | ∀g ∈ A : μ(g, m) > 0}, and the set of useraccess sessions which have all the same attributes inB ⊆ Mp∪Mr as B∗ = {g ∈ G | ∀m ∈ B : μ(g, m) > 0}.Definition 4. Given a Web Usage Context K =(G, Mp, Mr, I), the fuzzy support of a set of attributesB ⊆ Mp ∪Mr and B �= ∅ is defined as

Sup(B) =

∑g∈B∗(μp(g)× μr(g))

|G| ,

where

μp(g) ={

minmp∈(B∩Mp){μp(g, mp)}, if B ∩Mp �= ∅1, otherwise

μr(g) ={

minmr∈(B∩Mr){μr(g, mr)}, if B ∩Mr �= ∅1, otherwise

Definition 5. Given a Web Usage Context K =(G, Mp, Mr, I), if there exists a pair (A, B) with A ⊆ G,B ⊆ Mp ∪Mr (B ∩Mp �= ∅ and B ∩Mr �= ∅), A∗ = Band B∗ = A, then v(B) = {m, μ(B, m) | m ∈ B}, afuzzy set on B, is called a web access activity, whereμ(B, m) = maxg∈B∗{μ(g, m)}. We also define v(B) =vp(B) ∪ vr(B), where vp(B) = {mp, μ(B, mp) | mp ∈B ∩Mp} and vr(B) = {mr, μ(B, mr) | mr ∈ B ∩Mr}represent two fuzzy subsets of v(B) on domains ofB ∩Mp and B ∩Mr respectively. In addition, v(∅) = ∅is defined as a virtual web access activity.

WP = {v(Bi)} is the set of all web access activitiesof a user. |WP | is the total number of web accessactivities. A web access activity represents a periodicweb access behavior of a user, i.e., an implication fromperiodic attributes to resource attributes.

3.2.3 Personal Web Usage LatticeWe now identify the activity relationships in order toconstruct PWUL.

Definition 6. The fuzzy support of a web access activityv(B) is defined as Sup(v(B)) = Sup(B) and the



0

6

1

5

8

4

Legendn -- web access activity

-- direct activity relationship

Conf= -- fuzzy confidenceSup= -- fuzzy support

5

0{ }

Sup=1.0Conf=1.0

1

Sup=0.21Conf=0.62

{P2:0.8,R3:0.9}

7

Sup=0.06Conf=0.60

{P1:0.6, P2:0.5, R1:0.8, R3:0.6}

2

Sup=0.30Conf=0.45

{P3:1.0,R3:0.9}

8{P2:0.8, P3:0.6, R1:0.8,

R2:0.6, R3:0.5}

Sup=0.06Conf=0.30

3{P3:1.0,R2:0.9}

Sup=0.39Conf=0.58

6Sup=0.16Conf=0.24

{P3:1.0,R2:0.7,R3:0.5}

5

Sup=0.13Conf=0.66

{P2:0.8,P3:0.8,R3:0.9}

4Sup=0.14Conf=0.41

{P2:0.8,R1:0.8,R3:0.6}

Fig. 4. Personal Web Usage Lattice.

fuzzy confidence of v(B) is defined as Conf(v(B)) =prob((B ∩ Mr) | (B ∩ Mp)) = Sup(B)

Sup(B∩Mp) , whereprob(· | ·) is a conditional probability. For virtual webaccess activity v(∅), we define Sup(v(∅)) = 1.0 andConf(v(∅)) = 1.0.

Definition 7. For two web access activities of a userv(Bi), v(Bj) ∈ WP , v(Bi) is a sub-activity of v(Bj),denoted as v(Bi) <WP v(Bj), if and only if Bj ⊂ Bi.Equivalently, v(Bj) is a super-activity of v(Bi). <WP

is a partial order on WP , called activity relationship. Inparticular, if v(Bi) <WP v(Bj) and there is no v(Bk) ∈WP (Bk �= Bi and Bk �= Bj) such that v(Bi) <WP

v(Bk) <WP v(Bj), then v(Bi) is a direct sub-activityof v(Bj), and v(Bj) is a direct super-activity of v(Bi).We denote this as v(Bi) ≺WP v(Bj). ≺WP is called adirect activity relationship. Obviously, the virtual webaccess activity v(∅) is the super-activity of all otherweb access activities.

Definition 8. A Personal Web Usage Lattice based ona Web Usage Context K = (G, Mp, Mr, I) of a useris LP = (WP , <WP ), where WP is the set of all webaccess activities, and <WP is a partial order on WP torepresent the hierarchy of web access activities.

Figure 4 shows the PWUL obtained from the datain Table 2. Each node in the lattice represents aweb access activity with membership values of itsattributes, and the corresponding fuzzy support andconfidence values. Each edge represents a direct activ-ity relationship. One virtual node (virtual web accessactivity v(∅)) is at the top of the lattice.

Some efficient approaches [46], [47], [48] have beenproposed in traditional FCA for computing conceptlattice. Among them, the TITANIC algorithm [46]is one of the most efficient lattice construction al-gorithms, especially for weakly correlated data invery large data sets. We have modified the TITANICalgorithm with fuzzy support and fuzzy confidencefor constructing PWUL from the Web Usage Context.

TABLE 3Periodic access patterns.

Activity Periodic Association Access Pattern Sup Conf1 {Evening(0.8)} ⇒ {Chat(0.9)} 0.21 0.622 {Night(1.0)} ⇒ {Chat(0.9)} 0.30 0.453 {Night(1.0)} ⇒ {Games(0.9)} 0.39 0.584 {Evening(0.8)} ⇒ {Sports(0.8), Chat(0.6)} 0.14 0.415 {Evening(0.8), Night(0.8)} ⇒ {Chat(0.9)} 0.13 0.666 {Night(1.0)} ⇒ {Games(0.7), Chat(0.5)} 0.16 0.247 {Late Afternoon(0.6), Evening(0.5)} ⇒ 0.06 0.60

{Sports(0.8), Chat(0.6)}8 {Evening(0.8), Night(0.6)} ⇒ 0.06 0.30

{Sports(0.8), Games(0.6), Chat(0.5)}

From PWUL, inference rules can be extracted todeduce the user’s periodic association access patterns.In particular, fuzzy logic can be applied to inferassociation access patterns of a user.

