I. INTRODUCTION II. RELATED STUDY

Transcription

1 A Context-Aware based Dynamc User Preference Profle Constructon Method JeeHyun Km Qan Gao Young Im Cho Department of Computer Software School of nformaton College of Informaton Technology Seol Unversty Shandong Polytechnc Unversty Unversty of Suwon Seoul-s, Korea Jnan, Shandong Provnce, Chna Hwaseong-s, Gyeongg-do, Korea Abstract Ths paper proposed a new horzon for the personalzed nformaton retreval whch can use mage nput nstead of text query to express the user s ntenton. Furthermore, ths paper proposed a novel dynamc user preference profle constructon method whch can comprehensvely trac the users local devce operaton behavor and browsng behavor by usng the context nformaton (nteractve hstorcal nformaton and user nformaton related wth the retreval whch are stored and adopted n all of the smart devces owned by the same user (such as documents, E-mal, pcture and so on to grasp the relevance between the dfferent documents more accurately. Smulaton used the dataset of a branchng factor wth a depth of four levels n the herarchy of the Open Drectory, and the smulaton results show that the proposed method can meet the user s ntenton much better and acheve hgher precson rato and recall rato than other nowledge or context based personalzed nformaton retreval systems. Index Terms Context-Aware, Dynamc, Behavor, Weghted text query cannot express the exact ntenton of the user, even an expert may not use lmted text query to express ther meanng, whle the query that s prone to ambguty can be elaborated clearly by some unstructured data, such as pctures, vdeo. Therefore, some methods that can analyze and understand other nds of data need to be researched. Amng at those problems, n ths paper, we proposed a novel dynamc user preference profle constructon method whch can comprehensvely trac the users local devce operaton behavor and browsng behavor by mang use of the context nformaton (nteractve hstorcal nformaton and user nformaton related wth the retreval whch are stored and used n all of the smart devces owned by the same user (such as documents, E-mal, pcture and so on to grasp the relevance between dfferent documents more accurately. The rest of ths paper s organzed as follows: Secton 2 gves a short revew of the related wor. Secton 3 descrbes the framewor and the realzaton of our system. Secton 4 gves the smulaton based on our proposed method and we arrve at the concluson n Secton5. I. INTRODUCTION We lve n an era loaded wth the enormous amount of nformaton, and t s shown by statstcs from authortatve organzatons that there are more than 9 bllons of publcly ndexed Web pages, as n []. The Internet has become the man source for people to obtan and exchange nformaton. The ey ssue of the nowadays nformaton retreval system s to tae the user s own stuatons or crcumstances nto account. Put another way, t should provde personalzed retreved nformaton accordng to varous demands of dfferent users. But nowadays t s dffcult to effectvely gather nformaton and retreve the most relevant documents on the topc of nterest from the Web because of the large amount of nformaton n all types of formats. Researches devote themselves to modelng a dynamc user profle to record the user s preference, but n most cases they focus on the tracng of the user s browsng behavor, nstead of capturng the user s local computer operaton behavor. Besde, tradtonal ontology based personalzed nformaton retreval system normally classfy the concept on the bass of the general doman ontology, whch cannot ndcate the dfference between ndvduals. Furthermore, sometmes, the tradtonal II. RELATED STUDY One of the most promsng methods of realzng the personalzed nformaton retreval s to create the user preference profle. User profles may nclude demographc nformaton such as name, age, country, educaton level, etc, and may also represent the nterests or preferences of ether a group of users or an ndvdual. Personalzaton of Web portals, for example, may focus on ndvdual users. In order to buld a user profle, some source of nformaton about the user must be collected through drect user nterventon, or mplctly, through agents that montor user actvty. Although profles are typcally bult only from topcs of nterest to the user, some proects have pursued the method to nclude nformaton about non-relevant topcs n the profle, as n [2] [3]. In these approaches, the system s able to use both nds of topcs to dentfy relevant documents and dscard non-relevant documents at the same tme. Hence n general, a good user preference profle should comprse the results of lexcal analyss, the nput query, the documents clced by the user, the queres by the user n the past, and some weght values. However, a user preference profle that ncludes ncorrect user preferences only brngs about trouble 24

