Ontological recommendation multi-agent for Tainan City travel

Expert Systems with Applications 36 (2009) 6740–6753

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Expert Systems with Applications

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Ontological recommendation multi-agent for Tainan City travel

Chang-Shing Lee a,*, Young-Chung Chang b, Mei-Hui Wang a

a Department of Computer Science and Information Engineering, National University of Tainan, 33, Section 2, Shu-Lin Street, Tainan 700, Taiwanb Academia Sinica Grid Computing, Academia Sinica, Taipei, Taiwan

a r t i c l e i n f o a b s t r a c t

Keywords:OntologyAgentFuzzy inferenceAnt colony optimizationCity travel

0957-4174/$ - see front matter � 2008 Elsevier Ltd. Adoi:10.1016/j.eswa.2008.08.016

* Corresponding author. Tel.: +886 6 2602573.E-mail address: [email protected] (C.-S. Lee)

Due to the gradual increase in travel, the travel agent plays an important role in both planning andrecommending a personalized travel route. Tainan City, located in the southern Taiwan, is famous forits abundant historic sites and delicious snack food, and it has been one of the top tourist attractionsin Taiwan for years. In this paper, we propose an ontological recommendation multi-agent for Tainan Citytravel. The core technologies of the agent contain the ontology model, fuzzy inference mechanism, andant colony optimization. The proposed agent can recommend the tourist a personalized travel route toenjoy Tainan City according to the tourist’s requirements. It includes a context decision agent and a travelroute recommendation agent. First, the context decision agent finds a suitable location distance, countsthe context relation, and infers the context information based on the tourist’s requirements and TainanCity travel ontology. Next, the travel route recommendation agent is responsible for finding a personal-ized tour and plotting this travel route on the Google Map. Finally, the tourist can follow the personalizedtravel route to enjoy the cultural heritage and the local gourmet food during his stay at Tainan City. Theexperimental results show that the proposed approach can effectively recommend a travel route matchedwith the tourist’s requirements.

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1. Introduction

In recent years, due to the growing number of travelers, a widerange of information such as the tourist attractions and local gour-met food are posted on the Internet to appeal to the tourists. How-ever, it is not easy for a tourist to search the information whatexactly he really wants from a large amount of information avail-able on the Internet. Because of this, if there is an agent to helpwith this heavy work, then it is sure both to reduce travel agency’sworkload and to accelerate the speed of tourist’s getting the travelinformation. What the tourist or the travel agency only does is justto list his travel requirements. Later, the agent would recommend aroute of travel to make the tourist enjoy his trip. Also, Tainan City isa city which features its abundant historic sites, cultures, delicioussnack food, ecological environment, and heritage. Therefore, wecombine Tainan City tourism with some core techniques like theontology, fuzzy inference, and ant colony optimization to proposean ontological recommendation multi-agent for Tainan City travel.

Ontology is a conceptualization of a domain into a humanunderstandable, machine-readable format consisting of entities,attributes, relationships, and axioms (Tho, Hui, & Fong, 2006). Itis also with the characteristic of the reusability, which makes itvery attractive and powerful for representing domain knowledge(Francisco, Rodrigo, Leonardo, Jesualdo, & Dagoberto, 2006). The

ll rights reserved.

.

related applications of the ontology involve in many researchfields. Alani et al. (2003) proposed the Artequakt that attempts toidentify entity relationships using ontology relation declarationsand lexical information to automatically extract knowledge aboutartists from the Web. Francisco et al. (2006) proposed an ontol-ogy-based recruitment system to provide intelligent matching be-tween employer advertisements and the curriculum vitae of thecandidates. Belmonte, Perez-de-la-Cruz, and Triguero (2008)developed a multi-agent decision support system for the bus fleetmanagement domain. Maillot and Thonnat (2008) proposed anontology-based cognitive vision approach for complex object rec-ognition. Hubner, Spittel, Visser, and Vogele (2004) proposed anontology-based search for interactive digital maps to develop theontology for describing vocabularies and catalogues as well assearch mechanisms for keywords with respect to their semantics.Lee et al. proposed a fuzzy ontology application to news summari-zation (Lee, Jian, & Huang, 2005), and an automated ontology con-struction for unstructured text documents (Lee, Kao, Kuo, & Wang,2007).

Ant colony optimization was introduced in the early 1990s as anovel technique for solving hard combinatorial optimization prob-lems and inspired by the behavior of ants in finding paths from thecolony to food (Dorigo, Birattari, & Stutzle, 2006). The algorithm forthe ant colony optimization is stochastic search procedures to findgood paths through graph (Dorigo & Blum, 2005). For the recentyears, the research on the ant colony optimization has been spreadwidely to be critical components in many different applications.

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Han and Shi (2007) proposed an improved colony algorithm forfuzzy clustering in image segmentation. Ho, Yang, Wong, Cheng,and Ni (2005) presented an improved continuous ant colony opti-mization algorithm to apply to electromagnetic devices designs.Fournier and Pierre (2005) proposed an ant colony optimizationheuristic to solve the NP-Hard problem of the assignment of cellsto switches in mobile networks.

An agent represents a fundamental shift in the human–com-puter interaction paradigm. That is, an agent is a program that per-forms unique tasks without direct human supervision (Chen & Yen,1996). In addition, an agent possesses skills; can offer service; iscapable of perceiving its environment, or is driven by a set of ten-dencies (Ferber, 1999). An intelligent agent differs from an agent inpart because of its ability to reason about a task and learn fromtask performance (Chen & Yen, 1996). Delen and Pratt (2006) de-signed and developed an intelligent decision support system formanufacturing. Ishida (2002) proposed a scenario description lan-guage for interactive agents. Soo, Lin, Yang, Lin, and Cheing (2006)presented a cooperative multi-agent platform to help industrialknowledge managers retrieve and analyze existing patent docu-ments, and extract structured information from patents with theaid of ontology and natural language processing techniques. Lee,Jiang, and Hsieh (2006) proposed a genetic fuzzy agent using theontology model for meeting scheduling system. In addition, Leeand Wang (2007) also proposed an ontology-based computationalintelligent multi-agent to apply to Capability Maturity Model Inte-gration (CMMI) assessment.