Definition 9. Given a PWUL LP = (WP , <WP ), eachpersonal web access activity v(B) = vp(B) ∪ vr(B) ∈WP (excluding virtual personal web access activi-ties) can be represented as a periodic association accesspattern, which is in the form of “vp(B) ⇒ vr(B)”.We also define the fuzzy support as Sup(vp(B) ⇒vr(B)) = Sup(v(B)) and the fuzzy confidence asConf(vp(B) ⇒ vr(B)) = Conf(v(B)).

For example, for the activity {P2:0.8, R1:0.8, R3:0.6}in node 4 of Figure 4, a periodic association accesspattern “ {Evening(0.8)} ⇒ {Sports(0.8), Chat(0.6)}”with Sup = 0.14 and Conf = 0.41 can be extracted.It can be interpreted as “The user has interests inresources on Sports and Chat during the period ofEvening”. The associated fuzzy membership valuescan provide additional information on describing pe-riodic association access patterns. The fuzzy supportand confidence indicate the quality of such periodicassociation access patterns. Table 3 shows eight pe-riodic association access patterns extracted from theexample PWUL in Figure 4.

In practice, the number of periodic association ac-cess patterns of a user may be quite complicatedand large because many of the generated web accessactivities will have insignificantly low support values.To ease further processing efforts, pruning of lowquality association is necessary.

Definition 10. Given a minimum support MinSup ∈[0, 1] and a minimum confidence MinConf ∈ [0, 1],we call a periodic association access pattern inter-esting, if its fuzzy support value is not less thanMinSup and its fuzzy confidence value is not lessthan MinConf .

In the example shown in Table 3, if we setMinSup = 0.1 and MinConf = 0.15, then the last twoentries will be discarded, leaving only six interestingperiodic association access patterns.



3.3 Global Web Usage Lattice Construction

In this step, we construct GWUL of all users. We rep-resent a web access activity using the set of selectedperiodic attributes Mp and resource attributes Mr.

Definition 11. Let |Mp| = a and |Mr| = b be the totalnumbers of selected periodic attributes and resourceattributes respectively. A set Bk ⊆ Mp ∪ Mr withi periodic attributes (1 ≤ i ≤ a) and j resourceattributes (1 ≤ j ≤ b) is defined as a global web accessactivity of all users. In addition, B0 = ∅ is defined asa virtual global web access activity.

WG = {Bk} denote the set of all global web accessactivities. |WG| is the total number of global webaccess activities. As such, there should be a total of(ai

) × (bj

)global web access activities with i periodic

attributes and j resource attributes, where(ai

)and(

bj

)denote the number of combinations. Therefore,

|WG| =∑a

i=1

∑bj=1

(ai

)× (bj

)+ 1.

Definition 12. For two global web access activitiesBi, Bj ∈WG (i �= j), Bi is a sub-activity of Bj , denotedas Bi <WG Bj , if and only if Bj ⊂ Bi, since Bi canrepresent more specific periodic pattern-based webaccess patterns than Bj . Equivalently, Bj is a super-activity of Bi. <WG is a partial order on WG, calledglobal activity relationship. In particular, if Bi <WG Bj

and there is no Bk ∈ WG (Bk �= Bi and Bk �= Bj)such that Bi <WG Bk <WG Bj , then Bi is a direct sub-activity of Bj , and Bj is a direct super-activity of Bi.We denote this as Bi ≺WG Bj . ≺WG is called a directactivity relationship. Obviously, the virtual global webaccess activity B0 = ∅ is the super-activity of all otherglobal web access activities.

For each global web access activity with i periodicattributes (1 ≤ i ≤ a) and j resource attributes (1 ≤j ≤ b), it has (a− i)+ (b− j) direct sub-activities. Andthe virtual activity w0 has a × b direct sub-activities.Then, a total of a× b +

∑ai=1

∑bj=1{(

(ai

)× (bj

))× [(a−

i)+(b−j)]} direct sub-activity relationships ≺WG existamong all global web access activities. Using all directsub-activity relationships, we can construct an entirelattice, called Global Web Usage Lattice, of all globalweb access activities of users.

Definition 13. A Global Web Usage Lattice based on thesets of periodic attributes (Mp) and resource attributes(Mr) is LG = (WG, <WG), where WG is the set of allglobal web access activities, and <WG is a partial orderon WG to represent the hierarchical relationships of allglobal web access activities.

Figure 5 shows a GWUL that contains three periodicattributes “P1 (Late Afternoon)”, “P2 (Evening)” and“P3 (Night)”, and three resource attributes “R1 (Sportsconcept)”, “R2 (Games concept)” and “R3 (Chat con-cept)”. In this example, there are 50 nodes repre-senting global web access activities and 135 edges

{P1, R2} {P2, R1} {P3, R2}{P3, R1} {P3, R3}{P1, R1} {P2, R3}{P2, R2}{P1, R3}

{P1,R1, R3}

{P2,R1, R2}

{P1,R1, R2}

{P2R2, R3}

{P2,R1, R3}

{P1,R2, R3}

{P1, P2,R1, R3}

{P1, P3,R1, R2}

{P1, P2,R1, R2}

{P1, P3,R2, R3}

{P1, P3,R1, R3}

{P1, P2,R2, R3}

��

��

��

{P1, P2, P3,R1, R2}

{P1, P2, P3R1, R2, R3}

{P1, P2, P3,R1, R3}

{P1, P2, P3,R2, R3}

{P1, P2,R1, R2, R3}

{P1, P3,R1, R2, R3}

{P2, P3,R1, R2, R3}

��

��

{φ}B0

Fig. 5. Global Web Usage Lattice.

representing direct sub-activity relationships.

3.4 Global Web Usage Ontology Generation

Typically, ontology consists of a taxonomy with a setof inference rules. The taxonomy can be expressed asa set of domain concepts (i.e., classes of objects) andthe relationships among them (i.e., class hierarchy).Based on the formal definition of ontology [49], wedefine GWUO as follows.

Definition 14. A Global Web Usage Ontology is OG =(C, <C , Q), where• C is a set of activity classes (or concepts).• <C is a partial order on C, called activity class

hierarchy or taxonomy. If ci <C cj , for ci, cj ∈ C(i �= j), then ci is a sub-activity class of cj , and cj

is a super-activity class of ci. If there is no ck ∈ C(k �= i and k �= j) with ci <C ck <C cj , thenci is a direct sub-activity class of cj , and cj is adirect super-activity class of ci. This is denotedas ci ≺C cj .

