A Novel Ontology Analysis Tool

Transcription

1 Appl. Math. If. Sci. 8, No. 1, (2014) 255 Applied Mathematics & Iformatio Scieces A Iteratioal Joural A Novel Otology Aalysis Tool Zhixiao Wag, Shixiog Xia ad Qiag Niu School of Computer Sciece ad Techology, Chia Uiversity of Miig ad Techology, Xu Zhou, , Chia Received: 12 Ju. 2013, Revised: 18 Oct. 2013, Accepted: 20 Oct Published olie: 1 Ja Abstract: With the developmet of sematic web, more ad more otologies are developed for various purposes. I order to evaluate these otologies, there have emerged umerous otology aalysis approaches. This paper itroduces the classical field theory to otology area, ad puts forward a sematic field model to aalyze otology characteristics from the perspective of cocept iteractio ad hierarchical clusterig. I sematic field, the equipotetial lies preset a atural estig structure. The equipotetial lies distributio correspods with the sematics distributio resultig from the iteractio ad associatio of otology cocepts. Based o equipotetial lie distributio, we ca aalyze cocept hierarchical clusterig characteristics. Experimets showed that the sematic field does well i otology cocept hierarchical clusterig aalysis. The sematic field model has good scalability, ad clusterig executio time follows a approximate liear relatioship with cocept scale. Keywords: Otology; Sematic Field; Cocept Hierarchical Clusterig. 1. Itroductio Otologies provide a commoly agreed uderstadig about domai kowledge, ad as such, are becomig a crucial elemet of the Sematic Web. Although may defiitios have bee give for the term otology, the most commo ad simply stated is that otology is a specificatio of a shared coceptualizatio [1]. A otology specifies a shared domai vocabulary to describe cocepts, their properties ad relatios. I order to evaluate these otologies, there have emerged umerous otology aalysis approaches [2]. Some of them evaluate otologies for correctess, completeess ad redudacy; some of them are used to esure the cosistecy of otology throughout its etire lifetime; more recet approaches are proposed to aalyze the reuse ad adaptatio of existig otologies. This paper puts forward a ovel aalysis method amed sematic field. Differet from above approaches, the sematic field aalyzes otology characteristic from the perspective of cocept iteractio ad hierarchical clusterig. The sematic field model derives from classical field theory. The latter was used to describe o-cotact iteractio betwee matter particles. Later o, people abstracted it as a mathematical model to describe o-cotact iteractio betwee objects, ad to depict distributio law of physical quatities. I otology, cocepts are ot isolated. There exist o-cotact sematic iteractio ad associatio amog them. Table 1 shows the aalogies betwee electrostatic field ad sematic field. I this light, we itroduce classical field theory to otology area, ad put forward sematic field model. Table 1: Aalogies betwee electrostatic field ad sematic field electrostatic field sematic field electro otology cocept electro eergy importace of cocept electrostatic force cocept iteractio I sematic field, the equipotetial lies preset a atural estig structure. The equipotetial lies distributio correspods with the sematics distributio resultig from the iteractio ad associatio of otology cocepts. Based o equipotetial lie distributio, we ca reveal cocept hierarchical clusterig characteristics withi a otology. The sematic field ca show which cocepts are located i clusterig ceter, ad have powerful ifluece o surroudig cocepts. These are importat cocepts for the otology, ad should be paid eough attetio to i various otology operatios. Correspodig author zhxwag@cumt.edu.c