In this paper, we propose an ontological recommendation mul-ti-agent for Tainan City travel. First, based on Tainan City travelontology predefined by domain experts, the context decision agentfinds the concepts matched with the tourist’s requirement. Then,the ant colony optimization for semantic relation mechanism andthe context relation computation mechanism implement thesemantic relation and context relation with the tourist’s require-ments, respectively. Next, the travel route recommendation agentcarries out the fuzzy inference engine to infer the context informa-tion between the tourist’s requirements and all instances stored inthe ontology. Then, the sorting mechanism sorts the context infor-mation and selects the top three matched historical sites and thetop five matched local gourmet food stores to plan a personalizedtravel route as the tourist’s first-day tour at Tainan City. In addi-tion, the Google-maps-based search mechanism plots these visitedplaces on the Google Map. Finally, the tourist follows this recom-mended travel route to enjoy Tainan City’s tourist attractions anddelicious snacks during his tour days. The remainder of this paperis structured as follows: Section 2 describes the structure of TainanCity travel ontology. Section 3 presents a context decision agent forimplementing the semantic relation and context relation betweenthe tourist’s requirements and predefined ontology. Section 4 pre-sents a travel route recommendation agent to show a recom-mended travel route to the tourist on the Google Map. Theexperimental results are shown in Section 5. Finally, some conclu-sions are drawn in Section 6.

2. The structure of Tainan City travel ontology

This paper proposes an ontological recommendation multi-agent based on Tainan City travel ontology for planning a person-alized travel route to visit Tainan City. The structure and definitionof Tainan City travel ontology are briefly described below.

2.1. Ontology structure

The domain ontology architecture adopted herein, including thedomain layer, category layer, class layer and instance layer (Lee et al.,

2007) is shown in Fig. 1. It adopts four types of inter-conceptualrelations, namely the ‘‘generalization,” ‘‘association,” ‘‘aggregation,”and ‘‘Instance of.” The relationship between the domain layer andthe category layer is ‘‘generalization.” The relationship betweenthe category layer and the concept layer is ‘‘Aggregation.” The ‘‘asso-ciation” relation denotes a semantic relationship among conceptsin the concept layer and the instance layer. The relationship be-tween the concept layer and the instance layer is ‘‘Instance of.” Thedomain layer represents the domain name of the ontology, andcomprises various categories defined by domain experts. The cate-gory layer defines several categories, namely ‘‘category 1, category2, category 3, . . ., category k.” Each concept in the concept layer,contains a concept name Ci , an attribute set fACi1; . . . ;ACiqi

g, andan operation set fOCi1; . . . ;OCiqi

g for an application domain. The in-stance layer also comprises some instances represented the in-stances of the concepts in the concept layer. For example, conceptI1 is an instance of the concept C1.

2.2. Tainan City travel ontology

In this section, we describe a scenario for one application of thispaper. Suppose that there is a foreign visitor who will attend aninternational conference held in Taiwan and then visit Tainan Cityafter the conference. So, he is planning a trip to Tainan City, asouthern Taiwan’s old city, for an upcoming journey. He wants tovisit some popular tourist spots, taste some delicious local food,and buy some gifts for friends during travelling. Information aboutTainan City’s tourism, therefore, is very important to him. First, hemust surf the Tainan City Government’s official tourism Web site(http://tour.tncg.gov.tw/english/index.asp) to learn more aboutTainan City. After that, he knows that there are six administrativedistricts at Tainan City, namely the East District ( ), the WestCentral District ( ), the South District ( ), the North District( ), the Anping District ( ), and the Annan District( ). He also knows that Tainan City is a city with an abundantand diverse historical background, which left it with a wealth ofhistoric sites. According to the age of the historic sites, they are di-vided into three classes, namely the first-class, the second-class,and the third-class, where the first-class denotes the oldest build-ing. Moreover, he even realizes that Tainan City is not only a cul-turally ancient city but also a place filled with different kinds ofstores selling gifts and traditional food, including main course,snack, to cold drink & ice. In accordance with the popularity of eachhistoric site and gourmet food, the Tainan City Government rankseach of them in four grades, that is, most popular, more popular,popular, or less popular grade. Table 1 shows the basic tourisminformation about Tainan City.

This scenario is one of the applications under research anddevelopment of the proposed approach in this paper. If there isan agent with the knowledge of travelling Tainan City, then suchkind of agent is able to do just like what the travel agent does tohelp us with Tainan City’s travel. But first, we need to constructthe knowledge of Tainan City travel for this agent, which is repre-sented in ontology. The design of the Tainan City ontology consid-ers all the factors described above. Based on the domain ontologyarchitecture, shown in Fig. 1, the ontology of Tainan City travel isbuilt. Fig. 2 shows the architecture of Tainan City travel ontology.The domain name of this ontology is ‘‘Tainan City Travel.” The cat-egories in the category layer include ‘‘Administrative District,” ‘‘His-toric Site Class,” ‘‘Gourmet Food Type,” and ‘‘Rating Popularity Grade.”Each concept in the concept layer is with a concept name, an attri-bute set, and an operation set. For example, in the concept layer,concepts ‘‘East District,” ‘‘First-Class Historic Site,” ‘‘Main Course,”and ‘‘Most Popular” have the attribute set ‘‘{Location(1500,900)},”‘‘{Location(1213,864.5)},” ‘‘{Location(1306,849.5)},” and ‘‘{Loca-tion(1192,866.75)},” respectively, where ‘‘Location” means the

http://tour.tncg.gov.tw/english/index.asp

…

C1

AC11,AC12 ,…,AC1q1

C4

AC41,AC42,…,AC4q4

OC41,OC41,…,OC4q4 …

OC11 ,OC11,…,OC1q1

Category 1 Category 2 Category 3 Category k

Domain Layer

Category Layer

Domain

Concept Layer

C3

AC31,AC32 ,…,AC3q3

OC31 ,OC31 ,…,OC3q3

Cm

ACm1,ACm2,…,ACmqm

OCm1 ,OCm1,…,OCmqm

C5

AC51,AC52,…,AC5q5

OC51,OC51,…,OC5q5

C2

AC21,AC22 ,…,AC2q2

OC21 ,OC21 ,…,OC2q2 …

C : Concept

A : Attribute

O : Operation

Aggregation

Association

Generalization

… Instance Layer

…

I3

AI31,AI32 ,…,AI3q3

OI31 ,OI31 ,…,OI3q3

Im

AIm1,AIm2,…,AImqm

OIm1 ,OIm1,…,OImqm

I2

AI21,AI22 ,…,AI2q2

OI21 ,OI21 ,…,OI2q2

I5

AI51,AI52,…,AI5q5

OI51,OI51,…,OI5q5

I4

AI41,AI42,…,AI4q4

OI41,OI41,…,OI4q4

I1

AI11,AI12 ,…,AI1q1

OI11 ,OI11,…,OI1q1

Instance of

Fig. 1. Domain ontology architecture.