• Q is a set of properties (or relations) which con-sists of attribute properties QA = {qi} to repre-sent periodic and resource attributes of activityclasses, one taxonomy property qT to representthe direct sub-activity relationship between twoactivity instances, and two quality properties,qsup and qconf , to represent the values of supportand confidence of each activity instance.

GWUO can be generated through class and hierarchymapping, and property mapping. Class and hierarchymapping initializes a set of activity classes C andbuilds the activity class hierarchy <C according toGWUL. Property mapping generates a set of proper-ties Q, and assigns them to the corresponding activityclasses by setting the domains of the properties.

The class and hierarchy mapping is performed inthe following two steps:• Class mapping. All global web access activities

Bi ∈ WG (0 ≤ i ≤ |WG|) in the GWUL aremapped to activity classes ci ∈ C in the GWUO.The name (class ID) of each activity class ci ∈ C(i > 0) is given based on the attributes involved



in its corresponding global web access activitywith “Activity ” as the prefix. For example, theactivity class based on the global web accessactivity {P2, R3} in Figure 5 is named as “Activ-ity Evening Chat”. For the activity class c0 basedon the virtual activity B0 ∈ WG, we name it as“WebAccessActivity”.

• Hierarchy mapping. The activity hierarchy <WG

in the GWUL is mapped to the activity classhierarchy <C in the GWUO. If Bi <WG Bj , forBi, Bj ∈ WG (i �= j), then there exists ci <C cj ,for ci, cj ∈ C (i �= j). As such, the activity classc0 is the root class in the activity class hierarchy.All other activity classes are its (direct or indirect)sub-activity classes and inherit its properties.

The activity class based on the global web accessactivity {P2, R3} in Figure 5, is defined as<owl:Class rdf:ID="Activity_Evening_Chat"><rdfs:subClassOf rdf:resource="#WebAccessActivity"/>

</owl:Class>

whereas the activity class based on the global webaccess activity {P2, R1, R3} is defined as<owl:Class rdf:ID="Activity_Evening_Sports_Chat"><rdfs:subClassOf rdf:resource="#Activity_Evening_Sports"/><rdfs:subClassOf rdf:resource="#Activity_Evening_Chat"/>

</owl:Class>

Note that only direct super-activity classes are de-clared in the “partial” part in the definition of each ac-tivity class, as all indirect super-activity class relation-ships can be derived from direct super-activity classrelationships. For example, the activity class “WebAc-cessActivity” is not included in the “partial” part ofthe activity class “Activity Evening Sports Chat”.

The property mapping comprises attribute propertymapping, taxonomy property mapping and quality prop-erty mapping:Attribute Property Mapping. All periodic and resourceattributes mi ∈ Mp ∪ Mr (0 < i ≤ |Mp ∪ Mr|) aremapped to attribute properties qi(Xi, [0, 1]) ∈ QA,where Xi = {ck | ck ∈ C, and mi ∈ Bk, Bk ∈ WG

is the corresponding global web access activity ofck.}. Xi is the domain of qi, which is defined asthe set of activity classes whose corresponding globalweb access activities involve the attributes mi. [0, 1]indicates the range of qi, which is a fuzzy membershipvalue in [0, 1]. The name of each attribute propertyis given according to its corresponding attribute with“during” as the prefix of periodic attributes or “ac-cess” as the prefix of resource attributes. For example,the property based on the periodic attribute P2 isnamed as “duringEvening”, and the property basedon the resource attribute R3 is named as “accessChat”.The attribute property “duringEvening” is defined as<owl:DatatypeProperty rdf:ID="duringEvening"><rdfs:domain><owl:Class><owl:unionOf rdf:parseType="Collection"><owl:Class rdf:about="#Activity_Evening_Sports"/><owl:Class rdf:about="#Activity_Evening_Games"/><owl:Class rdf:about="#Activity_Evening_Chat"/>

</owl:unionOf></owl:Class>

</rdfs:domain><rdfs:range rdf:resource="&xsd;float"/>

</owl:DatatypeProperty>

whereas the attribute property “accessChat” is de-fined as<owl:DatatypeProperty rdf:ID="accessChat"><rdfs:domain><owl:Class><owl:unionOf rdf:parseType="Collection"><owl:Class rdf:about="#Activity_LateAfternoon_Chat"/><owl:Class rdf:about="#Activity_Evening_Chat"/><owl:Class rdf:about="#Activity_Night_Chat"/>

</owl:unionOf></owl:Class>

</rdfs:domain><rdfs:range rdf:resource="&xsd;float"/>

</owl:DatatypeProperty>

Note that only activity classes ck ∈ Xi with noother super-activity class cj ∈ Xi are declared inthe “domain” part in the definition of each attributeproperty, as all sub-activity classes of ck can inherit thecorresponding attribute property. For example, as theactivity class “Activity Evening Sports Chat” is thesubclass of “Activity Evening Sports” and “Activ-ity Evening Chat”, it is not included in the “domain”part of the attribute property “duringEvening”.Taxonomy Property Mapping. One taxonomy propertyqT = hasDirectSubActivity(C, C) is created to repre-sent the direct sub-activity relationship between twoactivity instances. The domain and range of qT aredefined as C, i.e., the set of all activity classes. Thetaxonomy property hasDirectSubActivity(C, C) is de-fined as<owl:ObjectProperty rdf:ID="hasDirectSubActivity"><rdfs:domain rdf:resource="#WebAccessActivity"/><rdfs:range rdf:resource="#WebAccessActivity"/>

</owl:ObjectProperty>

Only the activity class “WebAccessActivity” is setwith domain and range; all other activity classes areits direct or indirect sub-activity classes and inheritthe hasDirectSubActivity(C, C) property.Quality Property Mapping. Two quality proper-ties qsup = hasSupport(C, [0, 1]) and qconf =hasConfidence(C, [0, 1]) are created to represent val-ues of support and confidence of activity instances.The quality property “hasSupport” is defined as<owl:ObjectProperty rdf:ID="hasSupport"><rdfs:domain rdf:resource="#WebAccessActivity"/><rdfs:range rdf:resource="&xsd;float"/>


whereas the quality property “hasConfidence” is de-fined as<owl:ObjectProperty rdf:ID="hasConfidence"><rdfs:domain rdf:resource="#WebAccessActivity"/><rdfs:range rdf:resource="&xsd;float"/>