2 256 Z. Wag et al : A Novel Otology Aalysis Tool Except for aalyzig cocept iteractio ad hierarchical clusterig withi a otology, the sematic filed ca also be used i sematic-based resource clusterig, resource orgaizatio ad search i P2P, etc. These sematic field applicatios are ot the research scope of this paper. More details ca be referred to our previous work [3]. 2. Related Works Faraday put forward field cocept i area of thermophysics ad electromagetics. Later o, people abstracted it as a mathematical model to describe o-cotact iteractio betwee objects. The cocept of sematic field has bee hotly debated i liguistic commuity, which was used to aalyze sematic relatios betwee vocabularies i glossary system [4]. To the best of our kowledge, there are a few literatures about this topic i computer sciece ad iformatio techology area. Ismael Navas-Delgado et al [5] preseted the Sematic Field which eables the global compariso of otologies. The sematic field uses the results obtaied from otology matchig tools to estimate the global similarity betwee otologies. It ca discover the global cofiguratio of relatioships betwee existig otologies, ad had bee used i a tool called the Sematic Field Tool (SemFiT). They also published other research literature about this topic i [6]. The sematic field i [5] ad [6] is a set of relevat otologies. Ku Yue ad Weiyi Liu [7], ispired by the theories of lexical sematics ad electrostatic field, proposed a theoretical model called sematic field. I the method, text documets are regarded as sematic packages ad words are regarded as sematic elemets coverig these packages. The sematic filed was used to reflect the similarity or dissimilarity betwee sematic elemets or sematic packages i iformatio retrieval systems. They took automatic keyword extractio as the represetative applicatio of the proposed sematic field. The sematic field i [7] takes sematic elemets ad sematic packages i text documets as research target. Bejami Stoe ad Simo Deis [8, 9] outlied a method amed sematic field for estimatig the visual saliecy differet areas displayed o a web page. Latet Sematic Aalysis is used to calculate Sematic Fields values of ay (x, y) coordiate poit o a web page based o the structure of that web page. These Sematic Field values were the used to predict eye-trackig data. The sematic field i [8] ad [9] is based o four sematic field model: word overlap, Vector Space Model, Latet Sematic Aalysis, ad Sparse Noegative Matrix Factorizatio. Differet from above works, this paper itroduces the classical field theory to otology area, ad puts forward a sematic field model to aalyze otology characteristic from the perspective of cocept iteractio ad hierarchical clusterig. 3. Sematic Field Model 3.1. Sematic field costructio Defiitio 1.Give otology O ad its cocept set C = {c 1,c 2,...,c i,...,c }.The represetative cocept set (obtaied by usig complex etwork techology [10]) is C = {c 1,c 2,...,c i,...,c m},m<.costructig a m-dimesioal space R m, the i-th dimesio of R m is sematic similarity betwee represetative cocept C i ad all otology cocepts (1 i m). R m is called the sematic space of otology O, deoted by Ω o. Oce sematic space Ω o is established, all cocepts of otology O ca be put ito Ω o. Defiitio 2.Give otology O ad its correspodig sematic space Ω o, the positio vector of cocept c i i Ω o is x ci ={s i1,s i2,...,s ik,...,s im }, the k-th dimesio s ik is: s ik = sim(c i,c k ) 1 i,1 k m (1) where, c k is represetative cocept of otology O ; sim(c i, c k ) is the sematic similarity betwee c i ad c k. I this paper, we use sematic similarity measure proposed by Li et al [11], which sigificatly outperformed traditioal similarity measures [12]. si(c i,c k )= e αl eβh e βh e βh + e βh, c i c k 1, otherwise where, l is the shortest path legth betwee cocept c i ad cocept c j, h is the depth of the lowerest commo acestor i the otology. α ad β are parameters scalig the cotributio of shortest path legth ad depth, respectively. The optimal values are as follow: α=0.2, β=0.6. Defiitio 3.Give otology O ad its correspodig sematic space Ω o. All otology cocepts are mapped ito Ω o. Otology cocepts are regarded as field sources, ad these cocepts iteract ad associate with each other, formig oe field called sematic field. Sematic field depicts iteractio amog otology cocepts ad sematic distributio law withi a otology. We ca describe sematic field from the perspectives of potetial, gradiet ad itesity. Potetial represets the sematics of a certai positio; gradiet describes local sematics chage of sematic field; ad field itesity reflects the stregth of sematics associatio amog cocepts. Research showed that the ifluece scope of oe field source is limited i short-rage field, ad potetial value will quickly atteuate to zero with distace icreasig. I sematic field, the ifluece scope of sematic (2)