Table 1Tainan City’s basic information

Item Category

Administrativedistrict

East district, West central district, South district, Northdistrict, Anping district, Annan district

Historic site class First-class, second-class, third-classGourmet food

typeMain course, snack, cold drink & ice, gift

Rating popularitygrade

Most popular, more popular, popular, less popular

6742 C.-S. Lee et al. / Expert Systems with Applications 36 (2009) 6740–6753

location of the concept on the ontology. Instance ‘‘City God Temple( )” is an instance of concepts ‘‘West Central Dis-trict,” ‘‘Second-Class Historic Site,” and ‘‘More Popular.” Also, its attri-bute set is {Location(22.992,120.209)} and ‘‘Location” means thisinstance’s latitude and longitude. Table 2 shows the some of the in-stances stored in the ontology.

3. Context decision agent for Tainan City travel

This section utilizes the predefined Tainan City travel ontologyto perform the recommendation for the travel route to Tainan City.Fig. 3 displays the structure of the ontological recommendationmulti-agent for Tainan City travel.

There are two agents, including the context decision agent andthe travel route recommendation agent, in the ontological recom-mendation multi-agent. First, the domain experts construct the

Tainan City travel ontology based on the Tainan City travel infor-mation. In addition, the location transfer mechanism also dependson the Tainan City travel information to create the value of theattribution location for the concepts stored in the ontology. Thecontext decision agent retrieves the Tainan City travel ontology, re-ceives the tourist’s requirements to find the matched concepts,then computes each instance’s semantic relation and context rela-tion with the tourist’s requirements. Based on the output of thecontext decision agent, the travel route recommendation agentimplements the fuzzy inference engine to infer the context infor-mation to get the tourist spots met with the tourist’s requirements.Next, it also sorts and selects the top three matched historic sitesand the top five matched local gourmet food stores to be the visitedplaces during the first-day tour. Then, the ant colony optimizationfor travel route finds the personalized travel route and the Google-maps-based search mechanism plots the recommended travelroute on the Google Map. Finally, the tourist is able to follow thisrecommended route to visit the historic sites and get a taste ofthe local gourmet food at Tainan City. The algorithm for the con-text decision agent is given as follows.

Algorithm for Context Decision AgentBEGIN

Input the tourist’s requirements set {R1,R2,...,RN}.Input the ontological concept set fCO1 ;CO2 ; . . . ;COMg.Input the ontological instance set fIO1 ; IO2 ; . . . ; IOK g.Initialize a matched concepts set with tourist’s requirements

MCTR = u.

Fig. 2. Architecture of Tainan City travel ontology.


Initialize a semantic relation set SRS = u.Initialize a context relation set CRS = u.Initialize i = 1Initialize j = 1DO UNTIL (i > N)

DO UNTIL (j > M)

IF Ri matches with COjTHEN

Add COjto the MCTR set.

END IFincrement j

END DO UNTILincrement i

Finding MatchedConcepts

Mechanism

Travel at Tainan CityRequirements

Ant Colony Optimization for

Semantic RelationMechanism

Fuzzy InferenceEngine

Context RelationComputationMechanism

Ant Colony Optimization for

Travel RouteMechanism

Sorting Mechanism Google-Maps-basedSearch Mechanism

Ontological Recommendation Multi-Agent for Tainan City Travel

Context Decision Agent

Travel Route Recommendation Agent

Domain Expert

Tourist

Recommended Travel Route Repository

Tainan City TravelOntology

Tainan CityTravel

Information

Fuzzy RuleBase

Location TransferMechanism

Tainan CityTravel

Information

Fig. 3. Structure of the ontological recommendation multi-agent for Tainan City travel.

Table 2Instance examples of the Tainan City travel ontology

No Instance name Category Attribute

Administrative District Historic site class Rating popularity grade Location

H1 Anping Ford ( ) Anping District First-class Most Popular (23.000,120.161)H2 City God Temple ( ) West Central District Second-class More Popular (22.992,120.209)H3 Great East Gate ( ) East District Third-class More Popular (22.986,120.216)H4 Chihkanlou ( ) West Central District First-class Most Popular (22.997,120.202)H5 Sacrificial Rites Martial Temple ( ) West Central District First-class Most Popular (22.996,120.202)H6 Tainan Confucius Temple ( ) West Central District First-class Most Popular (22.997,120.203)H7 Erkunshen Fortress ( ) Anping District First-class Most Popular (22.987,120.159)H8 Haishan Hall ( ) Anping District Third-class More Popular (23.001,120.162)H9 Tait & Co.( ) Anping District Third-class More Popular (23.003,120.159)

Administrative District Gourmet food type Rating popularity grade

F1 Dusiaoyue Tantze Noodle Soup ( ) West Central District Main course Most Popular (23.002,120.197)F2 Yangs Superb Dumpings ( ) West Central District Main course Most Popular (22.992,120.209)F3 Anping Tongs Tofu Store ( ) Anping District Cold drink & ICE Most Popular (23.008,120.157)F4 Karasumi Dried Mullet Roe ( ) Anping District Gift More Popular (23.007,120.176)F5 Rongs Good Farmer Rice Cake ( ) South District Main course Popular (22.990,120.172)F6 Aizaicheng ( ) West Central District Main course Most Popular (22.988,120.195)F7 Tainan Cai Milkfish Shop ( ) West Central District Main course Most Popular (23.000,120.198)F8 Yongtaishing Sweetmeats Store ( ) Anping District Snack Most Popular (23.000,120.163)F9 Jhous Shrimp Rolls ( ) Anping District Snack Most Popular (22.998,120.183)F10 Martial Temple Rice Cake ( ) West Central District Snack Most Popular (22.996,120.201)F11 Songcun Smoked Food ( ) West Central District Snack Most Popular (22.998,120.203)F12 Cyuande Spring Rolls ( ) West Central District Snack Most Popular (22.997,120.198)F13 Fushenghao ( ) West Central District Snack Most Popular (23.005,120.199)F14 Lilis Fruit Store ( ) West Central District Cold drink & ice Most Popular (22.988,120.204)F15 Fonghuanghua Cake ( ) Anping District Gift Most Popular (23.000,120.161)


END DO UNTILInitialize i = 1Initialize j = 1DO UNTIL (i > K)

Initialize a matched concepts set with the ontologyinstances MCOI = u.