GWUO OG = (C, <C , Q) can be generated af-ter the automatic class and hierarchy mapping, andproperty mapping processes. Figure 6 shows theOWL representation of the activity class “Activ-ity Evening Chat” in the GWUO which is mapped



A Part of the GlobalWeb Usage Lattice

<owl:Class rdf:ID="Activity_Evening_Chat"> <rdfs:subClassOf> <owl:Class rdf:about="#WebAccessActivity"/> </rdfs:subClassOf></owl:Class>

{P2, R3}

{P2,R1, R3}

<owl:DatatypeProperty rdf:ID="#duringEvening"> <rdfs:domain> <owl:Class> <owl:unionOf rdf:parseType="Collection"> <owl:Class rdf:about="#Activity_Evening_Sports"/> <owl:Class rdf:about="#Activity_Evening_Games"/> <owl:Class rdf:about="#Activity_Evening_Chat"/> </owl:unionOf> </owl:Class> </rdfs:domain> <rdfs:range rdf:resource="&xsd;float"/></owl:DatatypeProperty>

<owl:DatatypeProperty rdf:ID="accessSports"> <rdfs:domain> <owl:Class> <owl:unionOf rdf:parseType="Collection"> <owl:Class rdf:about="#Activity_LateAfternoon_Sports"/> <owl:Class rdf:about="#Activity_Evening_Sports"/> <owl:Class rdf:about="#Activity_Night_Sports"/> </owl:unionOf> </owl:Class> </rdfs:domain> <rdfs:range rdf:resource="&xsd;float"/></owl:DatatypeProperty>

{ }

Fig. 6. Representation of an activity class in OWL.

from the global web access activity {P2, R3} in theGWUL. For the GWUL in Figure 5, a GWUO can begenerated automatically with 50 activity classes.

OWL allows extension of imported definitionswithout the need to modify the original ontology andsupports incremental ontology construction, makingit easy to incrementally update the activity class hi-erarchy. When new periodic attributes or resourceattributes are introduced, we just need to create newactivity classes with new attributes and insert newsubclass relationships between the new and existingactivity classes to extend the activity class hierarchy.

3.5 Personal Web Usage Ontology Generation

We define PWUO as follows.

Definition 15. A Personal Web Usage Ontology of a useris OP = (OG, IP , < IP ), where• OG = (C, <C , Q) is the GWUO.• IP is a set of activity instances of the correspond-

ing activity classes in C.• <IP is a partial order on IP , called activity in-

stance hierarchy or taxonomy. If ii <IP ij , for ii,ij ∈ IP (i �= j), then ii is a sub-activity instanceof ij , and ij is a super-activity instance of ii. Ifthere is no ik ∈ IP (k �= i and k �= j) withii <IP ik <IP ij , then ii is a direct sub-activityinstance of ij , and ij is a direct super-activityinstance of ii. This is denoted as ii ≺IP ij .

To generate PWUO, we combine the PWUL of auser with the GWUO using instance mapping. Instancemapping generates a set of activity instances IP fromthe corresponding activity classes in the GWUO, andan activity instance hierarchy <IP .

A Part of the PersonalWeb Usage Lattice

<Activity_Evening_Chat rdf:ID="Activity_Evening_Chat_User1"> <hasDirectSubActivity rdf:resource="#Activity_Evening_Sports_Chat_User1" /> <duringEvening rdf:datatype="&xsd;float">0.8</duringEvening> <accessChat rdf:datatype="&xsd;float">0.9</accessChat> <hasSupport rdf:datatype="&xsd;#float">0.21</hasSupport> <hasConfidence rdf:datatype="&xsd;float">0.62</hasConfidence></Activity_Evening_Chat>

{P2:0.8,R3:0.9}

{P2:0.8,R1:0.8,R3:0.6}

Sup=0.21Conf=0.62

Sup=0.14Conf=0.41

{ }

Fig. 7. Representation of an activity instance in OWL.

Instance mapping is performed as follows. Allpersonal web access activities v(Bi) ∈ WP (0 ≤i ≤ |WP |and Bi ∈ WG) in PWUL are mappedto the activity instance ii of activity class ci ∈ Cbased on the global web access activity Bi ∈ WG.The name (instance ID) of each activity instance isgiven based on its corresponding activity class withits user ID as the suffix. For example, the activityinstance based on the personal web access activity{P2:0.8, R3:0.9} of some “User1” in Figure 4 is “Ac-tivity Evening Chat User1”. The activity instance i0based on the virtual activity v(∅) ∈ WP is “WebAc-cessActivity User1”. The fuzzy membership valuesof attributes of a personal web access activity aremapped to the values of attribute properties of itscorresponding activity instance. The values of twoquality properties psup and pconf of each activityinstance are set to the values of fuzzy support andconfidence of the corresponding personal web accessactivity.

The activity hierarchy <WP in PWUL is mapped toactivity instance hierarchy in PWUO. If v(Bi) <WP

v(Bj), for v(Bi), v(Bj) ∈ WP (i �= j), then ii <IP ij ,for ii, ij ∈ IP (i �= j). Thus, activity instance i0 isthe root instance in the activity instance hierarchy.All other activity instances are its (direct or indirect)sub-activity instances. If personal web access activitiesv(Bi) ≺WP v(Bj), i.e., v(Bi) is a direct sub-activity ofv(Bj), then a taxonomy property hasDirectSubActiv-ity = ii is set for the activity instance ij . For example,the activity instance based on the personal web accessactivity {P2:0.8, R3:0.9} of User1 with Sup = 0.21 andConf = 0.62 in Figure 4 is created as<Activity_Evening_Chat rdf:ID="Activity_Evening_Chat_User1"><hasDirectSubActivity

rdf:resource="#Activity_Evening_Sports_Chat_User1"/><duringEvening rdf:datatype="&xsd;float">0.8</duringEvening><accessChat rdf:datatype="&xsd;float">0.9</accessChat><hasSupport rdf:datatype="&xsd;float">0.21</hasSupport><hasConfidence rdf:datatype="&xsd;float">0.62</hasConfidence>

</Activity_Evening_Chat>

Figure 7 shows the OWL representation of thepersonal web access activity {P2:0.8, R3:0.9} of User1with Sup = 0.21 and Conf = 0.62 in the activityinstance “Activity Evening Chat User1” of PWUO.The OWL definition of the corresponding activityclass “Activity Evening Chat” is shown in Figure 6.