2 to users. Incorrect user preferences are generally obtaned by the statc profle approach. In ths statc profle approach, preferences or weght values are statc whch do not change once the user preference profle s created. In contrast to statc profles that mantan the same nformaton over tme, dynamc profles can be modfed or augmented, whch taes tme nto consderaton and can dfferentate between short-term and long-term nterests, as n [4] [5]. Short-term profles represent the user s current nterests whereas long-term profles ndcate nterests that are not subect to frequent changes over tme. User profles are generally represented as sets of weghted eywords, semantc networs, or weghted concepts, or assocaton rules. Keyword profles are the smplest to buld, but because fundamentally they have to capture and represent all (or most words by whch nterests may be dscussed n future documents, they requre a large amount of user feedbac n order to learn the termnology by whch a topc mght be dscussed. Semantc user profles have an advantage over eyword-based profles because they can explctly model the relatonshp between partcular words and hgher-level concepts. Thus, they can deal more effectvely wth the nherent ambguty and synonymy of natural language. natural language automatcally. Based on these natural languages, the system can fnd whch mages stored n the mage database are most relevant wth the nput mages query. mage query User Devce DC NB SP PA text query Automatc Image Annotaton Agent Annotaton Search Agent WordNet User Devce based Document Local behavor and Browsng behavor User Preference Profle Constructon Agent Ontology III. PROPOSED METHOD Natural language Sentences Generaton Agent A. Framewor of the Context-Aware based Dynamc User Preference Profle Constructon Model Ths paper contans two man functons. The frst one s the mage annotaton and sentences generaton functon whch s for the purpose of analyzng the meanng of the mage so as to realze the personalzed nformaton retreval accordng to the users nput mage query. The second one s the dynamc user preference profle constructon functon s for the purpose of collectng the user s local access behavors and browsng behavors consderng both the long term behavor and short term behavor to trac the frequently changng user nterest. The framewor and worflow of the proposed method are shown n Fg.. When one uses the proposed nformaton retreval system, he/she can choose two nds of query: one s mage query, and the other one s text query. If the user nputs an mage query, the mage query wll be processed by the content based mage annotaton agent, whch can analyze and convert the mage nto text annotaton to understand the meanng of the mage and utlze the created annotaton as the eyword to search the related nformaton. The user s profle, whch was constructed based on the doman ontology, combnes local nformaton context aware method, browsng behavor nformaton context aware method to trac the user s daly behavors, then realzes the dynamc updatng of the user preference profle. Furthermore, n order to analyze the content of the mage stored n the mage database, ths system adopted a sentence generaton agent to extract the poston nformaton among the mages nsomuch as to generate the Structured Database Web Spder Agent Source Database Image Database Fg. Framewor of the context-aware based dynamc user preference profle constructon model B. Clent Agent A person often owns more than one ntellgent devce, such as a destop computer, noteboo, smart phone, and tablet. It s not enough to udge an ndvdual s preferences merely on the bass of the personal nformaton stored on a sngle computer; nstead, a user preference profle strategy should ncorporate all the devces of a user n order to determne hs preferences. Hence Clent Agent s used to verfy user denttes by establshng a unon user account and to montor whether a devce s prepared to mplement a user preference profle creaton tas. Every day, the devce tracer runs a smple loop that perodcally (for example every fve mnutes sends heartbeat 25

