Enterprise Knowledge Management in Semantic Web

Transcription

1 Enterprise Knowledge Management in Semantic Web Liviana Tudor Department of Information Technology, Mathematics, Physics Petroleum-Gas University of Ploiesti, Romania Abstract This article presents an enterprise knowledge management model in a Semantic Web application and performs a comparative analysis of the ways to optimize access to semantic data. In the Semantic Web, inference enables performing queries that make semantic matching based on meaningful relationships among pieces of data. The case study presents a Semantic Web application managed using the RDF data model and a relational database. The performance of the semantic model is emphasized through the study of the inference methods and of the parallel execution parameters for the queries on the analyzed RDF graph. Keywords: Web semantic, inference, relational database, knowledge management, query performance Introduction The semantic technology for databases allows the implementation of artificial intelligence techniques in the relational database theory. The semantic technology is operated using concepts such as knowledge base, rules, inference, ontology, which allow the integration of semantic data in the relational model of the databases. In this article, we will describe a semantic model used to manage knowledge in a Web application for an online magazine and we will perform a comparative analysis of the optimization techniques for the semantic queries. The support for the semantic technologies will be provided by the Resource Description Framework (RDF) model. In W3C RDF (2004), the RDF is presented as a language for representing information about resources in the World Wide Web. By generalizing the concept of a Web resource, RDF can be used to represent information that can be identified on the Web, even when they cannot be directly retrieved on the Web. The article is organized in the following sections. The section Semantic data modeling presents the basics on semantic data modeling and the way to implement it in Oracle databases. The representation of the semantic data uses the RDF data model, and the data query uses the rule-based inference. The section Case study: semantic data for journal article presents a semantic data management model used for an online magazine containing scientific articles. The section Performance of the semantic model makes a performance analysis of the previously described semantic model, by comparing the query optimization techniques. The last section presents the final conclusions and underlines the contributions of this article.

2 Semantic data modeling The semantic technology ensures semantic data storage and the query of ontologies, and the inference extends the power of the semantic queries. For example, an Oracle database may contain semantic data, ontologies (RDF/OWL models) and traditional relational data. RDF data model RDF data model is based on identifying information using Web identifiers, called Uniform Resource Identifiers (URIs), and describing resources in terms of simple properties and property values. This enables RDF to represent simple statements about resources as a graph of nodes and arcs representing the resources, and their properties and values. In W3C RDF Primer (2004), RDF statements are similar to a number of other formats for recording information, such as: entries in a simple record describing the resource in a data processing system, rows in a simple relational database, and simple assertions in formal logic. Murray et al. (2012) said that semantic data in Oracle database is modeled using a directed graph. The metadata statements are represented as triples: nodes are used to represent two parts of the triple, and the third part is represented by a directed link that describes the relationship between the nodes. Oracle statements are expressed in triples, where each triple is stored in a semantic data network and can be represented by a link in a directed graph: (<subject resource>, <predicate property>, <object value>). (1) Figure 1 illustrates the functionality of the semantic technologies in relational databases, the data storage capabilities, the logical inference and querying of RDF data, ontologies and enterprise data. RDF data and ontologies Enterprise data Relational Database STORE data INFERENCE QUERY Fig 1. Semantic technologies Inference: rules and rulebases Tudor (2012) has mentioned that, with declarative programming, the inference is a method to process pieces of knowledge in a knowledge base and to generate rule-based arguments. Through analogy to a relational database, it can be stated that the inference improves the performance of semantic queries, through logical deductions based on semantic data and rules. In a relational database, the inference enables performing queries that make semantic matching based on meaningful relationships among pieces of data, as opposed to just syntactic matching based on string or values. Inferencing involves the use of rules placed in rulebases. Murray et al. (2012) said that a rule is an object that can be applied to draw inferences from semantic data and consists of an IF side pattern for the antecedents, an optional filter condition that further restricts the subgraphs matched by the IF side pattern, and a THEN side pattern for the consequents. For example, the rule a manager of a journal is also a reviewer of the journal could be represented formally as follows: 74