As expected, the number of activity instances inPWUO of a user is much smaller than the number of



activity classes in GWUO. In our example, there are9 activity instances from a total of 50 activity classes.

4 PERFORMANCE EVALUATION

We evaluated the performance of the proposed webusage ontology generation approach based on theeffectiveness of its generated periodic access patternsfor supporting web personalization. It was imple-mented in C++ on a PC running Microsoft WindowsXP Professional. As in [50], the raw web sever logdata were obtained from a web forum at NanyangTechnological University, Singapore. The web forumhad 7 main topics (such as Past time, Sports, Com-puter, etc.) and 57 sub-topics (such as MovieTV in Pasttime, soccer in Sports, etc.). The web usage logs weresemantically enriched as described in Section 3.1.

Access data of the top 50 users (measured in termsof the number of access activities) were used in theexperiments. We used web access sessions of the usersover a twenty-day period as the training dataset, andweb access sessions over another ten days as thetesting dataset. Table 4 shows the session informationof the training and testing datasets of the top tenusers.

TABLE 4Experimental datasets for the top ten users.

User Training Dataset Testing Dataset Number of WebID (Number of Sessions) (Number of Sessions) Access Activitiesu13 94 38 912u16 90 40 876u19 81 37 632u27 94 32 1107u34 86 34 532u36 112 47 1196u48 94 37 1258u55 82 36 479u77 96 42 689u82 144 49 1005

Three experiments (two objective and one subjec-tive) were conducted. In the experiments, we con-structed Personal Web Usage Lattices for the usersfrom the training dataset. We employed two evalu-ation measures in the experiments: applicability andsatisfaction. These measures were derived from thewell-known precision and recall measures adapted forthe present setting.

Definition 16. Suppose we generate an ordered setof personalized resources “PRo(pc, LP )” for a givenperiod condition pc based on a Personal Web UsageLattice LP of a user. If PRo(pc, LP ) �= ∅, we callit applicable. Let S = {m, μ(S, m) | m ∈ Mp ∪ Mr}be a user access session in the period p(S) of thatuser. If pc ∩ p(S) �= ∅, we call S a period-supportedsession of the generated personalized resources. If∃mr ∈ PRo(pc, LP ) with μr(S, mr) > 0, we call Sa resource-supported session of the generated personal-ized resources.

Definition 17. Let PRall = {PR1, PR2, · · · , PRn} bea collection of sets of personalized resources for theoverall web personalization, and PRa be the subset ofPRall comprising all applicable sets of personalizedresources. The applicability of the overall web person-alization is defined as

applicability =|PRa||PRall| .

As PWUL only stores typical web access activi-ties (supported by some user access sessions in thetraining dataset of the web usage logs), if the period-supported activities cannot be found, the generatedset of personalized resources will be empty. Therefore,applicability measures how often applicable sets ofpersonalized resources will be generated.

Definition 18. Let ST be the set of all user accesssessions for testing. SSp(PRi) is the subset of ST

comprising all period-supported sessions of the setof personalized resources PRi, and SSr(PRi) is thesubset of SSp(PRi) comprising all resource-supportedsessions in SSp(PRi). The satisfaction of the set ofpersonalized resources PRi is defined as

satisfaction(PRi) =

{0, if SSp(PRi) = ∅|SSr(PRi)||SSp(PRi)| , otherwise .

The satisfaction for the overall web personalization isdefined as

satisfaction =

∑PRi∈PRa

satisfaction(PRi)|PRa| .

Therefore, satisfaction measures how likely a useris interested in one of the personalized resources inthe period-supported sessions.

4.1 Effectiveness of Periodic Access Patterns

In this experiment, we generated personalized re-sources for a set of predefined period conditionsand measured the performance based on applicabilityand satisfaction with respect to different durations ofperiod conditions d (from 0.5 to 4 hours) and differentnumbers of personalized resources NPR (from 1 to6), while keeping er fixed at 1. The purposes wereto guage the effectiveness of the approach and tosee how the parameters d and NPR would affect theresults in the absence of emotional influence. Supposethat [ts, te] = [0, 24] is the whole period for periodicweb personalization and d ∈ (0, 24] is the durationof each period condition. Then, we have a total ofn = [ te−ts−d

d +1] period conditions, where each periodcondition pi = [(ts + (i − 1) × d), (ts + i × d)] for1 ≤ i ≤ n. For example, if d = 4, then we have 6period conditions, which are p1 = [0, 4], p2 = [4, 8],p3 = [8, 12], p4 = [12, 16], p5 = [16, 20] and p6 =[20, 24]. In this experiment, we set d to five differentvalues, i.e., d = 0.5, 1.0, 2.0, 3.0, and 4.0.



Figure 8(a) shows the applicability results for thetop 5 users with different d values. Figure 8(b) showsthe results averaged over the top 10, 20 and 50 users.Overall, the results were good. We achieved at least85% for the users with d = 0.5, over 90% for all userswith d ≥ 2 and 100% when d = 4.

0%10%20%30%40%50%60%70%80%90%

100%

Ap

plic

abili

ty

0.5 1 2 3 4

d (hours)

u13 u27 u36 u48 u82

(a) Top 5

0%10%20%30%40%50%60%70%80%90%

100%

Ap

plic

abili

ty

0.5 1 2 3 4

d (hours)

top10 top20 top50

(b) Averages

Fig. 8. Applicability results

The satisfaction results showed little sensitivity tochanges in d. Figure 9 shows the satisfaction resultsfor d = 2.0, which is representative of all results forthe range of d values considered. So, we conclude thatsatisfaction stabilizes for d ≥ 0.5. As shown in Figure9, satisfaction increases with increasing NPR. How-ever, the increase begins to taper off when NPR > 4.Although satisfaction could be improved with a largerNPR, it is not advisable as too many personalizedresources will adversely affect the preparation processof useful resources for users. Thus, we recommendNPR = 4 for this web forum. With NPR = 4, weobtained satisfaction of about 90% on average. Theproposed approach therefore achieved effective webpersonalization for the predefined period conditions.

