A Method of Query Expansion Based on Event Ontology

Transcription

1 A Method of Query Expanson Based on Event Ontology Zhaoman Zhong, Cunhua L, Yan Guan, Zongtan Lu A Method of Query Expanson Based on Event Ontology 1 Zhaoman Zhong, 1 Cunhua L, 1 Yan Guan, 2 Zongtan Lu, 1 School of Computer Engneerng, Huaha Insttute of Technology, Lanyungang , Chna *2 School of Computer, Shangha Unversty, Shangha , Chna Abstract The human have suffered and have been sufferng all knds of emergency, and gettng event nformaton has become the key component for the users. Event ontology s a shared, objectve and formal specfcaton of an event class system model, and t represents knowledge wth a hgher granularty. The exstng methods of query expanson based on ontology do not dstngush the functons of dfferent event elements, and apply the same strategy to expand query terms. Amng at a lot of event-orented query requrements, we propose a method of query expanson based on event ontology (denoted by EO-QE). The paper emphatcally dscusses the concept of event four-tuple and the dfferent query expanson strategy based on dfferent event elements. The results show that EO-QE offers more effectve performance for retrevng events, compared wth pseudo relevance feedbackbased mechansm (denoted by PRF-QE) and local context analyss mechansm (denoted by LCA-QE). Keywords: Informaton retreval; query expanson; event ontology; event four-tuple 1. Introducton In the nformaton retreval feld, query expanson s to append related terms to the ntal query, and to form the new and more accurate query, whch can make up the defects of defcent query nformaton to a certan extent and also mprove the recall and the precson of retreval. The methods of query expanson approxmately can be dvded nto two categores: semantc dctonary-based and corpus-based. The semantc dctonary-based method selects query terms n terms of exstng semantc knowledge base such as dctonary, ontology, etc. Ontology s a modelng tool of concept n semantc and knowledge level, whch has reasonable concept structure and supports logc reasonng. Fusng ontology nto tradtonal nformaton retreval helps to not only process document nformaton n semantc level, but reason user s query content based on ontology [1,2]. Voorhees [3] proposed the method of ontology-based query expanson as early as n 1994, whch used the concept n ontology to expand query content, and concluded that the most effectve way s to expand query content usng synonymy concept and parent-chld relaton n ontology. Lao [4] descrbed the ontology n a formal way and ntroduced one of ts applcaton: an nformaton retreval method based on ontology n 2000, whch overcomes the nsuffcency of nformaton overloadng and mssng caused by the keyword-based retreval methods. In 2003, Navgl et al. [5] ntroduced how to use sense nformaton (and ontologes n general) to expand the query, and the expermental result shows that expandng wth synonyms or hyperonyms has a lmted effect on web nformaton retreval performance, whle other types of semantc nformaton dervable from an ontology are much more effectve at mprovng search results. Mak et al. [6] proposed the method of query expanson based on ontology structure, whch expanded query content usng structure dagram of ontology. Song et al. [7] ntroduced ontology-based nformaton retreval model. Ontology s generated usng a knd of basc descrpton logc, whch s a sutable tradeoff between expressvty of knowledge and complexty of reasonng problems. The logcal vews of documents and user s nformaton needs, generated n terms of these semantc ndex terms, can represent documents and user s nformaton needs well, so the performance of nformaton retreval can be mproved effectvely when sutable retreval functon s chosen. In addton, cased-based nformaton retreval [8, 9] also uses the classes, herarchcal structure and part-of relaton of doman concepts n ontology. Journal of Convergence Informaton Technology(JCIT) Volume7, Number9, May 2012 do:14156/jct.vol7.ssue

