Translation of Sparql to SQL Based on Integrity Constraint

Transcription

1 Journal of Computational Information Systems 7:2 (2011) Available at Translation of Sparql to SQL Based on Integrity Constraint Xianji CUI 1, 2, Dantong OUYANG 1, 2,, Yuxin YE 1,2, Xiaolong WANG 1,2 1 College of Computer Science and Technology, Jilin University, Changchun , China 2 Key Laboratory of Computation and Knowledge Engineering, Ministry of Education Abstract Most existing storages of the ontology use relational databases as a backend to manage RDF data. This motivates us to translate SPARQL queries, the proposed standard for RDF querying, into equivalent SQL queries. At the basis of giving the IC-based storage to store the ontology with integrity constraints, we present the corresponding translation of SPARQL to SQL called IC-based translation. With the different structure of the tables in the relational databases, the SPARQL query statements are divided into four types. For the different type of query statements, we give the corresponding translations. Keywords: Integrity Constraint; Ontology; SPARQL; SQL 1. Introduction RDF [1] is a framework for representing information in the Web, providing a powerful data model. SPARQL [2], a query language for RDF that has been proposed by W3C, allows the specification of triple and graph patterns to be matched over RDF graphs. Nowadays, the existing storage mechanisms of RDF use the relational database management as the foundation of storing the RDF data [3][4][5]. Therefore, it is in urgent need of translating SPARQL into SQL in relational databases. Cyganiak [6] has translated SPARQL into the relational algebra, and stated that the algebra is equivalent with SQL. Elliott [7] has proposed a SQL model-based algorithm to implement each SPARQL algebra operator and generate a flat SQL statement for the relational database query engine. Chebotko [8] has proposed a semantic preserving translation by relational algebra and SQL generation templates for the SPARQL operators. However, the current mainstream translations mentioned above are all based on the traditional storages that fail to consider the integrity constraints and may lead to the incompleteness of the query information. In this paper, integrity constraints will be added into the ontology to generate the ontology with integrity constraints; further the IC-based storage is proposed to store the ontology. Since the query translation depends on the storage mechanism and the traditional translations do not appropriate to this storage, we aim to translate the SPARQL to SQL based on the tables generated by the IC-based storage. The reminder of this paper is organized as follows. Section 2 adds the integrity constraints into the ontology and further describes the storage of the ontology in the relational database. Section 3 proposes the Corresponding author. addresses: ouyangdantong@163.com (Dantong OUYANG) / Copyright 2011 Binary Information Press February, 2011

2 X. Cui et al. /Journal of Computational Information Systems 7:2 (2011) translation of SPARQL to SQL according to the different query statements. Section 4 gives the experiment. We conclude in Section IC-based Storage 2.1. Ontology with Integrity Constraints Integrity constraints are conditions that hold in normal operation, but may fail to hold in the event of a fault. They are added into the ontology to improve the dependability of the ontology. Since each TBox axioms can be interpreted as the integrity constraints, we give the axioms to represent the integrity constraints. Definition 1. (IC-based Axiom) For each TBox axiom, if it is used for testing the integrity of the ontology call it an IC-based axiom; otherwise call it a normal axiom. There are three types of IC-based axioms: typing constraint axioms (include domain constraints and range constraints), value constraint axioms and number constraint axioms [9]. For example, the following axioms state that each GraduateStudent must take 1~3 Courses, the property name must be specified as a datatypeproperty on GraduateStudent that takes String as values and the age of the GraduateStudent must greater than or equal to 21. GraduateStudent 1 takescourse. Course GraduateStudent 3 takescourse. Course name. GraduateStudent name. String GraduateStudent age.( min 21) In order to assure the integrity of the ontology, we check the satisfaction of IC-based axioms to ensure the integrity of the ontology using the well-known method of logic programming [10]. For the data do not satisfy the integrity constraint, we work with knowledge engineer to modify the data to satisfy the constraints. The knowledge engineers are required to complement the missing data, modify the mistaken data and delete the duplicate data according to the requirement. In this way we obtain the IC-based ontology which is complete and satisfies the entire given IC-based axioms. A subset of the IC-based ontology evolved from LUBM (Lehigh University Benchmark) [11] is showed in Fig The Storage Strategy With the special feature of the ontology, we put forward IC-based storage to store the data. First of all, the following definition is given to distinguish the properties. Definition 2. (Constraint Property and Normal Property) Let the axiom C nrd. be the form of the number constraints, where {,, = }, {R} is a set of the object properties which appear in the right side of the axioms. For each property R i in the TBox, if R i { R} we call it a constraint property Prop C ; otherwise, call it a normal property Prop N. For example, in Fig.1 since there is the constraint axiomgraduatestudent 1 takescoursecourse., we see that takescourse is a constraint property, while other properties of GraduateStudent are normal properties. The storage has two parts: create tables and insert data. Then, describe them as follows: Create Tables. Since the properties of instances of the same class are similar, use the class-table to store the IC-based ontology data. Create a class-table for each class and the attributes of the table are the set of normal properties. Since the data satisfy the number constraints, the number of the constraint property