3 Appl. Math. If. Sci. 8, No. 1, (2014) / associatio amog cocepts is limited, too. That is to say: sematic field presets properties of short-rage field to some extet. The typical potetial fuctio of short-rage field is Gaussia fuctio, so this paper uses it as the potetial fuctio of sematic field. Defiitio 4.Give cocept (field source) set C={c 1, c 2,...,c i,...,c } of otology O ad its correspodig positio set P = {x c1,x c2,...,x ci,...,x c } i Ω o. x ci is positio vector of cocept c i (1 i ). The potetial of ay field positio x is defied as: ϕ(x)= ϕ ci (x)= ( ( m ci e xci x σ ) 2 ) where, is otology cocept umber; ϕ ci (x) is potetial produced by cocept c i at x ; m ci is cocept mass of c i, m ci 0; σ is ifluece factor, σ (0,+ ); x ci x is sematic distace betwee x ci ad x. If the x ci x exceeds the ifluece scope of c i, the ϕ ci (x)=0. I fact, field itesity fuctio F(x) ad potetial fuctio ϕ(x) is equivalet. They are coected together by differetial operator, F(x)= ϕ(x). Therefore, we ca get field itesity fuctio F(x) via the differetial of potetial fuctio ϕ(x). Here, we o loger describe itesity defiitio ad gradiet defiitio i detail Otology cocept mass calculatio Potetial computatio ivolves otology cocept mass m. Apparetly, matter particle has its iheret mass. But how to weigh otology cocept mass? Cocept mass should reflect the importace of the cocept. The more importat the cocept is, the bigger the mass is. I this paper, we adapt PageRak [13] algorithm, which is widely used i search egie area, to calculate otology cocept mass. The PageRak algorithm was put forward by Sergey Bri ad Larry Page, which had bee applied successfully i google search egie. The PageRak assumes that a web page s importace is measured by the umber ad importace of other web pages likig to the page. Similarly, the cocept importace ca be measured by the umber ad importace of its sub-cocepts. The more sub-cocepts oe cocept has, the more importat the cocept is. The cotributios of these sub-cocepts are differet: the more importat the sub-cocept itself is, the more cotributio the sub-cocept makes to the cocept. Defiitio 5. Give otology O ad its cocept set C= {c 1,c 2,...,c i,...,c }. The mass of cocept A is defied as follows: (3) m(a)=(1 d)+d ( m(c 1) s(c 1 ) + m(c 2) s(c 2 ) m(c k) s(c k ) ) (4) where,m(a) is mass of cocept A; c 1,c 2,...,c k are sub-cocepts of cocept A; m(c i ) is cocept mass of c i (i = 1,2,...k); s(c i ) is paret cocept umber of c i, ormally 1; d is dampig factor, 0<d < 1, geerally set at Ifluece factor selectio The ifluece factor σ will affect sematic field. If σ is small, the sematic associatio amog cocepts is weak. Ad whe σ 0, there is eve o sematic associatio amog cocepts. Coversely, if σ is big, the sematic associatio becomes strog, ad i extreme coditio all otology cocepts associates with each. Therefore, we eed select suitable σ value, so as to make the sematic field distributio reflect the real iteral distributio of sematics as much as possible. This paper adopt potetial etropy [14] to evaluate the ratioality of potetial field distributio. I iformatio theory, the shao etropy reflects system ucertaity. The bigger the shao etropy is, the more ucertai the system is; Coversely, the smaller it is, the least ucertai the system is. Whe field achieves the smallest shao etropy, the ifluece factor value is optimal. Suppose the potetial values of cocepts c 1,c 2,...,c