2 A Method of Query Expanson Based on Event Ontology Zhaoman Zhong, Cunhua L, Yan Guan, Zongtan Lu In recent years, some researchers have ntroduced the event dea nto query expanson based on the ontology or the event frame. Ln et al. [10] presented a retreval method called event ontology n 2005, n whch the top concepts are event elements such as locatons, and tme, etc. At the tme of retreval, query content can be expanded usng event elements. Hsu et al. [11] constructed an event frame as a mode of knowledge representaton. For queryng object A, the correlatve actons about A wll be expanded accordng to the event frame. For example, when query s auto, the events such as parkng and mantan, wll be assocated. Han [12] proposed a character ontology model based on events, n whch a character wll relate to some specal events, and events are attrbutes of characters. Event ontology s a shared, objectve and formal specfcaton of an event class system model. In comparson wth the conventonal ontology, the event ontology represents knowledge wth a hgher granularty. But the exstng methods of query expanson based on ontology can not analyze the functons of user s query contents, and can not use the event-orented technque to expand query contents. Hence, some problems about event-orented nformaton retreval can not be solved well. In ths paper, we propose a method of query expanson based on event ontology (denoted by EO-QE), and pay attenton to how to expand query terms, how to wegh query terms and how to compute the smlarty between query terms and documents. The remander of ths paper s organzed as follows. Secton 2 presents event ontology model. In secton 3, the method of query expanson based on event ontology s ntroduced. In secton 4, we mplement experments and evaluate the proposed algorthm. Fnally, we gve conclusons and dscuss future works. 2. Event Ontology Model An event [13] refers to one thng happenng at a specfc tme and locaton, nvolvng a number of actors, and showng some acton characterstcs. We use e to formally denote an event, whch can be defned as a four-tuple: e A, S, O, T. The elements of an event four-tuple are called event elements, whch represent acton ( A ), object ( O ), tme (T ) and locaton ( L ) respectvely. A (acton) refers to the trgger word for an event. In many cases, an event has some dfferent trgger words. O (object) refers to the set of object partcpatng n the event. T (tme) refers to the occurrence tme of an event and L (locaton) means the occurrence locaton of an event. For a sentence The 2008 Schuan earthquake occurred at 14:28:01 CST on Monday, May 12, 2008 n Schuan provnce of Chna, A s earthquake, T s 14:28:01 CST on Monday, May 12, 2008 and L s Schuan. An event class ( EC ) refers to the set of events whch have the same characterstcs, denoted by EC { E, A, O, T, L}, where E s the set of events, called the extenson of the event class. A, O, T, L are the ntenson of the event class, whch are the set of the same characterstcs of certan element for each event. An event nstance s the actual occurrence of an event, and an event class s a set of event nstances correspondng to a type or feature of events. An event ontology [14] s a shared, objectve, and formal specfcaton of an event class system model. Formally, we use EO to denote an event ontology, whch can be defned as a quadruple: EO ECS, R, W, Rules. (1) ECS { EC, EC,, EC } s the set of event classes; 1 2 n (2) R { r r s the relaton of EC, EC, r { r { R, R, R, R, R }}; j H I CE F C (3) W { w j w s the assocatve strength of j EC, EC j } ; (4) Rules s the set of rules relatng to a gven event class, descrbed by logc languages. where EC denotes an event class. R s the set of event class relatons, ncludng classfed relatons ( R ), component relatons ( R ), cause-effect relatons ( R ), follow relatons ( R ) and H I CE F concurrence relatons ( R ). w s an event class nfluence factor from C j EC to EC, whch refers j 365

3 A Method of Query Expanson Based on Event Ontology Zhaoman Zhong, Cunhua L, Yan Guan, Zongtan Lu to the probablty that after to1. EC occurred, 3. Query Expanson Based on Event Ontology EC causes the occurrence of 3.1. Steps of Query Expanson Based on Event Ontology EC, rangng from 0 j Steps of query expanson based on event ontology are as follows: Step 1. Inputtng event four elements (event trgger, tme, locaton and object) n apponted query box; Step 2. Normalzng tme element by <year, month, day>; Step 3. Expandng locaton element accordng to locaton ontology; Step 4. Identfyng whch event ontology subordnated by query events; Step 5. Expandng query terms accordng to specalzed event ontology; Step 6. Computng the smlarty between query contents and the document, descendng order of documents and outputtng them n terms of smlarty. Next, we dscuss some key technologes n the above steps Expandng Locaton Element Based on Locaton Ontology Interest n geographc nformaton, and n partcular nformaton related to locatons and locaton names, has grown sgnfcantly over the past few years. The powerful smplcty of applcatons such as Google Earth fueled a wealth of geo-related actvtes and needs for web users. Also, many ntatves are ongong to buld natonal and global spatal data nfrastructures to enable the share, use and reuse of geographc nformaton. Searchng and retreval of place related nformaton s central to these actvtes. An essental component of search engnes that supports the effectve retreval of geographcally referenced resources s locaton ontology. We construct a locaton ontology for Lanyungang cty, contanng man termnology and structure of geographc space as well as records of enttes n Lanyungang. Fgure 1 shows the fragment of the doman locaton ontology of Lanyungang. 366

4 A Method of Query Expanson Based on Event Ontology Zhaoman Zhong, Cunhua L, Yan Guan, Zongtan Lu Fgure 1. The fragment of the doman locaton ontology of Lanyungang If someone wants to get the nformaton happened n Lanyungang, the nformaton happened n some places such as Xnpu, Hazhou, Ganyu wll be returned accordng to Lanyungang locaton ontology. Therefore, the nformaton obtaned wll be more all-sded Identfyng Whch Event Ontology Subordnated by Query Events Steps of dentfyng whch event ontology subordnated by query events are as follows: Step 1. Rankng event classes for doman event ontology, supportng that there are n event ontology, denotng by, descendng order of event classes n and the obtaned event class set s. Step 2. Comparng event trggers nputted by users wth event class set of doman event ontology, memorzng the seral number that event trggers exstng n, f there s no event trggers n, then s maxmum machne number. Step 3. Fnally, selectng the event ontology that has the smallest as the expanded event ontology Expandng Query Terms Accordng to Event Ontology 367