3 396 X. Cui et al. /Journal of Computational Information Systems 7:2 (2011) value for an instance may be uncertain, thus extract the constraint property separately. Then, create a binary constraint-table for each constraint property. In further, because of the low query efficiency for the no targeted class queries (the queries that we do not know which class-table to query beforehand), extract the type attribute separately. Finally, we create a binary type-table in the relational database. Fig.1 A Subset of the IC-based Ontology Insert Data. The IC-based ontology data are transformed into a set of statements. For each statement, insert the data into the corresponding tables. If the predicate of the statement is type, insert the subject-object pair per row, which represents instance and class of the instance in ABox, into the type-table. If the predicate of the statement is a normal property, the object of this statement is inserted into the corresponding class-table. Otherwise, the predicate is a constraint property, and the subject-object pair of this statement, which represents the domain and range of the property, is inserted into the corresponding constraint-table. In this way, we can store all the IC-based ontology data that satisfy the number constraints. The tables according to the ontology in Fig. 1 are shown in following tables. 3. SPARQL to SQL Translation Since the tables generated in Section 2 have different structures, the SPARQL query statements to be translated are also different. In order to translate SPARQL to SQL, it is necessary to have a classification for the query statements first of all SPARQL Query Classification In this part, we aim to classify the SPARQL query statements according to different properties. Firstly, we give an overview of the SPARQL [12]. Definition 3. (Graph pattern) A graph pattern gp is defined by the following abstract grammar: gp tp gp AND gp gp OPT gp gp UNION gp gp FILTER expr.

4 X. Cui et al. /Journal of Computational Information Systems 7:2 (2011) Definition 4. (SPARQL query) A SPARQL query sparql is defined as sparql SELECT varlist WHERE (gp) where gp is the graph triple, varlist=(v 1,v 2,, v n ) is an ordered list of variables and v i is the variable that appear in gp. Table 1 Table Type Instance GraduateStudent4 GraduateStudent12 Course18 Course32 FullProfessor1 University184 Class GraduateStudent GraduateStudent Course Course FullProfessor University Table 2 Constraint-table TakesCourse Sub Graduatestudent4 Graduatestudent4 Graduatestudent12 Graduatestudent12 Graduatestudent17 Graduatestudent17 Graduatestudent17 Obj Course18 Course56 Course15 Course28 Course20 Course32 Course54 Table 3 Class-Table GraduateStudent Id Name Telephone Address MemberOf Advisor Graduate Student4 Graduate Student Graduate Student GraduateStudent4@Depart AssistantProf Department0 ment0.university0.edu essor4 GraduateStudent12@Depa FullProfessor Department1 rtment1.university0.edu 1 GraduateStudent17@Depa AssociatePro Department3 rtment3.university0.edu fessor2 TeachingA ssistantof Course12 null Course52 Undergradu atedegreef rom University6 40 University1 84 University6 17 In this paper, we only consider the SPARQL queries which contain the basic graph patterns (BGP). BGP are sets of triple patterns only include AND operation. For the SPARQL query statements contain the operators, like OPTION, UNION, FILTER, we decompose it into a number of BGP and matching it. In further, for the set of matching result, do the algebra operation and get the final results [13]. Since the tables generated by IC-based storage are partitioned by different properties, we classify the query statements by the different properties. To increase the ease of classification, we give the following definition. Definition 5. (t-triple) For each triple pattern of graph pattern in SPARQL tp, if the predicate of the triple is type, we call the triple t-triple.

