Ontology Generator from Relational Database Based on Jena

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1 Computer and Informaton Scence Vol. 3, No. 2; May 2010 Ontology Generator from Relatonal Database Based on Jena Shufeng Zhou (Correspondng author) College of Mathematcs Scence, Laocheng Unversty No.34 Wenhua Road, Laocheng, Shandong , Chna E-mal: Hayun Lng College of Computer Scence, Laocheng Unversty No.34 Wenhua Road, Laocheng, Shandong , Chna Me Han College of Prmary Educaton, Jnng Unversty No.1 Xngtan Road, Xnqu, Qufu, Shandong , Chna Huawe Zhang College of Computer Scence, Laocheng Unversty No.34 Wenhua Road, Laocheng, Shandong , Chna The research s fnanced by Natural Scence Foundaton of Laocheng Unversty (X051034) Abstract There are a lot of dffcultes n the ontology generaton from relatonal database such as unclear generaton approaches, un-unfed ontology languages and so on. So n order to provde unfed ontology and mprove the qualty of ontology generaton, approach proposed n ths paper frstly extracts database metadata nformaton from relatonal database usng reverse engneerng technque, and then analyzes the correspondent relatonshp between relatonal database and OWL ontology, and presents an ontology generaton from relatonal database. Fnally, a prototype tool of the generator, mplemented based on Jena n Java development platform, and case study demonstrates the feasblty and effectveness of the approach. Keywords: Semantc Web, Ontology, Relatonal Database, OWL, Jena 1. Introducton The Semantc Web, unlke the current web, wll represent web content that s also machne-readable n order to provde better machne assstance for human users (Note 1). Ontology, an explct specfcaton of a shared conceptualzaton, play an mportant role n creatng such machne-readable content by defnng shared and common doman concepts (Note 2). And ncreasngly wth the development of Semantc Web research, people have realzed that the success of Semantc Web, n most extent, depends on the prolferaton of ontology. Partcularly, ontologes could resolve semantc heterogenety by provdng a shared comprehenson of a gven doman of nterest. And at the same tme people pay more and more attenton to the constructon of ontologes. However, there s a large quantty of content on web pages stll generated from relatonal database. In partcular, as reported n (Chang, He et al. 2004), about 77.3 percent data on the current web are stored n relatonal database. Therefore, t s mportant to generate ontology from exstng rch legacy data source. Now, there are a lot of ontology constructons tool can be used, but there some dffcultes for doman expert to understand the meanng between these approaches such as unclear generaton approach, un-unfed ontology language and so on. So, n order to provde unfed ontology and mprove the qualty of ontology, ths paper analyze the correspondent relatonshp between relatonal database and OWL ontology, presents an ontology generaton from relatonal database(rdb2on) based on Jena. In fact, relatonal database can be formalzed by Frst Order Logc (Smullyan 1995); whle the ontology uses Descrpton Logc (Baader, Calvanese et al. 2007), 263

