Handling uncertainty in semantic information retrieval process

Transcription

1 Handling uncerainy in semanic informaion rerieval process Chkiwa Mounira 1, Jedidi Anis 1 and Faiez Gargouri 1 1 Mulimedia, InfoRmaion sysems and Advanced Compuing Laboraory Sfax Universiy, Tunisia m.chkiwa@gmail.com, jedidianis@gmail.com, faiez.gargouri@isimsf.rnu.n Absrac. This posiion paper proposes a collaboraion mehod beween Semanic Web and Fuzzy Logic aiming o handle uncerainy in he informaion rerieval process in order o cover more relevan iems in resul of search process. The collaboraion mehod employs OWL onology in query enhancemen, RDF in annoaion process and fuzzy rules in ranking enhancemen. 1 Inroducion In he informaion rerieval process, here are reurned documens which are relevan o he query bu hey focus in addiion of query main ineres on ohers addiional opics. To deal wih his imprecision we propose o valorize in he ranking process relevan documens which deal mainly wih query hemes. Anoher source of imprecision in he search process is he user queries; we propose o enhance i in order o come near he inenion of he user. This paper is organized as follows: in he nex secion we presen our proposiion o enhance he query background expression hen we explain how Semanic Web and Fuzzy Logic collaborae o enhance ranking process. In Secion 3, we presen some relaed works and Secion 4 concludes he paper. 2 Handling uncerainy by semanic/fuzzy collaboraion 2.1 The semanic/fuzzy query enhancemen A main cause of uncerainy in he informaion rerieval process comes from he user s queries. In order o reurn more relevan resuls, he informaion rerieval sysem has o indenify he user s inenion behind he query. To do i, we propose o enhance user queries by adding semanically relaed erms. In his purpose, we use he Web Onology Language OWL and hen we employ some fuzzy rules in order o weigh up he query erms imporance. In Figure 1, we presen our semanic/fuzzy query enhancemen.

2 Using OWL o Add he neares erms semanically Fuzzy rules query Q n erms query Q n + n/4 erms Fig. 1. Semanic/fuzzy query enhancemen Weighed query Q The Semanic query enhancemen passes hrough he enrichmen of he query by new erms synacically differen bu semanically near; he new added erms are no picked o derive he query meaning bu o find erms expressing more he user inenion. Several works as [1-3] are proposed o express he semanic similariy beween onology conceps. Afer eliminaing empy erms from he query, we can reuse he algorihm presened in [3] o find he semanically neares erm o each query erm using OWL onology. The number of added erms mus no be consan; i can derive he query meaning if i is large or useless if i is few. So we decide ha he number of added erms be proporional o he query lengh. Hence, we propose o add only n/4 erms having he highes similariy o query erms. Also, we propose ha he informaion rerieval sysem is ineracive and allows users o highligh cerain query erms in order o reflec heir imporance. Finally, o weigh he query erms, we apply some fuzzy rules; hose rules define he prioriy of weighing: If a query erm is added from he onology hen i will has low weigh prioriy. If a query erm is no bold, hen i will has a medium weigh prioriy. If a query erm is bold hen i will has a high weigh prioriy. 2.2 The semanic/fuzzy ranking enhancemen The semanic/fuzzy ranking enhancemen aims o manage uncerainy abou he oupu of classic querying process and o valorize documens focusing specially in he same user query ineress. I aims principally o limi he number of relevan documens dealing wih several opics. The semanic/fuzzy ranking enhancemen passes hrough wo fundamenal conceps: he mea-documen which allows annoaing semanically he collecion of documens and he hemes clouds which enhance he ranking process based on Fuzzy Logic. The mea-documen is inroduced in [4] and i is able o annoae semanically mulimedia objecs as well as web documens. A mea-documen uses RDF meadaa o annoae web resources in a way ha ensures is reusabiliy. The querying process maches he user query wih he mea-documens in order o idenify he score relevance of he resources o he query. We define he heme cloud as groups of weighed erms concerning a given heme. Simply, we collec poenial erms represening a given heme o consruc a heme cloud. The erms weighs express he abiliy of each erm o represen he heme. Afer running a usual querying process maching he query and he mea-documens, we ge he relevance score for each annoaed resource or documen. A his poin, he heme clouds are used o enhance ranking resuls in he benefi of relevan documens focusing