5 398 X. Cui et al. /Journal of Computational Information Systems 7:2 (2011) Definition 6. (c-triple and n-triple) For each triple pattern of graph pattern in SPARQL tp, if the predicate of the triple is constraint (/normal) property, we call the triple c-triple (n-triple). The t-triple?x type C states that the variable x is the instance of C, and all the instances of C are stored in the class table C, so we can query the information about x in class-table C. The c-triple is used to determine the constraint-table to query. Let SPAQ be the set of SPARQL query statements, tp is a triple pattern with the form of (sp, pp, op), for each SPARQL query statement, if gp only include t-triple, call it SPAQ T, if gp only include t-triple and n-triple, call it SPAQ N, if gp only include t-triple and c-triple, call it SPAQ C, else call it SPAQ M.. The query statements SPAQ T are used in type-table, SPAQ N in the class-table, SPAQ C in constraint-table and SPAQ M in all of the tables. The classification process is as follows: Step1. Initialize SPAQ = {SPAQ T, SPAQ C, SPAQ N, SPAQ M } is null, count C and count N present the number of constraint property Prop C and normal property Prop N, and count C =0, count N =0. Step2. Traverse all the gp in the SPARQL query statements. Step3. For each SPARQL statement spaqi SPAQ, determine the categories: for each triple pattern tp spaq determine the type of triples, and count the number of the corresponding j i predicates: for the predicate of tp i, if pp Prop C, count C = count C +1; if pp Prop N count N = count N +1 Step4. Divide the different SPARQL query statements. if count C == 0 and count N == 0 SPAQT:= SPAQ spaq T { i}; if count C == 0 and count N!= 0 SPAQ := spaq N SPAQN { i} ; if count C!= 0 and count N == 0 SPAQ : = SPAQ { spaqi} C C ; if count C!= 0 and count N!= 0 SPAQ : = SPAQ M { spaq i } M. The examples of different types of SPARQL query statements which also from LUBM [11]. In some cases the queries were slightly changed in order to eliminate unsupported elements of SPARQL s syntax. The queries below are shown in an abbreviated form (all prefixes are stripped out) Translation Strategy In this section, according to the different SPARQL query statements, we present the translations of SPARQL to SQL. There are four types of translations: the translation of SPAQ T, SPAQ N, SPAQ C and SPAQ M, respectively. Since the translation is at the basis of the IC-based storage, we call it IC-based translation. In the following, we give the translations of the four types of SPARQL queries. Translation of SPAQ T. Since the statement only contains t-triples, all the information to query is in the type-table, put type as the query table name to the FROM statement. If the subject of t-triple sp is variable, put instance as the query variable to SELECT statements, else put instance=sp as the query condition to the WHERE statement. If the object of t-triple op is variable, the attribute class as the query variable to SELECT statements, else put class=sp as the query condition to the WHERE statement.