are ϕ c1,ϕ c2,...,ϕ c respectively, the potetial etropy H is defied as: H = ϕ ci Z log(ϕ ci Z ) (5) where, is otology cocept umber; Z = ϕ ci is a ormalizatio factor. I essece, ifluece factor selectio is the problem of fuctio H(σ) miimizatio. That is: mih(σ)=mi ϕ ci Z log(ϕ ci Z ) (6) This paper adopts golde sectio method [15] to solve this problem, ad the algorithm is described as follow. Algorithm 1. Ifluece factor selectio algorithm Iput: otology cocept set C={c 1,c 2,...,c i,...,c }, positio set P = {x c1,x c2,...,x ci,...,x c }, degree of accuracy ε Output: optimal ifluece factor σ Descriptio: a= 2 3 mi ci c j x ci x c j, b= 2 3 max ci c j x ci x c j σ M = a+(1 τ)(b a),σ N = a+τ(b a),τ = Calculatig H M = H(σ M ) ad H N = H(σ N ) While b a >ε do If H M < H N the b=σ N, σ N = σ M, σ M = a+(1 τ)(b a),h N = H M Calculatig H M = H(σ M ) ELSE a=σ M, σ M = σ N, σ N = a+τ(b a),h M = H N

4 258 Z. Wag et al : A Novel Otology Aalysis Tool Calculatig H N = H(σ N ) Ed While If H M < H N the σ = σ M Else σ = σ N Retur σ 4. Sematic Field Based Cocept Hierarchical Clusterig I sematic field, if coect the positios with same potetial value, we ca get a series of equipotetial lies. These equipotetial lies preset a atural estig structure. The equipotetial lies distributio correspods with the sematics distributio resultig from the iteractio ad associatio of otology cocepts i sematic space. Based o equipotetial lie distributio, we ca discover the hierarchical clusterig characteristics of otology cocepts. Figure 1: Parts of SWRC otology cocepts 4.1. Iitial clusterig ceter selectio Potetial reflects ifluece capability of otology cocept i whole sematic space. Potetial heart is local maximum positio i sematic field, which ca be regarded as virtual field source. Uder the attractio of these virtual field sources, otology cocepts i sematic filed show self-orgaizig clusterig characteristic. I the sematic field formed by sigle cocept, potetial heart is the positio where the cocept itself lyig. For the sematic field formed by multiple cocepts, we ca search all potetial heart positios alog the directio of gradiet by usig climbig method. If positio x is potetial heart, for ay positio y, x should satisfy the followig formula: y y eighbor(x) ϕ(x) ϕ(y) (7) where, eighbor(x) are surroudig positios of x, ϕ(x) is potetial value of positio x, ϕ(y) is potetial value of positio y. Figure 1 is parts of SWRC [16] otology cocepts, Figure 2 shows the sematic field i two-dimesioal sematic space formed by these cocepts, ad potetial hearts are obvious. Because of potetial superpositio, potetial heart does ot coicide with otology cocept. We select the cocept beig closest to the potetial heart as iitial clusterig ceter. The potetial heart umber determies the iitial clusterig umber hierarchical clusterig algorithm descriptio Iteratively mergig iitial clusterig ceter based o saddle poits betwee two local maximum positios [14], we ca get hierarchical clusterig of otology cocepts. Figure 2: Sematic field i two-dimesioal sematic space (σ= 0.1) Algorithm 2. sematic field based cocept hierarchical clusterig algorithm Iput: otology cocept set C = {c 1,c 2,...,c i,...,c },positio set P = {x c1,x c2,...,x ci,...,x c }, oise threshold ε Output: cocepts hierarchical divisio {CL 0,CL 1,..., CL k } Descriptio: (1) select optimal ifluece factor σ usig algorithm 1; (2) Map = CreateMap(C, σ); // Mesh geeratio ad idex tree costructio (3) CriticalPoits = Search CriticalPoits(Map, σ); //Searchig topology critical poits (4) MaxPoits is local maximum positio set, SadPoits is saddle poits set; (5) CL 0 = Iitiallizatio Partitio(Map,C,Max Poits, σ, ε); //Otology cocepts iitial partitio based o local maximum positios; (6) {CL 0,CL 1,...,CL k } = Partitio Merge(Map, CL 0,MaxPoits,SadPoits,σ,ε); // Iteratively mergig iitial partitio based o saddle poits betwee two local maximum positios; Figure 3 shows the hierarchical clusterig results of otology cocepts i Figure 1 based o algorithm 2.