3 Rule manager_rule IF r = manager (journal) THEN r = reviewer (journal) ENDIF Figure 2 shows triple sets being inferred from model data and the application of rules in one or more rulebases. The database can have semantic models, rulebases, and inferred triple sets, and an inferred triple set can be derived using rules in one or more rulebases. Model 1 Triple set Model n Triple set n Fig 2. Inference The RDFS rulebase implements the RDFS entailment rules, as described in the W3C RDF Semantics (2004). An entailment (rules index) is an object containing precomputed triples that can be inferred from applying a specified set of rulebases to a specified set of models. An entailment stores triples derived via inferencing. Case study: semantic data for journal article Rulebase 1 Rulebase n This section presents a simplified semantic model made in PL/SQL Oracle for the management of knowledge in an electronic magazine. A semantic model holds an RDF graph (set of (<subject>, <predicate>, <object>) triples) and is associated with an SDO_RDF_TRIPLE_S column in an application table. The development of a Semantic Web application in PL/SQL involves the creation of a semantic data network in order to add semantic support to the Oracle database. The 'articles' semantic data model uses an 'articles_rdf_data' table, which memorizes references to the model data. The semantic relations between the articles published in the electronic magazine can be represented through the nodes and arcs of the RDF graph associated to the application. Figure 3 describes a few pieces of information used in the semantic model Goasdoué, F., Manolescu, I., Roatis, A. Foundations of RDF databases Arenas, M., Gutierrez, C., Pérez, J. Efficient Query Answering against Dynamic RDF Databases Fig 3. RDF graph 75

4 Article1 has the title Efficient Query Answering against Dynamic RDF Databases ; article1 was written by Goasdoué, F., Manolescu, I., Roatis, A.; article1 refers to article2; article2 has the title Foundations of RDF databases ; and article2 was created by Arenas, M., Gutierrez, C., Pérez, J. The partial image of the RDF graph associated to the Semantic Web application contains the following triples expressed in the form (1): ( 'Efficient Query Answering against Dynamic RDF Databases') ( 'Goasdoué, F., Manolescu, Roatis') ( ( Foundations of RDF databases ) ( Arenas, M., Gutierrez., Pérez, J. ) The insertion operation of the semantic data in the table does the correspondence in PL/SQL between (<subject>, <predicate>, <object>) and a triple of the RDF graph: SDO_RDF_TRIPLE_S ('articles', ' ' ' Efficient Query Answering against Dynamic RDF Databases ') Semantic data querying before making the inference only offers a simplified image of the results, due to the fact that it does not have the semantic connections performed between the RDF graph entities. A rule-based inference is made in order to have complete query results. A PL/SQL inference example creating an 'articles_rb' rule base and the rule 'manager_rule' is the following: EXECUTE SEM_APIS.CREATE_RULEBASE('articles_rb'); INSERT INTO mdsys.semr_articles_rb VALUES( 'manager_rule', '(?r : manager?journal)', NULL, '(?r : reviewer?journal)', SEM_ALIASES(SEM_ALIAS('',' The creation of entailment allows refreshing and indexing the rules in the semantic model: SEM_APIS.CREATE_ENTAILMENT( 'rdfs_rix_articles', SEM_Models('articles'), SEM_Rulebases('RDFS','articles_rb')); The semantic data queries allow the information memorized in the semantic model and logically deducted in the inference process to be obtained. SELECT x, y FROM TABLE(SEM_MATCH('{?x : reviewer?y}', SEM_Models('articles'), SEM_Rulebases('RDFS','articles_rb'), SEM_ALIASES(SEM_ALIAS('',' The performance of the semantic model Performing sufficient statistics for the query optimizer is critical for good query performance. Due to the inherent flexibility of the RDF data model, static information may not produce optimal execution plans for semantic queries, however dynamic sampling can often produce much better query execution plans. 76