6 X. Cui et al. /Journal of Computational Information Systems 7:2 (2011) Query 1 SELECT?X WHERE {?X type GraduateStudent} (a) SPAQ T Query 2 SELECT?X,?Y1,?Y2,?Y3 WHERE {?X type GraduateStudent.?X memberof < name?y1.?x address?y2.?x telephone?y3} (b) SPAQ N Query 3 SELECT?X,?Y WHERE {?X type GraduateStudent.?Y type Course.?X takescourse?y. < teacherof?y} (c) SPAQ C Query 4 SELECT?X,?Y,?Z WHERE {?X type GraduateStudent.?Y type Faculty.?Z type Course.?X advisor?y.?y teacherof?z.?x takescourse?z} (d) SPAQ M Fig. 2 the Classified SPARQL Statements If there are more than one t-triple, the inner operation is needed and the table names and variable names should be renamed. We consider the same variables exist between t-triples. Since an instance belongs to only one class, variables of between the two triples are different. Therefore, we only need directly join the two tables. The translated SQL statement corresponding to Fig. 2(a) is SELECT instance FROM type WHERE class = GraduateStudent Translation of SPAQ N. We consider the object of t-triple op in this part is known beforehand; otherwise, we can get it advance from type-table like in SPAQ T. For each t-triple appear in gp, if the subject of t-triple sp is variable, put op as the query table name to the FROM statement, and put id as the query variable to SELECT statements. For each n-triple appears in gp, the predicate of n-triple pp represents the attribute name of the table in the relational database. If op is variable, put the pp as the query variable to SELECT statement, else put pp=op as the query condition to the WHERE statement. If the gp contains more than one t-triple, the inner operation is needed the table names and variable names should be renamed. We consider the same variables exist between triples. The same as SPAQ T, it is impossible that the same variables appear in t-triples. For the variables appear in between t-triple and n-triple, delete the predicate of t-triple pp from FROM statement, because we can query the variable of this t-triple according to the n-triple. The translated SQL statement corresponding to Fig. 2(b) is

7 400 X. Cui et al. /Journal of Computational Information Systems 7:2 (2011) SELECT id, name, address, telephone FROM GraduateStudent WHERE memberof = < Translation of SPAQ C. The constraint table name is the constraint property name, so if the predicate of triple pp is constraint property, it is the corresponding constraint table name, put pp as the table name in the FROM statement. Obviously, the t-triple is not a constraint property, so we skip the t-triples. Then we consider the c-triple. The subject of c-triple sp and object of c-triple op are the two attributes of the constraint table. If sp is a variable, put sub as the query variable to the SELECT statements, else put sub=sp as the query condition to the WHERE statement. If op is variable, put obj as the query variable to the SELECT statement, else put obj=op as the query condition to the WHERE statement. If the gp contains more than one c-triple, the inner operation is needed and the table names and variable names should be renamed. After that, if there exists the same variable between t-triples, put t i.sub=t j.sub (/t i.sub=t j.obj/ t i.obj=t j.obj/t i.obj=t j.sub) as the query condition to the WHERE statement, where t i, t j represent the distinguish table names. The translated SQL statement corresponding to Fig. 2(c) is SELECT t 1.sub, t 2.obj FROM takescourse as t 1, teacherof as t 2 WHERE t 1.obj = t 2.obj, t 2.sub = < Translation of SPAQ M. In the following, we give the translation of SPAQ M. Firstly, we do the translation of SPAQ N and SPAQ C like proposed above respectively. After that, we combine the two translations as follows: If there exists the same variable between c-triples and t-triples, put t i.sub=t j.pp (/t i.obj=t j.pp) as the query condition to the WHERE statement. After that if the gp contains more than one c-triple, the inner operation is needed and the table names should be renamed. If the gp contains more than one c-triple and t-triple, the inner operation is needed and the table names should be renamed. If there exists the same variable between t-triples, put t i.sub=t j.sub (/t i.sub=t j.obj/t i.obj=t j.obj/ t i.obj=t j.sub) as the query condition to the WHERE statement, where t i, t j represent the distinguish table names. The translated SQL statement corresponding to Fig. 2(d) is SELECT t 1.id, t 1.advisor, t 2.sub FROM GraduateStudent as t 1, teacherof as t 2, takescourse t 3 WHERE t 1.id = t 3.sub, t 1.advisor = t 2.obj, t 3.obj = t 2.obj. 4. Evaluation All experiments were run underlying the following environments: - 1.9GHz AMD Athlon 64 X2 Dual Core Processor CPU; GB of RAM: 150GB of hard disk; Windows XP Professional OS; Java SDK 3.2.2; MySQL 5.0. Experiment. The result of executing the 4 queries evolved in Fig. 2 is shown in Fig. 3. The horizontal axis represents the different queries and the vertical axis represents the query times for different query and different scalability of dataset. Fig. 3 indicates that with the increase of the dataset, the efficiency gap of SPAQ C and SPAQ M is very obvious, while the one of SPAQ T and SPAQ N are in verse. Therefore, our method is more appropriate to the data with the properties of the instances of the same class are similar.