4.2 Effects of Emotional Influence

We evaluated the effects of emotional influence byadjusting the value of er based on the user inputΔE score using the three mapping rules describedin 3.2.2, i.e. −E (no emotional influence), +E1 (sup-presses negative emotional influence) and +E2 (dif-ferent weights for different degrees of emotional in-fluence). The applicability results for d = 0.5 to 3.0are summarized in Figure 10. All applicability valuesapproached unity when d = 4.0 (therefore not shown).The results in Figure 10 show that both +E1 and+E2 gave better applicability results than −E and theeffects became increasingly noticeable with increasingd. Further, +E2 consistently outperformed +E1. Forexample, the applicability values for the Top 50 userswere −E = 85.8%, +E1 = 86.5, +E2 = 86.9%when d = 0.5, and were −E = 95.5%, +E1 = 97.4,+E2 = 98.7% when d = 3. Thus, we conclude thatemotional influence had a positive effect on appli-cability especially with +E2 (higher discriminating

d = 2.0

0%

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40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6N PR

Sat

isfa

ctio

n

top10top20top50

Fig. 9. Satisfaction results

power and finer granularity than +E1) and for longerperiod durations.

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d = 3.0


(d) d = 3.0

Fig. 10. Comparisons of Applicability results

Figure 11 shows the comparisons of −E, +E1,+E2 and the non-periodic approach [10] based onsatisfaction for the Top 10, Top 20 and Top 50 userswith 0.5 and d = 4.0. For example, for Top 50 andNPR = 4, the satisfaction values for −E = 86.5%,+E1 = 87.3%, +E2 = 88.1% when d = 0.5, and−E = 86.5%, +E1 = 87.33%, +E2 = 89.8% whend = 4.0. The results again show the usefulness ofemotional influence, especially +E2. Further, although−E seemed invariant to increasing d (as mentioned in4.1), satisfaction results did improve with increasingd with emotional influence.



Also, the results between periodic and non-periodicapproaches were mostly similar except for small NPR.For our approach, the time of each request in thetesting dataset was regarded as the period condi-tion and used for generating periodic personalizedresources; the non-periodic approach used the re-quested resource of each request in the testing datasetas prior knowledge to generate non-periodic person-alized resources by matching the requested resourceagainst the discovered association rules. Our periodicweb personalization approach thus has the advantagethat it does not require the user’s current accessinformation, which makes it possible to perform morecompute-intensive personalized resource preparationin advance rather than in real-time.

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(d) Top 10, d = 4.0

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(f) Top 50, d = 4.0

Fig. 11. Comparisons based on Satisfaction

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Overall

Adaptation

Timeliness

Relevance

Response Time

Ease of Use

Usefulness

Fig. 12. User testing results.

4.3 Subjective Testing

Subjective testing was conducted to gain further in-sight into the system performance through end users’first hand experience. In particular, we asked 30 firstyear computing undergraduate student volunteers toperform user testing over 30 days. During the trialperiod, each user was asked to do a total of at leastfifteen hours of testing with the module on and aminimum of fifteen hours with the module off. Afterthe trial period, we asked the students to completea questionnaire to rate the system from 1 to 5 (5being the highest) under the following categories:usefulness (better/worse/no change with the person-alization module on?), user experience (ease of useand response time/latency), quality of personalizationresults (in terms of relevance and timeliness), adapta-tion (any improvements over time?) and an overallscore (would you recommend the system to others?).The results are summarized in Figure 12.

We also asked the users to provide written com-ments on their experience of using the system. Theircomments are mostly positive, such as “easy to use”,“good aid”, “relevant results” and “fast response”,“got better and better”. Fast response (low latency)was a strong point, since processing was performed inadvance. However, over a quarter of the respondentssaid it was “troublesome” or “tedious”’ to have torecord their ΔE score so frequently. We found that thishad a negative impact on “ease of use”. An on-goingresearch direction is to improve the user experience,especially on self reporting of ΔE or some other formof emotional influence that results from their websurfing experience.

5 CONCLUSION

We have presented an approach for automatic gener-ation of Personal Web Usage Ontology (PWUO) ofperiodic access patterns from web usage logs thathave been semantically enriched with information onemotional influence and resource topics. Over time,the knowledge base can capture both consumer webaccess behavior and emotional influence of the webresources on the user. Experimental results, both fromobjective and subjective tests, have demonstrated the



effectiveness of our approach in providing the userwith periodic web personalization based on periodicaccess patterns generated. Further, by varying the de-grees of emotional influence, we found that emotionalinfluence contributed positively to the results.

With PWUO, consumers’ periodic access behaviorscan be used by software agents to provide SemanticWeb services such as web personalization and se-mantic search. For example, personalized agents canrecommend related web contents, highlight importantlinks to make navigation more effective, and findinteresting information from multiple websites andorganize them as a personal web page to a user.Similarly, search engines can refine user queries andre-rank the search results based on the periodic be-haviors discovered from PWUO. Moreover, specialinterest groups can be formed based on users whohave shared similar patterns and interests based onPWUO.

REFERENCES

[1] R.J. Dolan. Emotion, Cognition, and Behavior. Science,298(5596):1191–1194, 2002.

[2] P. Weinberg and W. Gottwald. Impulsive consumer buying asa result of emotion. Journal of Business Research, 10(1):43–57,1982.

[3] F. Piron. A comparison of emotional reactions experiencedby planned, unplanned and impulse purchasers. Advances inConsumer Research, 20(341-344):199–204, 1993.

[4] J. Kalbach. Designing Web Navigation. O’Reilly, 2007.[5] C. Miao, Q. Yang, H. Fang, and A. Goh. A cognitive approach

for agent-based personalized recommendation. Knowl.-BasedSyst., pages 397–405, 2007.

[6] D. H. Choi and B. S. Ahn. Eliciting Customer Preferencesfor Products from Navigation Behavior on the Web: A Mul-ticriteria Decision Approach with Implicit Feedback. IEEETransactions on System, Man and Cybernetes, Part A, 39(4):880–889, 2009.