5 At the same time, the performance of semantic queries can be improved with the help of indexes associated to the semantic network. Lopez et al. (2010) mentioned that some B-tree indexes (including one for enforcing uniqueness constraint) are created automatically at semantic network creation time. Data type indexes may significantly improve the performance of SEM_MATCH queries involving certain types of FILTER expressions. We can investigate two query answering techniques against semantic data: saturation- and reformulation-based. In the article by Goasdoué et al. (2013), the saturation of the database is computed using the allowed entailment rules. The answer set of every query against the database is obtained by query evaluation against the saturation. Reformulation-based query answering reformulates a query Q 1 into another query Q 2 using the immediate entailment rules. Reformulationbased query answering in the RDF graph has been investigated for relational conjunctive queries, while the slight extension thereof has been investigated for SPARQL queries, in studies of Arenas et al (2009), Goasdoué et al (2011). To analyze semantic queries, an RDF graph with 30 triples is used and memorized in an Oracle 11g database. A set of 10 queries is analyzed in terms of the number of results returned and the processing time (in seconds), as described in table 1. A comparative study of the parallel execution method for the set of queries analyzed is emphasized in figure 4. Table 1: Query performance Query Q 1 Q 2 Q 3 Q 4 Q 5 Q 6 Q 7 Q 8 Q 9 Q 10 Answers Time (sec) Parallel query execution Queries Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Answers / Time Answ ers Time (sec) Fig 4. Parallel query performance For best inference performance, we can use incremental or parallel inference. Dynamic incremental inference selectively applies semi-naive rule evaluation while generating the entailment, according to Lopez et al. (2010). Inference performance is demonstrated by evaluating the time to finish inference (Table 2). Table 2: Inference performance Inference Time to finish inference (sec.) Parallel inference 1.36 Incremental inference 0.28 The comparison of the time required by the two types of inferences, for the analyzed RDF graph, is illustrated in figure 5. 77

6 Time to finish inference (sec.) 1.5 Time (sec.) Parallel inference Incremental inference Fig: 5. Inference performance A comparative analysis of the optimization techniques for the semantic queries emphasizes the significant influence of the dynamic incremental inference method and the use of data type indexes. The advantage of reformulation based query answering is that the database saturation does not need to be (re)computed. The disadvantage is that every incoming query must be reformulated, which often results in a more complex query. Conclusions The article portrays an enterprise data management model from a semantic network associated to a relational database. The case study describes an Oracle Semantic Web application for an online magazine and the main operations to create a rule base, inference, data querying. The semantic model performance evaluation is done from the inference and querying methods points of view. References Arenas, M., Gutierrez, C. and Pérez, J. (2009), Web Reasoning Web. Semantic Technologies for Information Systems, Springer-Verlag Berlin, Heidelberg. Goasdoué, F., Manolescu, I. and Roatis, A. (2013), Efficient Query Answering against Dynamic RDF Databases Proceedings of the 16th International Conference on Extending Database Technology (EDBT), ISBN: , March 2013, Genoa, Italy, Goasdoué, F., Karanasos, K., Leblay, J. and Manolescu, I. (2011), View selection in semantic web databases, Proceedings of the VLDB Endowment (PVLDB), 5 (2), Lopez, X. and Das, S. (2010), Semantic Technologies in Oracle Database 11g Release 2: Capabilities, Interfaces, Performance Oracle Documentation. [Online], [Retrieved January 15, 2014] Murray, C., Chong, E. I., Das, S., Kolovski, V., Perry, M., Srinivasan, J., Sundara, S., Wu, Z. A. and Yalamanchi, A. (2014), Oracle Database Semantic Technologies Developer's Guide, 11g Release 2 (11.2) Oracle Documentation. [Online], [Retrieved January 16, 2014] Tudor, N. L. (2012) Logic Programming and expert systems. Visual Prolog and ExSys applications (in Romanian), MATRIX ROM Publishing House Bucharest. W3C Working Group, (2014), RDF Primer, World Wide Web Consortium (W3C). [Online], [Retrieved February 25, 2014] 78