DO UNTIL (j > M)

IF IOimatches with COj

THENAdd COj

to the MCOI set.END IFincrement j

END DO UNTILUnite MCTR with MCOI to build a construction graph GC(V,

E), where V is a set of vertices and E is a set of edges.

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900

100

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

Annan District: (900,400)

Anping District: (1000,900)

South District: (1250,1150)

East District: (1500,900)

North District: (1400,700)

West Central District: (1300,850)

700

800

900

1000

1100

1200

1300

1400

Third-Class Historic Site: (1277,842)

First-Class Historic Site: (1213,864.5) Second-Class Historic Site: (1285,736)

600900 1000 1100 1200 1300 1400 1500 1600

700

800

900

1000

1100

1200

1300

1400

Main Course: (1306.5,849.5)

600900 1000 1100 1200 1300 1400 1500 1600

Snack: (1208.5,866.5)

Cold Drink & Ice: (1245,844.5)

Gift: (1141.5,926)

700

800

900

1000

1100

1200

1300

1400

600900 1000 1100 1200 1300 1400 1500 1600

Popular: (1272.25,864.75)Most Popular: (1192.5,866.75)

More Popular: (1242.5,837.25) Less Popular: (1340,790)

Popular Historic Site: (1350,824)

Popular Gourmet Food: (1191.5,877.5)

More Popular Historic Site : (1210,814)

More Popular Gourmet Food : (1275,860.5)

Most Popular Historic Site: (1129,878.5)

Most Popular Gourmet Food: (1255,855)

Less Popular Historic Site: (1340,790)

Fig. 4. The location map for concepts belonging to category (a) Administrative District (b) Historic Site Class (c) Gourmet Food Type, (d) Rating Popularity Grade.


Find a suitable tour using the ant colony optimization pro-cess shown as Fig. 8.

Calculate the location distance by Eqs. (2) and (3) to obtain thesemantic relation SRIOi

between IOiand the tourist’s requirements.

Add SRIOito the SRS set.

Accumulate the number of concepts stored in the MCOI setto obtain the context relation CRIOi

between IOiand the tourist’s

requirements.Add CRIOi

to the CRS set.increment iEND DO UNTIL

END

The context decision agent contains a finding matched conceptsmechanism, an ant colony optimization for semantic relation mech-anism, and a context relation computation mechanism. In addition, alocation transfer mechanism must be implemented to build the Tai-nan City travel ontology. Now, we describe each of them as below.

3.1. Location transfer mechanism

The location transfer mechanism is now introduced. The mainfunctions of this mechanism are to acquire the location value of

each concept. First, according to the geography of the Tainan City’sadministrative districts, we set the location value of concepts ‘‘EastDistrict,” ‘‘West Central District,” ‘‘South District,” ‘‘North District,”‘‘Anping District,” and ‘‘Anna District” to be (1500,900),(1300,850), (1250,1150), (1400,700), (1000,900), and (900,400),respectively.

Fig. 4a–d shows the location map for concepts belonging to cat-egories ‘‘Administrative District,” ‘‘Historic Site Class,” ‘‘Gourmet FoodType,” and ‘‘Rating Popularity Grade,” respectively. Fig. 5 shows thestatistic results for the concepts of the ontology. For example,Fig. 5a provides the information about how many percent of thefirst-class, second-class, or third-class historic sites are located inthe East District, West Central District, South District, North District,Anping District, and Annan District.Fig. 5b and c shows the statisticsanalysis in the percent of the gourmet food type and rating popu-larity grade, respectively. The distribution results contain 37 his-toric sites, six stores selling the local special gifts, and 58restaurants including serving local main course, cold drink & ice,and snack.

The location value x is generated by the value xi and its associ-ated weight wi. Similarly, the location value y is generated by thevalue yi and its associated weight wi. Eq. (1) shows the formulafor the location value,

Fig. 5. The distribution of the (a) historic site class, (b) gourmet food type, and (c) rating popularity grade for each Tainan City’s administrative district.


x ¼PNi¼1

xiwi;

y ¼PNi¼1

yiwi;

8>>><>>>:

ð1Þ

where N denotes the number of concepts belonging to category‘‘Administrative District,” xi denotes the value of x-axis for the ithconcept of the category ‘‘Administrative District,” yi denotes the va-lue of y-axis for the ith concept category ‘‘Administrative District,”and wi denotes the associated weight for the ith concept category‘‘Administrative District.” The location for concept ‘‘Most Popular” isthe mid-value between concepts ‘‘most popular historic site” and‘‘most popular gourmet food.” The computing method for concepts‘‘More Popular,” ‘‘Popular,” and ‘‘Less Popular,” is the same as concept‘‘Most Popular.”

3.2. Finding matched concepts mechanism

The finding matched concepts mechanism is responsible for find-ing the concepts matched with the tourist’s requirements. Fig. 6 isan example of how the matched concept is found. In addition, thismechanism is also charge of finding the concepts matched with theeach instance stored in the ontology. Suppose that there is a sce-nario that a tourist wants to spend one-day visiting some ‘‘mostpopular” ‘‘first-class historic sites” and restaurants serving ‘‘maincourse” in Tainan City’s ‘‘West Central District.” Because of this, aset of concepts, including ‘‘Main Course,” ‘‘Most Popular,” ‘‘First-Class Historic Site,” and ‘‘West Central District,” is regarded as the

matched concepts marked with a circle in Fig. 6. Take instance‘‘Rongs Good Farmer Rice Cake ( )” for exam-ple. Located in the ‘‘South District” of Tainan City, this store servessome ‘‘popular” ‘‘snacks” such as rice cake. Therefore, the matchedconcepts for this instance are marked with a square and shown inFig. 6.