[7] J.H. Janssen, J.N. Bailenson, W.A. IJsselsteijn, and J.H.D.M.Westerin. Intimate heartbeats: Opportunities for affectivecommunication technology. IEEE Trans. Affective Computing,1(2):72–80, 2010.

[8] J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan. WebUsage Mining: Discovery and Applications of Usage Patternsfrom Web Data. ACM SIGKDD Explorations, 1(2):12–23, 2000.

[9] WebTrends Web Analytics. Availaible athttp://www.netiq.com/webtrends.

[10] B. Mobasher, H. Dai, T. Luo, and M. Nakagawa. Effective Per-sonalization Based on Association Rule Discovery from webusage data. In WIDM ’01: Proceedings of the 3rd InternationalWorkshop on Web Information and Data Management , at ACMCIKM ’01, pages 9–15, Atlanta, Georgia, USA, 2001.

[11] J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu. Mining AccessPatterns Efficiently from Web Logs. In PAKDD ’00: Proceedingsof the 4th Pacific-Asia Conference on Knowledge Discovery and DataMining, Current Issues and New Applications, pages 396–407,Kyoto, Japan, 2000. Springer-Verlag.

[12] T. Berners-Lee, J. Hendler, and O. Lassila. The Semantic Web.Scientific American, 284(5):34–43, May 2001.

[13] G. Stumme, B. Berendt, and A. Hotho. Usage Mining forand on the Semantic Web. In Next Generation Data Mining,Proceedings of NSF Workshop, pages 77–86, Baltimore, Nov.2002.

[14] S. Middleton, N. Shadbolt, and BD.D. Roure. Capturinginterest through inference and visualization: Ontological userprofiling in recommender systems. In K-CAP ’03: Proceedings ofthe International Conference on Knowledge Capture, pages 62–69,2003.

[15] Rokia Missaoui, Petko Valtchev, Chabane Djeraba, and MehdiAdda. Toward recommendation based on ontology-poweredweb-usage mining. IEEE Internet Computing, 11(4):45–52, 2007.

[16] Ahu Sieg, Bamshad Mobasher, and Robin Burke. Web searchpersonalization with ontological user profiles. In CIKM ’07:Proceedings of the sixteenth ACM conference on Conference oninformation and knowledge management, pages 525–534, NewYork, NY, USA, 2007. ACM.

[17] A. Maedche and S. Staab. Ontology Learning for the SemanticWeb. IEEE Intelligent Systems, 16(2):72–79, 2001.

[18] A. Todirascu, F. De Beuvron, Dan Galea, and F. Rousselot.Using Description Logics for Ontology Extraction. In OL’00: Proceedings of the 1st Workshop on Ontology Learning atthe 14th European Conference on Artificial Intelligence ECAI ’00,volume 31 of CEUR Workshop Proceedings, Berlin, Germany,Aug. 2000.

[19] A. Maedche and S. Staab. Discovering Conceptual Relationsfrom Text. In ECAI ’00: Proceedings of the 14th European Confer-ence on Artificial Intelligence, pages 321–325, Berlin, Germany,Aug. 2000. IOS Press.

[20] P. Clerkin, P. Cunningham, and C. Hayes. Ontology Dis-covery for the Semantic Web Using Hierarchical Clustering.In Proceedings of the 1st Semantic Web Mining Workshop, atECML/PKDD ’01, 2001.

[21] P. Cimiano, S. Staab, and J. Tane. Deriving Concept Hierarchiesfrom Text by Smooth Formal Concept Analysis. In LLWA’03: Proceedings of GI Workshop Lehren-Lernen-Wissen-Adaptivit,pages 72–79, Karlsruhe, Germany, 2003.

[22] T. T. Quan, S. C. Hui, A. C. M. Fong, and T. H. Cao. Au-tomatic Fuzzy Ontology Generation for Semantic Web. IEEETransactions on Knowledge and Data Engineering, 18(6):842–856,2006.

[23] C. De Maio, G. Fenza, V. Loia, and S. Senatore. Towards anautomatic fuzzy ontology generation. In FUZZ-IEEE 2009:Proceedings of the IEEE International Conference on Fuzzy Systems,pages 1044 – 1049, Jeju Island, Korea, August 2009. IEEE.

[24] C. De Maio, G. Fenza, V. Loia, and S. Senatore. A multifacet representation of a fuzzy ontology population. In WebIntelligence/IAT Workshops’09, pages 401–404, 2009.

[25] X. Xu, Y. Wu, and J. Chen. Fuzzy FCA based ontologymapping. In ICNDC ’10: Proceedings of the 1st InternationalConference on Networking and Distributed Computing, pages 181– 185, Hangzhou, China, Oct. 2010.

[26] Z.M. Ma, Y. Lv, and L. Yan. A fuzzy ontology generationframework from fuzzy relational databases. J. Semantic WebInf. Syst., 4(3):1–15, 2008.

[27] H. Dai and B. Mobasher. Using Ontologies to DiscoverDomain-level Web Usage Profiles. In Proceedings of the 2ndSemantic Web Mining Workshop at ECML/PKDD, 2002.

[28] D. Oberle, B. Berendt, A. Hotho, and J. Gonzalez. ConceptualUser Tracking. In AWIC ’03: Proceedings of the 1st InternationalAtlantic Web Intelligence Conference, volume 2663 of LectureNotes in Computer Science, pages 155–164, Madrid, Spain, May2003. Springer-Verlag.

[29] M. Eirinaki, M. Vazirgiannis, and I. Varlamis. SEWeP: Us-ing Site Semantics and a Taxonomy to Enhance the WebPersonalization Process. In KDD ’03: Proceedings of the 9thACM SIGKDD International Conference on Knowledge Discoveryand Data Mining, pages 99–108, Washington, D.C., USA, 2003.ACM Press.

[30] R. Meo, P. L. Lanzi, M. Matera, and R. Esposito. IntegratingWeb Conceptual Modeling and Web Usage Mining. In Pro-ceedings of the WebKDD Workshop on Web Mining and Web UsageAnalysis, pages 105–115, 2004.

[31] P. Fraternali, M. Matera, and A. Maurino. Conceptual-LevelLog Analysis for the Evaluation of Web Application Quality.In LA-WEB ’03: Proceedings of the 1st Latin American WebCongress, Empowering Our Web, pages 46–57, Sanitago, Chile,Nov. 2003. IEEE Computer Society.