5 Appl. Math. If. Sci. 8, No. 1, (2014) / Experimets ad Results The goal of experimets is to ivestigate the effectiveess of sematic field as a otology aalysis tool. Simulatio program implemeted with java ad relative tools i the Liux eviromet. All otologies used i experimets come from Protégé Otology Library of Staford Uiversity ( Otology Library). Figure 4: Clusterig error rate better reveal cocept clusterig characteristics. hu otology is a simple hierarchic divisio of hydrologic uits, therefore, the clusterig error rate is relatively high Scalability Figure 3: otology cocepts clusterig spectra We selected 20 otologies from Protégé Otology Library to evaluate the scalability of this method. Otology cocept scale rage from 4633 to 102. Figure 5 shows the executio time of 20 otologies. The axis is cocept umber of each otology, ad the axis is clusterig executio time of each otology. The result shows that this method has good scalability. Cocept clusterig executio time follows a approximate liear relatioship with cocept scale Clusterig error rate We selected three otologies: ka otology (a otology about cocepts from academic research), hu otology (a hierarchic divisio of hydrologic uits), ad fiace otology (a otology o fiacial istrumets, ivolved parties, processes ad procedures i securtities hadlig). We carried out sematic field based cocept hierarchical clusterig for each otology, ad the compared these results with maual results doe by domai experts. This paper defies a evaluatio criterio amed clusterig error rate as follows: cocept umber o f clusterig error total cocept umber Figure 4 shows the clusterig error rate of three otologies at level 2, level 3 ad level 4. O the whole, the sematic field does well i otology cocept hierarchical clusterig. The more the clusterig partitio, the higher the clusterig error rate; o the cotrary, the less the clusterig partitio, the lower the clusterig error rate. Experimet also shows that otology structure may affect the hierarchical clusterig performace. Comprehesive otology structure ca (8) Figure 5: Scalability of sematic field based cocept hierarchical clusterig 5.3. Ifluece of otology evolutio Otology is ot static. Otology evolutio meas modifyig or upgradig the otology whe there is a certai eed for chage or there comes a chage i the

6 260 Z. Wag et al : A Novel Otology Aalysis Tool domai kowledge [17]. Otology evolutio geerally icludes cocept additio, cocept deletio, ad cocept traslocatio. We carried out experimet to evaluate the ifluece of otology evolutio o hierarchical clusterig results. I Figure1, supposes the leaf cocept F: Maager is deleted. Figure 6 shows the sematic field i two-dimesioal sematic space after cocept deletio. The hierarchical clusterig results of other cocepts does ot chaged after leaf cocept F: Maager deleted. 6. Coclusio Otologies are becomig a crucial elemet of the Sematic Web. I order to evaluate these otologies, there have emerged umerous otology aalysis approaches. This paper itroduces the classical field theory to otology area, ad puts forward a sematic field model to aalysis otology characteristics from the perspective of cocept iteractio ad hierarchical clusterig. I sematic field, the equipotetial lies preset a atural estig structure. The equipotetial lies distributio correspods with the sematics distributio resultig from the iteractio ad associatio of otology cocepts. Based o equipotetial lie distributio, we ca aalyze cocept hierarchical clusterig characteristic. The sematic field based hierarchical clusterig ca show which cocepts are located i clusterig ceter, ad have powerful ifluece o surroudig cocepts. These are importat cocepts for the otology. We should pay eough attetio to these cocepts i various otology operatios. Ackowledgemets Figure 6: Sematic field after cocept deletio (σ= 0.1) I Figure 1, supposes a leaf cocept J: Assistat Professor is added the sub-cocept of cocept H: Faculty Member. Figure 7 shows the sematic field i two-dimesioal sematic space after cocept additio. The hierarchical clusterig results of other cocepts does ot chage after leaf cocept J: Assistat Professor added. Figure 7: Sematic field after cocept additio (σ= 0.1) Figure 6 ad Figure 7 showed that the ifluece of otology evolutio o sematic field is local. Otology evolutio does ot chage the hierarchical clusterig results of other cocepts. This work was supported by the Fudametal Research Fuds for the Cetral Uiversities No.2013XK10. Our sicere thaks go to Professor Dalu Zhag, the doctoral supervisor of first author Zhixiao Wag, who itroduces field theory to us to solve scietific problems. Refereces [1] Gruber CTR. A traslatio approach to portable otologies. Kowledge Acquisitio, 5, (1993). [2] Valerie Cross, Aidita Pal. A otology aalysis tool. Iteratioal Joural of Geeral Systems, 37, (2008). [3] Zhixiao Wag, Dalu Zhag, Guagjie Yu, et al. Sematic field model. Joural of Togji Uiversity (ature sciece), 37, (2009). [4] Marta Carretero. The Sematic Field of Modal Certaity: A Corpus-Based Study of Eglish Adverbs. Joural of Pragmatics, 40, (2008). [5] Ismael Navas-Delgado, Maria del Mar Rold-Garcła, Jos F. Aldaa-Motes. Sematic fields: fidig otology relatioships. LNCS, 5690, (2009). [6] I. Navas, I. Saz, J.F. Aldaa, R. Berlaga. Automatic geeratio of sematic fields for resource discovery i the sematic web. LNCS, 3588, (2005). [7] Ku Yue, Weiyi Liu. Sematic field: a theoretical perspective of modelig iformatio retrieval. Iteratioal joural o artificial itelligece tools, 18, (2009). [8] Stoe, B., Deis, S.. Usig LSA Sematic Fields to predict eye movemet o web pages. Proceedigs of the twety ith coferece of the cogitive sciece society. Lawrece Erlbaum Associates, Mahwah, NJ, (2007).