3.3. Ant colony optimization for semantic relation mechanism

The main function of the ant colony optimization for semanticrelation mechanism is to find the semantic relation based on thefound path of concepts between the ontology and tourist’s require-ments. Now, an ant colony optimization for travelling salesmanproblem is applied to find the suitable tour among matched con-cepts stored in the ontology. The detailed introduction to an antcolony optimization for travelling salesman problem is describedin Section 4. First, some parameters about the ant colony optimiza-tion are initialized, including the number of iterations N = 100, thetotal number of ants K = 10, determining the relative importance ofpheromone versus distance parameter b = 2, pheromone decayparameter for global pheromone updating q = 0.1, pheromone de-cay parameter for local pheromone updating / = 0.1, and deter-mining the relative importance of exploitation versus explorationparameter q0 = 0.9. In Fig. 6, the concepts marked with a circleare the ones which are matched with the tourist’s requirements.Located in ‘‘West Central District” of Tainan City, Chihkanlou( ) is a ‘‘first-class historic site” and has been one of the‘‘most popular” tourist spots. Therefore, the concepts matched with

Third-Class Historic Site

A: {Location(1277,842)}

Popular

A: {Location(1254.75,862)}

More Popular

A: {Location(1231.5,843.5)}

Less Popular

A: {Location(1350,775)}

Gift

A: {Location: (1141.5,926)}

Second-Class Historic Site

A: {Location(1285,736)}First-Class Historic Site

A: {Location(1213,864.5)}

Most Popular

A: { Location(1195,863.75)}

Main Course

A: {Location: (1277.5,860.5)}

Snack

A: {Location: (1233,850.5)}

Cold Drink & Ice

A: {Location: (1216,856)}

Anping District

A: {Location: (1000,900)}

West Central District

A: {Location: (1300,850)}

South District

A: {Location: (1250,1150)}

East District

A: {Location: (1500,900)}

Annan District

A: {Location: (900,400)}

North District

A: {Location (1400,700)}

Fig. 6. Example of the ant colony optimization for semantic relation mechanism.

Table 3Fuzzy rules of the fuzzy inference engine

Rule no Fuzzy variable

Semantic relation (SR) Context relation (CR) Context information (CI)

1 VeryHigh High VeryHigh2 VeryHigh Medium VeryHigh3 VeryHigh Low High4 High High VeryHigh5 High Medium High6 High Low Medium7 Medium High High8 Medium Medium Medium9 Medium Low Low10 Low High Medium11 Low Medium Low12 Low Low VeryLow13 VeryLow High Low14 VeryLow Medium VeryLow15 VeryLow Low VeryLow


instance ‘‘Chihkanlou ( )” are the same as ones matchedwith the tourists requirements so that the formed graph is drawnwith solid lines in Fig. 6. Then, the distance between each of con-cepts is computed by Eq. (2), and the location distance for this caseis obtained by Eq. (3). If the instance is ‘‘Rongs Good Farmer RiceCake ( ),” then the built tour surround bydotted lines is shown in Fig. 6. It indicates that the more the con-cepts not matched with the tourist’s requirements are, the longerthe location distance is. When the longer the location distance is,the farther the semantic relation with the tourist’s requirementsis. Therefore, when the shorter the location distance is, the moresimilar the instance is to the tourist’s requirements

dðr; sÞ ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðxr � xsÞ2 þ ðyr � ysÞ

2q

; ð2Þ

LD ¼XM

r¼1;s¼1

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðxr � xsÞ2 þ ðyr � ysÞ

2q

; ð3Þ

where LD denotes the location distance, d(r,s) is the distance be-tween vertices r and s, and M is the number of vertices.

3.4. Context relation computation mechanism

The context relation computation mechanism is to compute thecontext relation between the tourist‘s requirements and the in-stances stored in the ontology. If this instance is a historic site, thenit has three context relations, including the located administrativedistrict, the historic site class, and the rating popularity grade. Onthe other hand, if this instance is a restaurant serving famousdishes or a store selling special local gifts, the context relation con-tains the located administrative district, the gourmet food type, andthe rating popularity grade. For example, if the tourist’s require-ments include concepts ‘‘First-Class Historic Site,” ‘‘Anping District,”and ‘‘Most Popular,” then the number of context relation with in-stance ‘‘Anping Fort ( )” is 3 because ‘‘Anping Fort” is sui-ted in the ‘‘Anping District” of Tainan City and one of the mostpopular first-class historic sites.

4. Travel route recommendation agent for Tainan City travel

This section introduces the travel route recommendation agent,including a fuzzy rule base, a fuzzy inference engine, a sortingmechanism, an ant colony optimization for travel route mecha-


nism, and a Google-maps-based search mechanism. The travelroute recommendation agent receives the results from the contextdecision agent and infers the context information. Then, the agentsorts the matched instances to generate a recommended travelroute. Finally, this agent displays this personalized travel routeon the Google Map to the tourist, and stores it to the recommendedtravel route repository. The algorithm for the travel route recom-mendation agent is given as below.

Algorithm for Travel Route Recommendation AgentBEGIN

Input the semantic relation set SRS = {SRIO1 ; SRIO2 ; . . . ; SRIOK }.Input the context relation set CRS = {CRIO1 ;CRIO2 ; . . . ;CRIOK }.Initialize the context information set CIS = u.Initialize i = 1DO UNTIL (i > K)

Infer the context information CIIOibetween SRIOi

and CRIOiby

the fuzzy inference engine.Add CIIOi

to CIS set.increment i

END DO UNTILInitialize the context information for the historic site set

CISHS = u.Initialize the context information for the gourmet food set

CISGF = u.Initialize i = 1DO UNTIL (i > K)

If CIIOiis a context information with Instances belonging to

‘‘Historic Site” THENAdd CIIOi

to the CISHS

END IF

Mean+50Mean-50 MeanMean-100 Mean+100

1

Location Distance

SR_VeryHigh SR_High SR_Medium SR_Low SR_VeryLow

0

Membership Degree

1

0

Mem

0.4 0.60.2

1

0

CI_VeryLow CI_Low CI_Medium

Membership Degree

Fig. 7. Membership functions of the fuzzy variables: (a) semantic relation

If CIIOiis a context information with Instances belonging to

‘‘Gourmet Food” THENAdd CIIOi

to the CISGF

END IFincrement i

END DO UNTILInitialize i = 1DO UNTIL (i > K)

Sort CISHS set in an ascending order and obtainCISHS = {CIHS

IO1;CIHS

IO2; � � � ;CIHS

IOM}.