3 method calls to every devce belongng to user. The heartbeat obtaned from every devce nforms the devce tracer that a devce s at wor (the heartbeat also doubles as a channel for messages. If the devce s at wor, ts Actve Degree (n the form of UserID.AD, where s the ndex of the devce wll be set to +. whch s used to udge the actveness of the devce. As part of the heartbeat, a devce tracer wll tell whether a devce s ready to mplement a user preference profle creaton tas, and f t s, the devce tracer wll tell the User Preference Profle Constructon Agent ( Method for User s Identty In ths paper, two opton methods are adopted to dentfy the users: one s cooes, and the other one s logns. The former one s for current sessons, and the latter s an opton for users who choose to regster wth a ste. If a user has only one computer or he doesn t want to set up a unon account number, then when hs browser clent frst connects to the system, a new user d s created. Ths d s stored n a cooe on the user s computer. When one revsts the same ste from the same computer, he uses the same user d. Ths places no burden on the user at all. However, f the user uses more than one computer, each locaton wll have a separate cooe, hence a separate user profle. Also, f the computer s used by more than one user, and all users share the same local user d, they wll all share the same, naccurate profle. Fnally, f the user clears ther cooes, they wll lose ther profle altogether, and f users have cooes turned off on ther computer, dentfcaton or tracng s not possble. Ths thess adopts an optonal method based on logns. If the user has more than one computer, t s better to create an account va the regstraton page, and logn and logout every tme he vsts the ste. Once the user dentfes hmself durng logn, the dentfcaton s generally accurate, and the user can use the same profle from a varety of physcal locatons. (2Method for Local Data Collecton The metadata ncludes fle name, creatng tme, modfcaton tme and access tme whch can be obtaned by the wn32 API offered by the wndows system. The Local user behavor ncludes duraton nformaton and applcaton program wndows swtchng nformaton whch can be obtaned by usng open source tools User Actvty Logger as n [6], to record the users daly worng behavor on the personal computer. Here, we adopt the Wndows Hoos mechansm and the ernel mode drven method to acqure the nteractve nformaton between users and the applcaton programs. User Actvty Logger employs log fle to record the users nteractve nformaton obtaned by the Wndows Hoos mechansm and ernel mode-drven method. (3 Automatc Image Annotaton Agent In ths paper, we adopt the approach proposed by X and Cho, as n [7] whch generated sentental annotatons for general mages. Ths method s feature weghted accordng to the statstcal dstrbuton of the features when clusterng the mage regon so as to avod the clusterng algorthm beng domnated by wealy relevant features, whch mproves the clusterng accuracy. (4Natural Language Sentences Generaton Agent After obtaned the annotated mages, we use the statstcal generatve model to generate sentences to descrbe mage content based on the annotated mages, as n [8]. Gven a tranng set of mages wth annotatons, we parse the mage gettng poston nformaton, then, we use Machne Learnng to get the probabltes of combnatons between labels and prepostons, obtan the data to text set, create trple <eyword, pre poston, eyword2>. Fnally we generate sentences from the xml report. (5 User Preference Profle Constructon Agent Statc User Preference Profle Constructon Agent In the begnnng, when a user usng hs browser clent to frst connect to our proposed system or creatng an account va the regstraton page, the statstc user preference profle wll be ntated by the weght a ratng scale between 0 and whch was gathered explctly durng sgnup stage. Sum of the weght of all categores wthn a partcular doman wll be. 2 Dynamc Update Subagent Dynamc user profle s used to address users frequently changed nterest whch s based on the context nformaton accordng to the user devce documents (e.g. readng duraton nformaton, fle content and metadata, fle content and metadata, applcaton programmng swtchng nformaton, the tme spent on a document stored n the users devce, swtchng frequency and ther combnaton, whch has a strong postve relatonshp wth users nterest. In ths paper, we use three stages to update the user preference profle dynamcally. Frst, we ntalze the user preference profle accordng to the use s nputted nterred nformaton whch was obtaned from the logn nformaton. Then we update the weght of each term n the user preference profle based on the doman document profle, the context nformaton about the user devce document, the feature words, the users local behavor and browsng behavor. a. Local Behavor based Updatng Two nds of context nformaton---metadata and local user behavor were used to wegh the user devce document and each concept n t. We use ( to calculate the tme weght of a user devce document: CRT ( D = α CurrentTme CreatngTme log( β CurrentTme AccessTme log( γ CurrentTme ModfcatonTme log( ( Here, α, β and γ are three constant parameters, whch was satsfedα + β + γ =, and the value of α, β and γ wll be obtaned by the smulaton.. Local user behavor ncludes duraton nformaton and applcaton program wndows swtchng nformaton. The readng tme refers to the tme spent on browsng the local document durng ths perod, exstng clc and strong of the mouse and the eyboard. The swtchng frequency refers to 26