2 Computer and Informaton Scence whch s a subset of Frst Order Logc. Therefore, t s feasble to generate ontology from relatonal database. In ths paper, we wll present a generator tool whch s mplemented based on Jena n Java platform. We wll descrbe the desgn and mplementaton of the generator n detal. The remander of ths paper s organzed as follows. Frst, the secton 2 descrbes the formal defnton of schema. Secton 3 enumerates a set of sgnfcant rules of transformaton from relatonal database schema to ontology. Second, secton 4 and 5 demonstrates the desgn and mplementaton of generator and descrbes the related work respectvely. Fnal, gves the summary of the paper wth future work n secton Formal Defnton Where, we gve smplfed defntons of concepts n order to descrbe the problem convenently. 2.1 Relatonal Schema Defnton 1. A relatonal schema s a tuple RU (, DdomI,, )(Note 3), where R Represents the name of a relaton. A relaton s defned as a set of tuples that have the same attrbutes, and a tuple usually represents an object and nformaton about that object. At the same tme a relaton s usually descrbed as a table, whch s organzed nto rows and columns. All the data referenced by an attrbute are n the same doman and conform to the same constrants. U Represents a fnte set of attrbutes n a relaton, such attrbutes usually are used to descrbe a entty, such as { A1, A2, L, An },.e., the relatonal table has n attrbutes. Where, every A s dsjonted wth A j when j. D Represents a fnte set of doman of the attrbutes. A doman descrbes the set of possble values for a gven attrbute. Because a doman constrans the attrbute s values and name, t can be consdered constrants. Mathematcally, attachng a doman to an attrbute means that all values for ths attrbute must be an element of the specfed set. Such as{ D1, D2, L, Dn}, where every D denotes a doman and has a set of assocated values. dom Represents a functon that maps theu to the D,.e., every attrbute A n U must have a vald value n D. I Represents a fnte set of ntegrty constrant, whch restrcts the data nstances that can be stored n the database. Constrants allow you to further restrct the doman of an attrbute, and provde one method of mplementng busness rules n the database. Constrant also can apply to sngle attrbute, to a tuple or to an entre relatonal table. There are two prncpal constrants named entty ntegrty and referental ntegrty. 2.2 OWL Ontology C R Defnton 2. An ontology s a tuple OCP (,, RP,, H ) (Note 4), where O Represents the name of ontology. Ontology represents doman knowledge. An ontology s defned as an explct specfcaton of a conceptualzaton. In Semantc Web doman, an ontology s defned as a set of knowledge terms, ncludng the vocabulary, the semantc nterconnectons, and some smple rules of nference and logc for some partcular topc.(hendler 2001) C Represents a fnte set of concepts n ontology. Where, a concept also be called a class whch defnes a group of ndvduals that belong together because they share some propertes. Classes can be organzed n a specalzaton herarchy. For example, Tom and John are both members of the class Person. C P Represents a fnte set of propertes of concepts. Propertes can be used to state relatonshps between ndvduals or from ndvduals to data values. Fox example, f c C, then the propertes of c can be C denoted by P ( c ). The concepts are dfferent as results of the concepts always have dfferent propertes. R Represents relatonshp between concepts n ontology. Where, the concepts always are classes. A relatonshp usually conssts of two parts, one s the doman, and the other s range. And the doman always s a concept, whle the range always s ether a concept or nterval. We can also say that property s a specal relatonshp when the range s nterval. R P Represents a fnte set of propertes of relatonshp, whch restrct the relatonshp n some extent. Fox example, we have a relatonshp named hasage, and ts range s nteger doman. Further, we could restrct that ts vald values are from one to 100. H Represents herarchy n concepts, propertes and classes. Class herarches may be created by makng one or more statements that a class s a sub-class of another class. Fox example, the class Person could be 264