3 mainly on query ineress. The Figure 2 gives a simple presenaion of he srucure of he semanic/fuzzy ranking enhancemen: Fig. 2. The semanic/fuzzy ranking enhancemen To run he ranking enhancemen, firs, we esablish he mea-documen/heme weighed links W DT. W DT expresses he poenial hemes menioned by he meadocumen. To assign a weigh W DT o a mea-documen/heme link, we simply sum he weighs of heme erms exising in he mea-documen. Then we esablish query/hemes weighed links which express he abiliy of each heme o represen he query. To assign a weigh o a query/heme link, we use he classic similariy measure beween wo weighed erms vecors: W QT = sim Q,Ti = W qj W ij (W qj ) 2 (W ij ) 2 (1) The nex sep of ranking enhancemen is o calculae for each documen his heme similariy wih he query in order o increase or decrease is relevance score in erms of he value of he heme similariy. The heme similariy TS is calculaed as follow: TS (Q, D) = k i=1 W QTi W DTi (2) The main goal of he ranking enhancemen is o increase relevance of documens focusing on he same query hemes and o decrease relevance of documen dealing wih differen hemes vis-à-vis he query. The TS (Q, D) value is opimal when is value is minimal; his means ha he query and he documen are focusing on he same hemes wih approached values. Conrariwise, if he TS is high, his means ha he documen deals wih oher hemes in addiion o he query hemes. Finally, he increase or he decrease Rae R affeced o a documen Relevance Score RS is based on he following fuzzy rules: If RS is high or medium and TS is low hen R is high If RS is low and TS is low hen R is medium If RS is low or medium and TS is high hen R is negaive

4 3 Relaed work Several approaches considering boh uncerainy and he Semanic Web have been proposed in he informaion rerieval issue. [5, 6] propose o fuzzify in differen ways RDF riples, likewise [7, 8] propose o fuzzify OWL onology saemens. A common poin in hose works is he use of formal ways o express he assignmen of a ruh degree o RDF riples or OWL axioms. In our proposiion, numerical membership values idenificaion is done in background using mahemaical deducion wihou he need of formal expressions (e.g. weigh prioriy of a query erm). Some oher works are near our proposiion: [9] shows ha i is useful o express a fuzzy proximiy values beween erms of a query. By using a fuzzy se of rules [10] shows he usabiliy of a ranking sysem based on fuzzy inference. In he query enhancemen issue, many works are proposed [11-12]; our mehod is characerized by is simpliciy and flexibiliy. 4 Conclusion In his paper we sudied wo ineroperable axes in he informaion rerieval process: he Semanic Web and he Fuzzy Logic. We propose o enhance query background expression and also o enhance ranking process using fuzzy rules. Given ha Numerical inpus of fuzzy rules are deduced from he mea-documens characerisics, i remains o idenify in he shor run, he numerical limis o fuzzy ses on which we will apply he fuzzy rules se. Equally, we plan o exend he curren proposiion and o invesigae he concep of user profile in order o cover more relevan resul documen. 5 References 1. B. Y. Liang, J. Tang, J. and Z. Li. Semanic Similariy Based Onology Cache. Heidelberg: Springer-Verlag, 2006, pp Z. Yang. Semanic similariy measure maching beween onology conceps based on heurisic rules. Journal of compuer applicaions, vol.27, no.12, pp , Xiao Min; Zhong Luo; Xiong Qianxing. Semanic Similariy beween Conceps Based on OWL Onologies. 2 nd Inernaional Workshop on Knowledge Discovery and Daa Mining, WKDD vol., no., pp.749,752, Jan Jedidi A. «modélisaion générique de documens mulimédia par des méadonnées : mécanismes d annoaion e d inerrogaion» Thesis of «Universié TOULOUSE III Paul Sabaier», France. July Simou, N., Soilos, G., Tzouvaras, V., Samou, G., and Kollias,S. Soring and Querying Fuzzy Knowledge in he Semanic Web. In Proceedings of he Fourh Inernaional Wor k- shop on Uncerainy Reasoning for he Semanic Web, Karlsruhe, Germany,, Mazzieri, M., and Dragoni, A. F.. A fuzzy semanics for Semanic Web languages. In ISWC-URSW, pages

5 7. Calegari, S. and Ciucci, D. Fuzzy Onology, Fuzzy Descripion Logics and Fuzzy-OWL. WILF '07 Proceedings of he 7h inernaional workshop on Fuzzy Logic and Applicaions: Applicaions of Fuzzy Ses Theory Pages Bobillo F., Sraccia U. Fuzzy Onology Represenaion using OWL 2. Inernaional Journal of Approximae Reasoning 52(7): , Beigbeder, M. and Mercier, A. Applicaion de la logique floue à un modèle de recherche d'informaion basé sur la proximié. In Aces 12 h renconres francophones sur la Logique Floue e ses Applicaions. Nanes, France, pp Rubens, N. The applicaion of Fuzzy Logic o he consrucion of he ranking funcion of informaion rerieval sysems. Compuer Modeling and New Technologies, Vol.10, No.1, Neda A., Laifur K. and Bhavani T. Opmized onology-driven query expansion using map-reduce framework o faciliae federaed queries. Compu. Sys. Sci. Eng. 27(2) (2012) 12. Min S, Il-Yeol S, Xiaohua H, Rober B. Allen. Inegraion of associaion rules and onologies for semanic query expansion. Daa Knowl. Eng. 63(1): (2007).