4 the total tmes of swtchng from the local document wndows to other applcaton program wndows (local or webpage sequentally. When a user browses a document for a perod of tme, f he sequentally swtches to another same wndow for a few tmes, t means that the content of the two wndows s closely correlated. Furthermore, the total number of a wndow has been swtched, whch dsplays the sgnfcance of ths document for ts user. The more documents have been swtched, the more attenton the document can get from the user. Snce the duraton tme s also an mportant affectng factor, we use (2 to defne the swtchng frequency of document d. n SF( d, CN = dsw( d, wndow, t (2 = Where dsw (d, wndow, t ndcates the swtchng frequency of document d drectly swtchng to the wndows n the perod of t, t s a constant that can be decded by the user, and SF (d, CN ndcates the swtch frequency of document d drectly swtchng to all of the other applcaton program wndows. Then we mae use of the swtch frequency to calculate the smlarty of two documents, as n (3. Based on the equaton, we calculate the behavor weght of the document, whch can reflect how mportant the document wll be, as n (4: dsw( d, d, t Sm( d, d = (3 RT ( d, d, t Where RT (d, d, and t ndcates the total readng tme before the d was swtched to d n the perod of t. n BWweght( d = Sm( d, d SF( d, CN (4 n = Where n ndcates the total number of chld nodes, d belongs to the chld nodes. Then we combne ( and (4 to get the local behavor based weght of the document, as n (5: LBW ( d = φ BWweght( d + θ CRT ( d (5 Where ϕ and θ are two constants satsfyng ϕ +θ=, and the value of ϕ and θ wll be obtaned by the smulaton. Snce the actvty of the smart devce can also affect the mportance of the document (That s to say, f the smart devce s rarely used, then the documents stored on ths devce s less mportant than the documents stored on the other frequently used smart devces, fnally, we combne the Actve Degree obtaned by the Clent Agent to refne the weght of each concept n document, as n (5: RLW ( d = Actce Degree LBWt( (6 d t smart devce Here RLW (d s a vector space model that represents the weght of each concept n document. LBWt (d s the weght of the each term that belongs to concept whch s calculated accordng to the context nformaton obtaned n t th smart devce. b. Browsng Behavor based Updatng Frst of all, the stemmed words were extracted from the web ste by performng Words Stemmng, Part of Speech (POS [SNLPG] and Stop Words Removal. Then for each stemmed word we adopt the Semantc Smlarty Matchng Algorthm as n [9] to loo up the most smlar concept between the stemmed words set V (wo, wo 2,, wo n and the correspondng doman ontology. For each stemmed word, f t has no smlar concept or llustraton term n the correspondng doman ontology, then dscard ths word from the feature word set, otherwse choose the most smlar concept from the correspondng doman ontology, and nsert the top M related terms (t,t 2,,t M nto the feature words set. When a user clcs a web page, t means that the user may have nterest n ths web page. However the extent of a user s nterest to ths web page s dependent on the seres of behavors after openng the web pages. We comprehensvely calculate the weght of the feature tem accordng to the term weght ω calculated by the local behavor based updatng subagent, Term Frequency (TF, and browsng behavor, as n (7: (7 Tf ( t, log( + max( Tf ( t, average( RLW ω ( t, = γ δ M Tf ( t, log( t T, WP Mt Tf ( t, Where, s the frequency of feature concept t that appears n the web page, WP s the collecton of all the web pages, M s the number of all the web pages, Mt s Tf ( t, the number of web pages ncludng concept t, max s the frequency of the most frequency for the concept t n the web page. average (RLW s the average weght of concept n all the documents that nclude concept. γ s a constant modfer used to mae the value of ω ( t, <, δ s the nterest degree calculated by the users browsng behavor, as n (8: Treadng 0 f < Thr N total Treadng (8 δ = 0.5 f Thr N total f user save or pr nt or collect Where T readng s the tme used to read the web page p, N total s the total number of the words n the web page, and Thr s the threshold whch can be obtaned by the average readng tme and smulaton. For each web page wth the term t, we use (7 to calculate the weght ω ( t,, and then choose the maxmum one as the weght of the feature word t, as n (9: ω ( t, = max ( ω( t, WP (9 IV. SIMULATION RESULTS We constructed an HDFS Cluster to smulate our 27