Sort CISGF in an ascending order and obtainCISGF = {CIGF

IO1;CIGF

IO2; . . . ;CIGF

ION}.

where M + N = Kincrement i

END DO UNTILSelect the historic sites with the top three high CI and obtain

historic site set HSS = {HS1, HS2, HS3}.Select the gourmet food with the top five high CI and obtain

gourmet food set GFS = {GF1, GF2, . . ., GF5}.Unite HSS with GFS to build a construction graph GC(V, E),

where V is a set of vertices and E is a set of edges.Find the suitable tour using the ant colony optimization pro-

cess shown as Fig. 8 to be the first-day recommended tour.Call the Google-maps-based search mechanism to plot the

first-day recommended route on the Goggle Map.END

4.1. Fuzzy inference engine

In this section, the fuzzy inference engine (Lee & Wang, 2007) isintroduced. Herein, the results of the context decision agent are

2 31 Context Relation

CR_Low CR_Medium CR_High

bership Degree

0.8 1 Context Information

CI_High CI_VeryHigh

(SR), (b) context relation (CR), and (c) context information (CI).

Initialize the parameters

Lay equal pheromone on each path

Each ant k positioned on node rchooses the city s

to move by Eqs. (5) and (6)

Updatelocal pheromone

By Eq. (7)

All ants K have visitedall cities

at this iteration?

Updateglobal pheromone

by Eq. (8)

Ant k visits all cities?

Yes

No

Reach the numberof iterations N?

StopYes

No

No

Begin

Yes

Note: K is the total number of the antsN is the number of iterations

Fig. 8. Process of the ant colony optimization for travelling salesman problem.


adopted to infer the context information between the tourist’srequirements and each instance stored in the ontology. Table 3shows the fuzzy rules predefined by domain experts. A trapezoidalmembership function for fuzzy set FS specified by four parametersFS(x: a,b,c,d) is given in the following equation

FSðx : a; b; c;dÞ ¼

0 x < a;

ðx� aÞ=ðb� aÞ a 6 x < b;

1 b 6 x 6 c;

ðd� xÞ=ðd� cÞ c < x 6 d;

0 x > d:

8>>>>>>><>>>>>>>:

ð4Þ

The linguistic terms of input fuzzy variable SR are VeryHigh, High,Medium, Low, and VeryLow. Fig. 7a shows the membership functionsfor fuzzy sets SR_VeryHigh(x: 0, 0, Mean � 100, Mean � 50),SR_High(x: Mean � 100,Mean � 50,Mean � 50,Mean), SR_Medium(x:Mean � 50,Mean,Mean,Mean + 50), SR_Low(x: Mean,Mean + 50,Mean + 50,Mean + 100), and SR_VeryLow(x: Mean + 50,Mean + 100,

Table 4The detailed travel requirements for tourists

Tourist no Tour day Rating popularity grade Adm

1 One-day Most Popular We

2 Two-day Most Popular NorWeAnp

Mean + 100,Mean + 100). The semantic relation represents thesemantic relation between the tourist’s requirements and instancesstored in the ontology. In Fig. 7a, the value of Mean is selected as thethreshold value for determining the membership function for fuzzyvariable SR. The Mean represents the value that averages the loca-tion distance of all instances stored in the ontology. But, the in-stances are divided into two categories, historic sites and gourmetfood, so each category has its own Mean value. Fig. 7b shows themembership functions for fuzzy sets CR_Low(x: 0,0,1,2), CR_Me-dium(x: 1,2,2,3), and CR_High(x: 2,3,3,3). Fig. 7c displays the mem-bership functions for fuzzy sets CI_VeryLow(x: 0,0,0,0.4), CI_Low(x:0.2,0.4,0.4,0.6), CI_Medium(x: 0.4,0.6,0.6,0.8), CI_High(x:0.6,0.8,0.8,1), and CI_VeryHigh(x: 0.8,1,1,1). If the value of CI ishigh, then the membership degree for the CI similarity is high.The algorithm for the fuzzy inference engine is given as follows:

Algorithm for Fuzzy Inference EngineBEGIN

Input the semantic relation set SRS = fSRIO1; SRIO2 ; . . . ; SRIOK

g.Input the context relation set CRS = fCRIO1

;CRIO2 ; . . . ;CRIOKg.

Input fuzzy setAin = {SR_VeryLow, SR_Low, SR_Medium, SR_High, SR_Very-

High, CR_Low, CR_Medium, CR_High}Aout = {CI_VeryLow, CI_Low, CI_Medium, CI_High,

CI_VeryHigh}Input fuzzy inference rules set FIR = {Rule 1, Rule 2, ..., Rule L}Initialize i = 1DO UNTIL (i > K)

Initialize j = 1DO UNTIL (j > N)

Let Xij = (Vij-SR, Vij-CR), where Vij-SR and Vij-CR are thesemantic relation and the context relation between the touristrequirements and IO1 , respectively.

Initialize l = 1DO UNTIL (l > L)

Calculate the matching degree of the Rule k bylij l ¼ MINðlAinðXijÞÞ

Calculate the center of area of the Rule l byyAout ij l ¼ COAðlij lÞ

increment kEND DO UNTILInitialize p = 1DO UNTIL (p > P)

Calculate the membership values of Xij to the fuzzyclasses Fij_p by

yij p ¼ MAXðyAout ij lÞwhere Fij_p means the fuzzy class for all of fuzzy

rules. Each fuzzy class is an aggregation of the fired rules thathave the same consequences.

increment pEND DO UNTILCIij ¼

PPp¼1wij pyij p=

PPp¼1wij p, where wij_p means the

weight for yij_p.increment j

inistrative district Historic site class Gourmet food type

st Central District First-class Main courseCold drink & ice

th District First-class Main coursest Central District Second-Class Snacking District Third-class Cold drink & ice

Gift

Fig. 9. Information about the first tourist’s (a) travel requirements, (b) matched historic sites and gourmet food, and (c) recommended travel route to Tainan City.