[32] D. L. McGuinness and F. Van Harmelen. OWL Web Ontol-ogy Language Overview. W3C Recommendation, Feb. 2004.Availaible at http://www.w3.org/TR/owl-features/.

[33] F. Yergeau, T. Bray, J. Paoli, C. M. Sperberg-McQueen, andE. Maler. Extensible Markup Language (XML) 1.0 (ThirdEdition). W3C Recommendation, Feb. 2004. Availaible athttp://www.w3.org/TR/2004/REC-xml-20040204/.



[34] G. Klyne and J. J. Carroll. Resource Description Framework(RDF): Concepts and abstract syntax. W3C Recommenda-tion, Feb. 2004. Availaible at http://www.w3.org/TR/rdf-concepts/.

[35] N. F. Noy and D. L. Mcguinness. Ontology Development 101:A Guide to Creating Your First Ontology. Technical report,Stanford Medical Informatics, Palo Alto, 2001.

[36] Y. Sure, M. Erdmann, J. Angele, S. Staab, R. Studer, andD. Wenke. OntoEdit: Collaborative Ontology Developmentfor the Semantic Web. In ISWC ’02: Proceedings of the FirstInternational Semantic Web Conference on the Semantic Web, pages221–235, Sardinia, Italy, June 2002. Springer-Verlag.

[37] W. Lin, S. A. Alvarez, and C. Ruiz. Efficient Adaptive-SupportAssociation Rule Mining for Recommender Systems. DataMining and Knowledge Discovery, 6(1):83–105, 2002.

[38] M. N. Moreno, F. J. Garcıa, M. J. Polo, and V. F. Lopez. UsingAssociation Analysis of Web Data in Recommender Systems.In EC-Web ’04: Proceedings of the 5th International Conference onE-Commerce and Web Technologies, volume 3182 of Lecture Notesin Computer Science, pages 11–20, Zaragoza, Spain, Aug. 2004.Springer.

[39] L. A. Zadeh. Fuzzy Logic and Approximate Reasoning.Synthese, 30:407–428, 1975.

[40] C. Wong, S. Shiu, and S. Pal. Mining Fuzzy AssociationRules for Web Access Case Adaptation. In Proceedings of the4th International Conference on Case-Based Reasoning, Case-BasedReasoning Research and Development, page 213220, Vancouver,Canada, 2001.

[41] S. C. K. Shiu and S. K. Pal. Case-Based Reasoning: Concepts,Features and Soft Computing. Applied Intelligence, 21(3):233–238, 2004.

[42] R. Agrawal and R. Srikant. Mining Sequential Patterns. InICDE ’95: Proceedings of the 11th International Conference onData Engineering, pages 3–14, Taipei, Taiwan, March 1995. IEEEComputer Society Press.

[43] L. A. Zadeh. Fuzzy Sets. Journal of Information and Control,8:338–353, 1965.

[44] R. Cooley, B. Mobasher, and J. Srivastava. Data Preparationfor Mining World Wide Web Browsing Patterns. Knowledgeand Information Systems, 1(1):5–32, 1999.

[45] D. Olaru and B. Smith. Modelling Daily Activity Scheduleswith Fuzzy Logic. In Proceedings of the 10th InternationalConference on Travel Behaviour Research, Lucerne, Switzerland,2003. Elsevier.

[46] G. Stumme, R. Taouil, Y. Bastide, N. Pasquier, and L. Lakhal.Computing Iceberg Concept Lattices with TITANIC. DataKnowledge Engineering., 42(2):189–222, 2002.

[47] L. Nourine and O. Raynaud. A Fast Algorithm for BuildingLattices. Information Processing Letters, 71(5-6):199–204, 1999.

[48] R. Godin, R. Missaoui, and H. Alaoui. Incremental ConceptFormation Algorithms based on Galois (Concept) Lattices.Computational Intelligence, 11:246–267, 1995.

[49] E. Bozsak, M. Ehrig, S. Handschuh, A. Hotho, A. Maedche,B. Motik, D. Oberle, C. Schmitz, S. Staab, L. Stojanovic, N. Sto-janovic, R. Studer, G. Stumme, Y. Sure, J. Tane, R. Volz, andV. Zacharias. KAON - Towards A Large Scale Semantic Web.In EC-WEB ’02: Proceedings of the 3rd International Conferenceon E-Commerce and Web Technologies, pages 304–313, Aix-en-Provence, France, Sept. 2002. Springer-Verlag.

[50] B. Zhou, S.C. Hui, and A.C.M. Fong. Efficient sequentialaccess pattern mining for web recommendations. InternationalJournal of Knowledge-Based and Intelligent Engineering Systems,10(2):155–168, 2006.

A.C.M. Fong is professor of information engineering at AucklandUniversity of Technology, New Zealand. Previously, he was an asso-ciate professor at Nanyang Technological University, Singapore. Hisresearch interests include information processing and management,multimedia and communications.

B. Zhou joined IBM Research China as a researcher since 2010.Prior to that, he worked in HP Labs China as a Research Scientistfor more than four years. He received his Ph.D. degree in ComputerEngineering from Nanyang Technological University, Singapore andM.Sc. degree in Control Theory and Control Engineering from Ts-inghua University, China. His research interests include data mining,pattern recognition, machine learning, Semantic Web and digitallibraries.

S.C. Hui is an Associate Professor in the Division of InformationSystems, School of Computer Engineering, Nanyang TechnologicalUniversity, Singapore. He received his B.Sc. degree in Mathematicsin 1983 and D. Phil in 1987 from the University of Sussex, UK. Heworked in IBM China/Hong Kong Corporation as a system engineerfrom 1987 to 1990. His current research interests include datamining, Web mining, Semantic Web, intelligent systems, informationretrieval, intelligent tutoring systems, timetabling and scheduling.

J. Tang is an associate professor in Tsinghua University. His re-search interests are machine learning and text mining.

G.Y. Hong is a senior lecturer in the Department of Computing atUnitec New Zealand. Prior to that, she was a senior lecturer ininformation systems at Massey University. Her research interestsinclude software engineering and information systems.


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Generation of Personalized Ontology Based on Consumer Emotion and Behavior Analysis