8 X. Cui et al. /Journal of Computational Information Systems 7:2 (2011) Query times(ms) M 2.5 M 8.5 M 25. 5M 1 SPQT SPQN SPQC SPQM Query Statements Fig.3 Query Times with Different Dataset 5. Conclusions Motivated by the traditional ontology not considering the integrity of the ontology, which confusing the data incompleteness, we have added the integrity constraint to the ontology, generated the IC-based ontology and further store the ontology to the relational database. Then, we have proposed IC-based translation algorithms to implement each SPARQL generate efficient SQL queries. The SPARQL queries have been divided into four types-spaq T, SPAQ N, SPAQ C, SPAQ N, and according to different type of query statements, we have proposed the corresponding translation. Acknowledgement This work was supported in part by NSFC under Grant Nos , and ; Jilin Province Science and Technology Development Plan under Grant Nos , Erasmus Mundus External Cooperation Window's Project (EMECW): Bridging the Gap, EM IT-ERAMUNDUS-ECW-L12. The Foundation of Key Laboratory of SCKE of Ministry of Education under Grant No References [1] W3C, Resource description framework (RDF): concepts and abstract syntax, in: G. Klyne, J.J. Carroll, B. McBride (Eds.), W3C Recommendation[S], 10 February < />. [2] W3C, SPARQL query language for RDF, in: E. Prud hommeaux, A. Seaborne (Eds.), W3C Recommendation[S], 15 January < -query />. [3] K. Wilkinson, C. Sayers, H. Kuno, D. Reynolds. Efficient RDF storage and retrieval in Jena2[C]. Proceedings of the International Workshop on Semantic Web and Databases (SWDB), 2003, [4] J. Broekstra, A. Kampman, F. van Harmelen. Sesame: a generic architecture for storing and querying RDF and RDF Schema[C]. Proceedings of the International Semantic Web Conference (ISWC), 2002, [5] S. Harris, N. Gibbins, 3store: efficient bulk RDF storage[c]. Proceedings the International Workshop on Practical and Scalable Semantic Systems (PSSS),2003, [6] R. Cyganiak. A relational algebra for SPARQL[R]. HP Laboratories Bristol. HPL , [7] B. Elliott, E. Cheng, C.T. Ogbuji, Z. Meral Ozsoyoglu. A complete Translation from SPARQL into Efficient SQL[C]. International Database Engineering and Application Symposium(IDEAS 2009), 2009, [8] Artem Chebotko, Shiyong Lu, Farshad Fotouhi. Semantic preserving SPARQL-to-SQL translation[j]. Data and

9 402 X. Cui et al. /Journal of Computational Information Systems 7:2 (2011) Knowledge Engineering, 2009,68(10): [9] X.J. Cui, D.T. Ouyang, Y.X. Ye. Persistent Storage of Ontology-based data with Integrity Constraint[C]. Proceedings of 5th International Conference on Frontier of Computer Science and Technology (FCST 2010). Aug 18-22, Changchun, China, 2010: [10] B. Motik, I., Horroks, and U., Sattler, Bridging the gap between OWL and relational databases[j]. Journal of Web Semantics, 2009,7: [11] Y. Guo, Z. Pan, and J. Heflin. LUBM: A benchmark for OWL knowledge base systems[j]. Journal of Web Semantics (WS), 2005,3(2-3): [12] J. Perez, M. Arenas, C. Gutierrez, Semantics and complexity of SPARQL[C]. Proceedings of the International Semantic Web Conference (ISWC), 2006, [13] R. Angles, C.Gutierrez. The Expressive Power of SPARQL [C] The Semantic Web ISWC 2008: 7th International Semantic Web Conference, Berlin Heidelberg: Springer-Verlag, 2008,