3 Computer and Informaton Scence Vol. 3, No. 2; May 2010 stated to be a subclass of the class Mammal. From ths a reasoner can deduce that f an ndvdual s a Person, then t s also a Mammal. Lkewse, Property herarches may be created by makng one or more statements that a property s a sub-property of one or more other propertes. Fox example, C s sub-class of C j, f and only f every nstance n C s nstance n C j. 3. Rules for Ontology Generaton The generaton process s done progressvely as followng rules. Each relatonal table s transformed nto a class and each column s transformed nto a property respectvely. In addton, f the relatonal database table has foregn key references to other tables, these can be transformed to object property. Lkewse, f the column value s not nullable, the cardnalty of property correspondng s set to one, etc. However, n order to save space we only descrbe the man rules as follows, and at the same tme gve the formal representaton. Rule 1. Each table n relatonal database should be transformed nto a class wth same name correspondng to the table, and comment of table be transformed nto comment of class, respectvely. T RDB Class( ID( T)) Where, RDB represents relatonal database, ID represents the unque dentfer functon of table. Class represents a functon to create class accordng to table. Rule 2. For table n relatonal database, each column, whch s nether prmary key nor foregn key, could be transformed nto a data-type property wth same name correspondng to the column. And the doman s the class created by ths table, range the data-type of the column n the database. T RDB F Attr( T ) sfkey( F, T) spkey( F, T ) DatatypeProperty( ID( F), doman( ID( T )), range( datatype( F ))) Where, Attr get all columns from table. sfkey and spkey determne whether a column s a foregn key and prmary key respectvely. DatatypeProperty s also a functon to create datatype property n OWL language. Rule 3. Each column, whch belongs to prmary key n table, should be transformed nto functonal property n OWL. F Attr( T ) spkey( F, T ) FunctonProperty( ID( F), doman( ID( T )), range( datatype( F))) Where, FunctonProperty s a functon to create object property. Rule 4. The foregn key n T 1, whch references to prmary key of T 2 n database, are transformed nto two nverse object-propertes. The doman of one object-property s class correspondng to T 1 and the range s class correspondng to T 2 respectvely. Those of the other are reverse. T1, T2 RDB F Attr( T1) sfkey( F, T1) Referce( F, T1, T2) ObjectProperty( ID( F), doman( C1), range( C 2)), ObjectProperty( nvid( F), doman( C2), range( C 1)) Where, Referce s a functon that one relatonal table s related wth the other by foregn key. Then, we gve some smple constrants on ordnary columns and foregn keys. Rule 5. In a relatonal table, f a column (except foregn key) s declared as NOT NULL, then the cardnalty of the property corresponded s set to one. Rule 6.In a relatonal table, f a column (except foregn key) s declared as UNIQUE, then the maxmal cardnalty of the property corresponded s set to one. Rule 7. In a relatonal table, f a foregn key s declared as NOT NULL, then the mnmal cardnalty of the object property corresponded s set to one. Rule 8. In a relatonal table, f a foregn key s declared as NULLABLE, then the maxmal cardnalty of the object property corresponded s set to one. 4. Desgn and Implementaton of the Ontology Generator The am of the ontology generator s to make full use of the exstng relatonal database to generate ontology automatcally, to save the tme of development and to mprove the qualty of the ontology generaton. In fact, there are a lot of explct and mplct conceptual model and nformaton resource n relatonal database, so mnng and usng them as the knowledge base of conceptual desgn of ontology to enable the ontology generated 265

4 Computer and Informaton Scence to have more rch semantcs. Based on the rules ntroduced above, an ontology generator from relatonal database (RDB2On) was developed. The RDB2On can drectly extract the relatonal schema from database, and then translates them nto an OWL ontology wrtten n RDF/XML syntax (Note 5). The functon modules of the generator consst of three parts. Database Analyze Module. In ths module, we adopt reverse engneerng technque(roger, Terence et al. 1996) to acqure database metadata from database. Frstly, analyzes the database structure and extracts the metadata about all tables from the relatonal database. And then acqures the metadata nformaton about all columns from every table. Fnally, collects all the nformaton together as the descrpton of entre relatonal database. Savng space, we show a case n fgure 1 below. Usng MySQL, we created the database product and descrbed as an example. Schema Transform Module. Ths module transforms the relatonal database descrpton nto OWL ontology accordng to the rules ntroduced above. Frstly, accordng to the metadata of relatonal database, we transform all tables nto classes, and columns nto data type property or object property. Secondly, usng ntegrty constrants relatonshp, we created some cardnalty constrants on the columns. OWL Ontology Module. Ths module manly deals wth the fnal ontology document, ncludng wrtng the ontology nto a document and readng the ontology document to the screen for end user. For convenence, we wrte the document nto RDF/XML syntax (see fgure 2). At the same, user can also change to other style accordng to ther demand. The generator s mplemented based on Jena2.6.0 (Note 6)n Java1.6 (Note 7) development platform. Jena s open source and grown out of work wth the HP Labs Semantc Web Programme (Note 8). At the same tme Jena s a Java framework for buldng Semantc Web applcatons. It provdes a programmatc envronment for OWL. In the prototype tool, we use DatabaseAnalyser Java class to extract the database metadata about all tables n database and use them as nput nformaton to our translaton algorthm, and then an standard OWL ontology document s automatcally generated from the orgnal database. We use MySQL5.0.28(Note 9) databases as the orgnal database, because they provde specfc vews about the database metadata. 5. Related Work In the lterature there are several approaches for addressng ontology constructon from relatonal database. Volz et al. n (Stojanovc, Stojanovc et al. 2002) propose an approach based on sem-automatc generaton of a F-logc ontology from a relatonal database model. The ontology generaton process takes n account dfferent types of relatonshp between database tables and maps them to sutable relatons n the ontology. The process s not completely automatc and a user nterventon s needed when several rules could be appled to choose the most sutable. Relatonal.OWL (de Laborda and Conrad 2005) s an OWL ontology representng abstract schema components of relatonal databases. Based on ths ontology, the schema of any relatonal database can be descrbed and n turn be used to represent the data stored n that specfc database. However, other approaches, such as D2R (Bzer 2003), use a declaratve, XML-based language to descrbe mappngs between relatonal database models and ontologes mplemented n RDF Schema. In D2R, basc concept mappngs are defned usng class maps that assgn ontology concepts to database sets. The class map s also the contaner of a set of attrbute and relaton mappng elements called brdges. Our approach generates ontology from relatonal database drectly and automatcally. At the same tme, we consder that user nterventon may be needed later to refne the generated ontology wth the help of doman experts. Subsequently, we could get hgh qualty ontology to provde better semantcs for local database source n specal doman. However, the ontology, generated from relatonal database usng our generator, could be vewed as the local data source ontology wthout any nstances, and several local ontologes could be ntegrated nto a global ontology n the future for the entre system. For global ontology, the advantage of wrappng each nformaton source to a local ontology s to allow the development of source ontology ndependently of other sources or ontologes. So we beleve that ths approach s more effectve that a massve dump. 6. Summary and Future Work In ths paper, we present an ontology generator tool mplemented based on Jena2.6.0 n Java1.6 platform. We use MySQL as the orgnal database, because they provde specfc vews about the database metadata. The man contrbutons of ths paper are lsted as follows. Frst, we descrbe a new approach whch can provde unfed ontology and mprove the qualty of ontology. Second, ths approach can mne the mplct conceptual relatonshp n relatonal database,.e., herarchy relatonshp, concept constrants, property constrants and so on. Fnal, the ontology generated usng our prototype tool could be ntegrated nto a global ontology n the future. In 266