END DO UNTILincrement i

END DO UNTILEND

4.2. Sorting mechanism

The function of the sorting mechanism is to sort the contextinformation between the tourist’s requirements and the ontology

Fig. 10. Information about the second tourist’s (a) travel requirements, (b) matched historic sites and gourmet food, (c) first-day, and (d) second-day recommended travelroute to Tainan City.


in a descending order. Next, according to the tourist’s selected tourdays, the sorting mechanism chooses the top three matched his-toric sites and the top five matched gourmet food as the visitedplaces during first-day stay at Tainan City. In addition, if this tour-ist wants to stay at Tainan City more than one-day, then the sortingmechanism chooses the next three matched historic sites and nextfive matched gourmet food to be the next-day travel schedule.

4.3. Ant colony optimization for travel route mechanism

An ant colony system is introduced in this subsection. In thissystem, a set of cooperating agents called ants search in parallelfor good solutions to the travelling salesman problem and cooper-ate through pheromone-mediated indirect and global communica-tion (Dorigo & Gambardella, 1997). The main process of the antcolony system is shown in Fig. 8. The state transition rule whichmeans an ant positioned on node r chooses the city s to move is fol-lowed the exploitation of a prior and accumulated knowledgeabout the problem or the exploration of new edges. The appliedrule is given by Eqs. (5) and (6). In addition, the pheromone depos-ited by ants evaporates with time, so there are two pheromoneupdate rules to allocate a greater amount of pheromone to shorter

tours. A local pheromone updating rule is applied while ants con-struct a solution and is calculated by Eq. (7). A global pheromoneupdating rule is applied only to edges which belong to the best tourand is given by Eq. (8)

s ¼arg max

u2JkðrÞf½sðr;uÞ� � ½gðr;uÞ�bg; if q 6 q0 ðExploitation RuleÞ;

S; otherwise ðExploration RuleÞ;

8<:

ð5Þ

where g(r, u) = 1/d(r, u), d(r, u) is the distance between cities r and u ,Jk(r) is the set of cities that remained to be visited by ant k posi-tioned on city r, b is a parameter which determines the relativeimportance of pheromone versus distance and is over zero, q is arandom number uniformly distributed in [0,1], q0 ranging from 0to 1 is a parameter determining the relative importance of exploita-tion versus exploration, s(r, u) is the amount of pheromone on edge(r,u), and S is a random variable selected according to the probabil-ity distribution given in the following equation:

pkðr; sÞ ¼½sðr;sÞ��½gðr;sÞ�bP

u2Jk ðrÞ½sðr;uÞ��½gðr;uÞ�b

; if s 2 JkðrÞ;

0; otherwise;

8<: ð6Þ

Fig. 10 (continued)


where pk(r, s) denotes the probability with which ant k in city rchooses to move to the city s and s is the pheromone

sðr; sÞ ð1�uÞ � sðr; sÞ þu � Dskðr; sÞ; ð7Þ

where Dsk(r, s) = s0, / is a pheromone decay parameter between 0and 1, and s0 is the initial pheromone concentration

sðr; sÞ ð1� qÞ � sðr; sÞ þ q � Dskðr; sÞ; ð8Þ

where (i) Dskðr; sÞ ¼1

Lgb; ifðr; sÞ 2 global-best-tour;

0; otherwise

�; (ii) q is a

pheromone decay parameter between 0 and 1; and (iii) Lgb is thelength of the globally best tour from the beginning of the trial.

The ant colony optimization for travelling salesman problem isapplied to plan a recommended travel route based on the matchedtourist spots. Every matched historic site and gourmet food has alocation attribute which stores its geographical coordinates, thatis, longitude and latitude values. By following the process shownin Fig. 8, a personalized travel route is recommended and displayedto the tourist.

4.4. Google-maps-based search mechanism

The Google Maps API is embedded in the Google-maps-basedsearch mechanism. The main task of this mechanism is to plotthe recommend travel route on the Google Map. In this way, some


directions, interactive maps, and satellite imagery of the recom-mended route can be provided for the tourist, which makes it eas-ier for the tourist to understand his travel schedule and have a funon his journey to Tainan City.

5. Experimental results

We have constructed an experimental Web site at National Uni-versity of Tainan to test the performance the proposed approach(http://140.133.13.43:8080/UDOEAT_En/index.html). The experi-mental information comes from Tainan City’s government tourismWeb site, including 37 historic sites, 6 stores selling the local spe-cial gifts to friends, and 58 restaurants serving local main course,cold drink & ice, and snack.

Suppose that one tourist wants to visit some of the ‘‘West Cen-tral District’s” ‘‘most popular” ‘‘first-class historic sites” and tastesome of the ‘‘West Central District’s” ‘‘most popular” ‘‘gourmet food”for his one-day tour at Tainan City. The detailed information aboutthis tourist’s travel requirements are listed in Table 4. Then, thebuilt Web site is allowed the tourist to set up his requirementsshown in Fig. 9a. The places matched with the tourist’s require-ments are listed in Fig. 9b. The recommend travel route for theone-day tour to Tainan City is shown in Fig. 9c and the detailedinformation for each tourist spot marked with a label is listed inTable 2. Fig. 9c indicates that the proposed agent recommends thatthe tourist can follow below route to spend his time visiting somehistoric sites and tasting some local traditional food at Tainan City.

Yangs Superb Dumplings ( ) ? Lilis Fruit Store( ) ? Tainan Confucius Temple ( ) ? DusiaoyueTantze Noodle Soup ( ) ? Sacrificial Rites Martial Tem-ple ( ) ? Chihkanlou ( ) ? Tainan Cai Milkfish Shop( ) ? AizaiCheng ( ).