7 Appl. Math. If. Sci. 8, No. 1, (2014) / [9] Bejami Stoe, Simo Deis. Sematic models ad corpora choice whe usig Sematic Fields to predict eye movemet o web pages. Iteratioal Joural of Huma- Computer Studies, 69, (2011). [10] Dalu Zhag, Zhixiao Wag, We Liu, et al. Otology structure aalysis based o complex etwork. Joural of Togji Uiversity (ature sciece), 37, (2009). [11] Li Yuhua, Badar Zuhair A, McLea David. A approach for measurig sematic similarity betwee words usig multiple iformatio sources. IEEE Tras. o Kowledge ad Data Egieerig, 15, (2003). [12] Agelos Hliaoutakis, Giais Varelas, Euripides G.M. Petrakis, et al. MedSearch: A Retrieval System for Medical Iformatio Based o Sematic Similarity. 10th Europea Coferece o Research ad Advaced Techology for Digital Libraries. Alicate, Spai. September, 17-22, (2006). [13] Sergey Bri, Lawrece Page. The aatomy of a large-scale hypertextual web search egie. Computer etworks ad ISDN systems, 30, (1998) [14] Li DY, Du Y. Artificial Itelligece with Ucertaity. Beijig: Natioal Defese Idustry Press, (2005). [15] Jiaju Huag, Xiogwei Zhag, Yafei Zhag, et al. A ovel speech ehacemet algorithm based o data field. Sigal Processig, 27, (2011). [16] Sure Y, Bloehdor S, Haase P, et al. The swrc otology-sematic web for research commuities. Proceedigs of 12th Portuguese Coferece o Artificial Itelligece,LNCS, 3803, (2005). [17] Haridimos Kodylakis, Dimitris Plexousakis. Otology evolutio i data itegratio: query rewritig to the rescue. LNCS, 6998, (2011). Zhixiao Wag is a associate professor of College of Computer Sciece ad Techology, Chia Uiversity of Miig ad Techology. He received his PhD degree i the Departmet of Computer Sciece ad Egieerig at Togji Uiverstiy i 2011,ad the BSc ad MSc degrees i computer sciece from Chia Uiversity of Miig ad Techology, i 2001 ad 2004, respectively;his research iterests iclude field theory applicatio, iformatio retrieval ad data miig. He has published more tha 10 peer-reviewed research papers i jourals ad iteratioal cofereces. Shi Xiog Xia is bor i 1962, Ph.D. He is a professor at school of Computer Sciece ad Techology i CUMT. He has published more tha 60 research papers i jourals ad iteratioal cofereces. His research iterest is Wireless sesor etworks ad itelliget iformatio processig et al. Qiag Niu is bor i 1974, doctor, a associate professor i Chia Uiversity of Miig ad Techology, received B.S. i Northeaster Uiversity i 1997, received Master i 2004 ad PHD i 2010 i Chia Uiversity of Miig ad Techology. His mai research iterests are itelliget optimizatio algorithms ad data miig.