5 A Method of Query Expanson Based on Event Ontology Zhaoman Zhong, Cunhua L, Yan Guan, Zongtan Lu Steps of expandng query terms based on specalzed event ontology are as follows: Step 1. Supposng that the number of expanded terms s s and the number of expanded terms that has selected s, f, then stop expandng. Step 2. Selectng event nstances of the event ontology to expand, f query terms have ncluded the selected terms, then gnore the selected terms. Step 3. Selectng event class elements of the event ontology to expand, f, then stop expandng; f query terms have ncluded the selected terms, then gnore the selected terms. Step 4. Expandng query events accordng to classfed relatons between event classes, f, then stop expandng. Step 5. Expandng query events accordng to assocatve strength between event classes, f, then stop expandng Computng The Smlarty Between Query Terms and Document Weghng query terms. We use the method proposed n lterature [14] to wegh query terms. The weghts of query terms are calculated by formula (1): wt / s. (1) In formula (1), s the weght of th query terms and s the number of query terms. Computng the smlarty between query terms and documents. We take the frequences of feature terms n document as ther weghts. The smlarty between the vector of query terms and the vector of document s computed accordng to the followng formula: S( Q, d ) W ( t, Q) W ( t, d ) Q d 2 W ( t, Q) 1 1 t Q De W ( t, d ) 2 (2) where t Q d s the same term of Q and d. Q and d are the sze of Q and d respectvely. 4. Experments and Evaluaton For the purpose of verfyng the valdty of event-orented query expanson, we do not take nto account the lnks between pages, and do not use some heurstc nformaton such as the ttle of the text, the frst paragraph and the frst sentence. For the method proposed n ths paper, we mplemented the experments to compare the retreval performance wth pseudo relevance feedback-based query expanson (denoted by PRF- QE) and local context analyss-based query expanson (denoted by LCA-QE) The Expermental Data and Evaluaton Methods There are some Chnese nformaton retreval evaluaton corpus such as the 863 evaluaton data of Chnese nformaton retreval, SEWM and CIRB03 But they all orent unversal search evaluaton, and only a small part of them relates to event retreval. Therefore, we focus on emergences, manly ncludng fve types: earthquake, fre, food posonng, traffc accdent, and terrorst attack, to collect corpus documents have been collected usng Google after nputtng some query keywords, and 2435 documents have been downloaded usng crawler from apponted webstes. After deletng some repeated documents through comparng ther ttles, the fnal test corpus ncludes 4011 documents. Smlarly to the manner of usng search engne, we set some key words for each query topc. We set 10 query topcs centerng on emergences and use P@10 and P@20 as evaluaton 368

6 A Method of Query Expanson Based on Event Ontology Zhaoman Zhong, Cunhua L, Yan Guan, Zongtan Lu ndexes. s commonly used by some retreval evaluaton conferences. It s an anthropomorphc ndex and smulates the results returned by unversal search engnes. We apply the poolng technology to determne the standard answer for each query topc. The steps of determnng the standard answer for each topc are: (1) gettng unon set S by selectng top-n documents returned by three expanson methods, and (2) people are responsble for selectng related documents as the standard answer for a query topc. Table 1 lsts 10 query topcs n the experment. Table 1. Ten query topcs D I Query Topc Wenchuan reconstructon D 3 earthquake rescue 4 5 terrorst attack n Xnjang 7 Hebe traffc accdent I 2 Query Topc the death of Wenchuan earthquake the 2008 Wenchuan earthquake 6 terrorst attack n Inda the death of Hebe traffc accdent 9 Karamay 12.8 fre 0 Chna fre 4.2. The Comparson of Retreval Performance for EO-QE, PRF-Roccho and PRF- LCA We mplemented the experment on dfferent number of expandng terms from 0 to 4 Selectng best average result of 10 query topcs for three expanson methods, Table 2 lsts the comparatve results. Table 2. Comparson of best average results for three expanson methods Expanson Method From Table 2, EO-EQ s the best and PRF-EQ s the worst for three expanson methods. For the evaluaton ndex P@10 and P@20, EO-EQ s ncreased by 22 and 19 compared wth PRF-EQ respectvely. Furthermore, we have gotten a good search result n the case of less number of expandng terms for the query topc related to events. The recommended number of expandng terms s 10 to 26 for PRF-EQ and PRF-LCA, and t s about 10 for EO-EQ, whch s dfferent compared wth the recommended number of unversal nformaton retreval. The experment n lterature [15] shows that the retreval performance s slghtly worse wth 30 expandng terms than wth 70 expandng terms. Lterature [16] recommends the number of expandng terms s 30 to 10 Ths shows that the number of expandng terms s dfferent for dfferent query topcs n order to get better retreval performance. 4. Conclusons and Future Works Number of Expandng Terms or Events PRF-EQ 21 LCA-EQ 18 EO-EQ