In the second experiment, we suppose that another touristwants to spend two-days travelling Tainan City and the detailedinformation is listed in Table 4. Fig. 10a shows the second tourist’srequirements. Fig. 10b shows the recommend places in which toappreciate the brilliant culture and historical heritage of TainanCity as well as to enjoy the local traditional food. Fig. 10c and dshows the recommended travel route for the first-day tour andthe second-day tour, respectively. The detailed information foreach tourist spot marked with a label in Fig. 10c and d is listedin Table 2. The directions on two-day travel route are as follows:

Lilis Fruits Store ( ) ? Chihkanlou ( ) ? Cyu-ande Spring Rolls ( ) ? Fushenghao ( ) ? Fon-ghuanghua Cake ( ) ? Anping Fort ( ) ?Anping Tongs Tofu Store ( ) ? Erkunshen Fortress( ).

Yangs Superb Dumpings ( ) ? Tainan ConfuciusTemple ( ) ? Martial Temple Rice Cake ( ) ? Song-cun Smoked Food ( ) ? Jhous Shrimp Rolls ( ) ?YongTaishiung Sweetmeats Store ( ) ? Haishan Hall( ) ? Tait & Co. ( ).

6. Conclusion

The ontological recommendation multi-agent for Tainan Citytravel, including a context decision agent and a travel route rec-ommendation agent, is presented in this paper. The proposedagent can recommend the tourist a personalized travel route toenjoy Tainan City according to the tourist’s requirements. Thecontext decision agent finds the matched concepts, uses the antcolony optimization to get the semantic relation, and countsthe context relation between the tourist’s requirements and eachinstance stored in the ontology. The travel route recommendation

agent infers the context information based on the output of thecontext decision agent. In addition, it is also responsible for find-ing a suitable tour and plotting this travel route on the Googlemap. Hence, the tourist can follow the suggested personalizedtravel route to enjoy the cultural heritage and the local gourmetfood during his stay at Tainan City. The experimental resultsshow that the proposed agent can recommend a personalizedtravel route to the tourist. In the future, we will combine our ap-proach to Web 2.0 technology and add various the machinelearning mechanisms to the intelligent agent. In addition, theontology learning issue will be further considered and imple-mented to enhance the robustness of the ontology model. More-over, the domain ontology for Tainan City travel can be appliedto other cities or fields.

Acknowledgement

The authors would like to thank the partially support sponsoredby the National Science Council of Taiwan under the grant NSC95-2221-E-024-009-MY2 and NSC97-2221-E-024-011-MY2.

References

Alani, H., Kim, S., Millard, D. E., Weal, M. J., Hall, W., Lewis, P. H., et al. (2003).Automatic ontology-based knowledge extraction from web documents. IEEEIntelligent System, 18(1), 14–21.

Belmonte, M. V., Perez-de-la-Cruz, J. L., & Triguero, F. (2008). Ontologies and agentsfor a bus fleet management system. Expert Systems with Applications, 34(2),1351–1365.

Chen, H., & Yen, J. (1996). Toward intelligent meeting agents. IEEE Computer, 29(8),62–70.

Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization: Artificial antsas a computational intelligence technique. IEEE Computational Intelligence, 1(4),28–39.

Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. TheoreticalComputer Science, 344(2–3), 243–278.

Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: A cooperative learningapproach to the traveling salesman problem. IEEE Transactions on EvolutionaryComputation, 1(1), 53–66.

Delen, D., & Pratt, B. (2006). An integrated and intelligent DSS for manufacturingsystem. Expert Systems with Applications, 30(2), 325–336.

Francisco, G. S., Rodrigo, M. B., Leonardo, C., Jesualdo, T. F. B., & Dagoberto, C. N.(2006). An ontology-based intelligent system for recruitment. Expert Systemswith Applications, 31(2), 248–263.

Ferber, J. (1999). Multi-agent systems. New York: Addison-Wesley.Fournier, J. R. L., & Pierre, S. (2005). Assigning cells to switches in mobile networks

using an ant colony optimization heuristic. Computer Communications, 28(1),65–73.

Hubner, S., Spittel, R., Visser, U., & Vogele, T. J. (2004). Ontology-based search forinteractive digital maps. Intelligent Systems, 19(3), 80–86.

Han, Y., & Shi, P. (2007). An improved ant colony algorithm for fuzzy clustering inimage segmentation. Neurocomputing, 70(4–6), 665–671.

Ho, S. L., Yang, S., Wong, H. C., Cheng, K. W. E., & Ni, G. (2005). An improved ancolony optimization algorithm and its application to electromagnetic devicesdesigns. IEEE Transactions on Magnetics, 41(5), 1746–1767.

Ishida, T. (2002). Q: A scenario description language for interactive agents.Computer, 35(11), 27–42.

Lee, C. S., Jian, Z. W., & Huang, L. K. (2005). A fuzzy ontology and its application tonews summarization. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, 35(5), 859–880.

Lee, C. S., Jiang, C. C., & Hsieh, T. C. (2006). A genetic fuzzy agent using ontologymodel for meeting scheduling system. Information Sciences, 176(9), 1131–1155.

Lee, C. S., Kao, Y. F., Kuo, Y. H., & Wang, M. H. (2007). Automated ontologyconstruction for unstructured text documents. Data & Knowledge Engineering,60(3), 547–566.

Lee, C. S. & Wang, M. H. (2007). Ontology-based computational intelligent multi-agent and its application to CMMI assessment. Applied Intelligence. doi:10.1007/s10489-007-0071-1.

Maillot, N. E., & Thonnat, M. (2008). Ontology based complex object recognition.Image and Vision Computing, 26(1), 102–113.

Soo, V. W., Lin, S. Y., Yang, S. Y., Lin, S. N., & Cheing, S. L. (2006). A cooperative multi-agent platform for invention based on patent document analysis and ontology.Expert Systems with Applications, 31(4), 766–775.

Tho, Q. T., Hui, S. C., & Fong, A. C. M. (2006). Automatic fuzzy ontology generation forsemantic web. IEEE Transactions on Knowledge and Data Engineering, 18(6),842–856.

http://140.133.13.43:8080/UDOEAT_En/index.html

http://dx.doi.org/10.1007/s10489-007-0071-1

http://dx.doi.org/10.1007/s10489-007-0071-1

Ontological recommendation multi-agent for Tainan City travel

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