5 proposed method (a sngle computer wth an Intel Core4 CPU, 4 GB RAM as NameNode, and 5 computers wth Intel Core2 CPUs and 4 GB RAM as DateNode. We used Java as the programmng language and the Eclpse ntegrated development envronment. In ths study, we analyzed and operated the ontology fles usng an open-source framewor Jena. We use a branchng factor of double wth a depth of four levels n the herarchy. The expermental data set contaned 268 concepts n the herarchy and a total of 2,829 documents ndexed under varous concepts. The ndexed documents were pre-processed and dvded nto two separate sets ncludng a tranng set, and a test set. The tranng set was utlzed for the representaton of the personalzed ontology profle, and the test set was utlzed as the document collecton for searchng. The tranng set was conssted of 54 documents used for the one-tme learnng of the personalzed ontology profle. The concept terms and correspondng term weghts were computed usng the formulas descrbed n secton 4.5. A total of 35 documents were ncluded n the test set (ncludng 50 documents and 65 pctures, whch were used as the document collecton for performng our search experments. The user data s collected and analyzed usng the above proposed method on a daly bass for one month. The test set documents were orgnally ndexed under a specfc doman, and all of ts concepts were treated as relevance documents for that doman whereas all other test set documents were treated as non-relevance. The entre process conssted of determnng profle stablty frst and then buldng 2 profles: an ntal profle, a profle bult after 20 days of browsng after reachng stablty (profle for several category combned together, as shown n Fg. 2 and Fg.3. In the ntal, snce the user preference profle s not perfect, t cannot reflect the user s ntenton very accurately. As a result, f the user s nput query term has ambguty, some rrelevant webstes may be retreved, as shown n Fg. 4(The red one s the relevance returned results, the green one s the rrelevance returned results. We can see that the returned results nclude the anmal doman and the computer doman, whch cannot reflect the user s daly nterest. Fg.2 The ntal user preference profle Fg.3 The user preference profle for about 20 days 28

6 We use the evaluaton method the precson averages at standard recall levels (SPR to evaluate the performance of the proposed system, and the expermental SPR results are plotted, as shown n Fg. 6. The SPR curves show that my retreval system based on the user preference profle s the best, followed by the Ontology model as n [0], TREC model, the web model, and the Category model. Fg.4 The returned searched results for two terms text query when the user preference s not perfect. But along wth the tme, more local document readng behavor and browsng behavor were collected and analyzed, so that the user s long term preference profles were tendng towards stablty. Therefore, even f the text query s somewhat ambguous, the searched nformaton s closer to the user s real ntenton, and much more relevant nformaton wll be fed bac, as shown n Fg.5. As can be seen that the returned results show hgh nterest of the user on the user s nterest doman. Fg.5 When the user preference s almost perfect. Fg.6 TheSPR results of the proposed method versus other 4 approaches. The overall results show that our approach s better than the other three approaches n terms of the precson averages at standard recall levels. V. CONCLUSION Ths paper nvestgated the realzaton of personalzed nformaton retreval for text nformaton and mage nformaton by constructng context nformaton based dynamc user preference profle, and was smulated usng the dataset of a branchng factor wth a depth of four levels n the herarchy of the Open Drectory. Frst, we proposed a new horzon for the personalzed nformaton retreval whch uses mage nput nstead of text query to express the user s ntenton. If the user cannot use text query to express ther ntenton more clearly, t can use mage query nstead of text query. In order to realze ths purpose, ths thess proposes a novel system whch generates the sentental annotatons for general mages. Then the proposed system constructed a dynamc user preference profle based on the doman ontology and context nformaton n local devce document and browsed webste pages, whch can comprehensvely trac the users long-term behavors and short-term behavors Smulaton shows that the proposed algorthm can ncrease the precson and flexblty as well as reduce the calculaton amount compared wth other nowledge structured based user preference profle constructon method and context-aware based user preference profle constructon method. 29