7 A Method of Query Expanson Based on Event Ontology Zhaoman Zhong, Cunhua L, Yan Guan, Zongtan Lu We propose an approach to event ontology-based query expanson n ths paper. Comparng wth the exstng query expanson methods such as PRF-EQ and LCA-EQ, the expermental results have proved that event ontology-based query expanson has a lot of advantages n practcal applcatons. But some problems should be further dscussed. Frst, t s dffcult to construct reasonable event ontology for specalzed doman. Second, the method of dentfyng four event elements s somewhat rough, and the functon of each event element should be further researched. 5. Acknowledgements Ths research (paper) s supported by the Natonal Natural Scence Foundaton of Chna (No ), and the Scence and Technology Foundaton of Lanyungang (CG1121). 6. References [1] X. H. Zhu, Q. H. Cao, F. F. Su, "A Chnese Intellgent Queston Answerng System Based on Doman Ontology and Sentence Templates", JDCTA: Internatonal Journal of Dgtal Content Technology and ts Applcatons, Vol. 5, No. 11, pp , [2] B. H. Qang, G. Y. Ca, Y. M. Wen, C. M. Wu, C. H. Tang, "Identfcaton and Classfcaton of Deep Web Query Interfaces va Ontology", IJACT: Internatonal Journal of Advancements n Computng Technology, Vol. 3, No. 9, pp. 33 ~ 41, [3] E. Voorhees, Query expanson usng lexcal semantc relatons, In Proceedngs of the 17th Annual Internatonal ACM SIGIR Conference on Research and Development n nformaton retreval, Dubln,Ireland, pp , [4] M. H. Lao, Ontology and nformaton retreval, Computer Engneerng, Vol. 26, No. 2, pp , 200 [5] R. Navgl, P. Velard, An analyss of ontology-based query expanson strateges, In Workshop on Adaptve Text Extracton and Mnng (ATEM 2003), n the 14th European Conference on Machne Learnng (ECML 2003), pp , [6] W. Mak,L. McKnley, A. Thompson, Semantc dstance norms computed from an electronc dctonary (wordnet), Behavor Research Methods, Instruments, & Computers, Vol. 36, No. 3, pp , [7] J. F. Song, W. M. Zhang, W. D. Xao, J. Y. Tang Research on ontology based nformaton retreval model, Journal of NanJng Unversty (Nature Scence), Vol. 41, No. 2, pp , [8] A. Gomez-Perez, Evaluaton of taxonomc knowledge n ontologes and knowledge bases, In Proceedngs of KAW 99, Banff, Alberta, Canada, [9] M. Talls, J. Km, Y. Gl, User studes of knowledge acquston tools: methodology and lessons learned, J. Expt. Theor. Artf. Intell., Vol. 13, No. 4, pp , [10] H. F. Ln, and J. M. Lang, Event-based ontology desgn for retrevng dgtal archves on human relgous self-help consultng, In Proceedngs of the 2005 IEEE Internatonal Conference on e- Technology, e-commerce and e-servce, Hong Kong, Chna, pp , [11] W. L. Hsu, S. H. Wu, Y. S. Chen, Event dentfcaton based on the nformaton map- INFOMAP, In Proceedngs of the IEEE Internatonal Conference on Systems, Man, and Cybernetcs, Tucson, Arzona, USA, pp , [12] Y. Han, Reconstructon of people nformaton based on an event ontology, In Proceedngs of the 2007 IEEE Internatonal Conference on Natural Language Processng and Knowledge Engneerng, Bejng, Chna, pp , [13] Z. M. Zhong, Z. T. Lu, W. Zhou, J. F. Fu, The model of event relaton representaton, Journal of Chnese Informaton Process, Vol. 23, No. 6, pp , [14] Z. M. Zhong, Z. T. Lu, C. H. L, Y. Guan, A method of rankng event class for event ontology, JCIT: Journal of Convergence Informaton Technology, Vol. 6, No. 9, pp , [15] J. Xu, B. W. Croft, Improvng the effectveness of nformatonal retreval wth local context analyss, ACM Transactons on Informaton Systems, Vol. 18, No. 1, pp ,

8 A Method of Query Expanson Based on Event Ontology Zhaoman Zhong, Cunhua L, Yan Guan, Zongtan Lu [16] G. D. Dng, S. Ba, B. Wang, Local co-occurrence based query expanson for nformaton retreval, Journal of Chnese Informaton Processng, Vol. 20, No. 3, pp ,