Computer and Informaton Scence Vol. 3, No. 2; May 2010 addton, case study demonstrates that ths approach s feasble and effectve. References Baader, F., D. Calvanese, et al. (2007).

C. C., B. He, et al. (2004). Structured databases on the web: Observatons and mplcatons, ACM New York, NY, USA. 33: 61-70. de Laborda, C. P. and S. Conrad. (2005). Relatonal.

5 Computer and Informaton Scence Vol. 3, No. 2; May 2010 addton, case study demonstrates that ths approach s feasble and effectve. References Baader, F., D. Calvanese, et al. (2007). The descrpton logc handbook, Cambrdge Unversty Press New York, NY, USA. Bzer, C. (2003). D2R MAP-A database to RDF mappng language. The 12th Internatonal World Wde Web Conference(WWW2003). Chang, K. C. C., B. He, et al. (2004). Structured databases on the web: Observatons and mplcatons, ACM New York, NY, USA. 33: de Laborda, C. P. and S. Conrad. (2005). Relatonal. OWL: a data and schema representaton format based on OWL, Australan Computer Socety, Inc. Darlnghurst, Australa, Australa: Hendler, J. (2001). Agents and the semantc web. IEEE INTELLIGENT SYSTEMS AND THEIR APPLICATIONS, IEEE COMPUTER SOCIETY. 16: Roger, H. L. C., M. B. Terence, et al. (1996). A Framework for the Desgn and Evaluaton of Reverse Engneerng Methods for Relatonal Databases, Elsever Scence Publshers B. V. 21: Smullyan, R. M. (1995). Frst-order logc, Dover Publcatons. Stojanovc, L., N. Stojanovc, et al. (2002). Mgratng data-ntensve web stes nto the semantc web. New York, NY, USA, ACM Symposum on Appled Computng (SAC2002). Madrd, Span: Fgure 1. Relatonal Database Schema Fgure 2. Ontology from Relatonal Database Notes Note 1: Note 2: Note 3: Note 4: Note 5: Note 6: Note 7: Note 8: Note 9: 267

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