7 REFERENCES [] [2] K. Hoash, K. Matsumoto, N. Inoue, and Hashmoto. Document Flterng method usng non-relevant nformaton profle, In: Proceedngs of the 23rd Annual Internatonal ACM SIGIR Conference on Research and Development n Informaton Retreval, pp.76-83, [3] H. Wdyantoro, R. Ioerger, and J. Yen, Learnng User Interest Dynamcs wth Three-Descrptor Representaton, Journal of the Amercan Socety of Informaton Scence and Technology (JASIST, vol.52, no.352, pp , 200. [4] H. Km and P. Chan, Learnng mplct user nterest herarchy for context n personalzaton, In: Proceedngs of IUI 03, pp.0-08, [5] K. Sugyama, K. Hatano, and M. Yoshawa, Adaptve web search based on user profle constructed wthout any effort from users, In: Proceedngs 3 th Internatonal Conference on World Wde Web, pp , [6] S. Chernov, G. Demartn, E. Herder, et al, Evaluatng personal nformaton management usng an actvty logs enrched destop dataset, In: Florence Italy Proceedngs of 3rd Personal Informaton Management Worshop, [7] S. X and Y. Cho, Image Capton Automatc Generaton Method Based on Weghted Feature, 3th Internatonal Conference on Control, Automaton and Systems, 203. [8] S. X, Q. Gao, and Y. Cho, Mappng Method Between Image and Natural Sentence, th Internatonal Symposum on Robotcs, October 24~26,(ISR 203, Seoul, Korea, 203. [9] Q. Gao and Y. Cho, A Mult-Agent Improved Semantc Smlarty Matchng Algorthm Based on Ontology Tree, Journal of Insttute of Control, Robotcs and Systems(202, vol. 8, no., pp ,202. [0] X. Tao, Y. L, and N. Zhong, A Personalzed Ontology Model for Web Informaton Gatherng, Knowledge and Data Engneerng, vol. 23, pp , 20. Frst J. Km ( receved a Doctor of Computer Scence from Danoo Unversty n Seoul, Korea n 2004, her M.B.A degree n the Informaton Management from Danoo Unversty n 994, her B.S degree n Mathematcs from Ewha Womans Unversty n 978. She s a professor n the Department of Computer Software n Seol Unversty n Korea. Her man wors nclude Relevance of the Cyclomatc Complexty Threshold for the Web Programmng, Journal of the Korea Socety of Computer and Informaton, vol.7, No.6, pp.53-6, 202; Change Detecton and Management Scheme of OWL Documents, Journal of Dgtal Contents Socety, vol.3, No., pp.43-52, 202; A Query Processng Method for Herarchcal Structured e-learnng System", Journal of the Korea Socety of Computer and Informaton, vol.6, No.3, pp.-3, 20; Her research nterests are Web Engneerng, Database, Qualty Management, Informaton Retreval and Ubqutous system. She receved the certfcate of Professonal Engneer from Korea n 997. Second Q. Gao ( s a PhD. canddate n the Department of Computer Scence at the Unversty of Suwon n Suwon cty, Gyeongg-do of Korea, she receved her M.S. degree n computer applcaton technology from Shandong Unversty n Jnan cty, Shandong provnce of Chna n 2008, and her B.S. degree n computer scence and technology from Shandong Unversty of Scence and Technology n Taan cty, Shandong Provnce of Chna n 200. She s a lecturer n the School of Informatcs at Qlu Unversty of Technology n Chna, was an assstant professor at the Unversty of Suwon. Her man wors nclude A Mult-Agent Personalzed Query Refnement Approach for Academc Paper Retreval n Bg Data Envronment, Journal of Advanced Computatonal Intellgence and Intellgent Informatcs, vol.6, no.7, pp , 202; A Mult-Agent Improved Semantc Smlarty Matchng Algorthm Based on Ontology Tree, Journal of Insttute of Control, Robotcs and Systems, vol. 8, no., pp ,202; A Dynamc Ontology-based Mult-Agent Context-Awareness User Profle Constructon Method for Personalzed Informaton Retreval", Internatonal Journal of Fuzzy Logc and Intellgent Systems, vol.2, No.4, pp , 202. Her research nterests nclude mult-agent system, artfcal ntellgence, nformaton retreval, and ubqutous system. Thrd Y. Cho ( s receved a post-doc. degree at the Unversty of Massachusetts n USA n 2000, her Ph.D. degree n Computer Scence from Korea Unversty n 994, her B.S. degree n Computer Scence from Korea Unversty n 988, and her M.S. degree n Computer Scence from Korea Unversty n 990. She s an assocate professor n the Department of Computer Scence at the Unversty of Suwon n Korea. Once wored at Samsung electroncs company at 995. Her man wors nclude A Mult-Agent Personalzed Query Refnement Approach for Academc Paper Retreval n Bg Data Envronment, Journal of Advanced Computatonal Intellgence and Intellgent Informatcs, vol.6, no.7, pp , 202; A Mult-Agent Improved Semantc Smlarty Matchng Algorthm Based on Ontology Tree, Journal of Insttute of Control, Robotcs and Systems, vol. 8, no., pp ,202; A Dynamc Ontology-based Mult-Agent Context-Awareness User Profle Constructon Method for Personalzed Informaton Retreval", Internatonal Journal of Fuzzy Logc and Intellgent Systems, vol.2, No.4, pp , 202. Her research nterests nclude mult-agent system, artfcal ntellgence, nformaton retreval, and ubqutous system as well as a supervsor of Q.Gao. She has receved many awards from Internatonal Socety le Control Robot and System Socety, Korea Intellgent System Socety etc. Also, she got award from the Korea government as a Korea the man of mert partcularly n Informatzaton area n