Essay on SPARQL and SPARQL Editor

SPARQL²³ is an RDF query language used to probe data from semantic network possessions. It permits operators to recover and employ data that was deposited in RDF or OWL format. RDF data format (configuration measure comprising of matter, establish and purpose) has to utilized the prototypical information that can be employed on the resources of web. SPARQL query provides a momentous apparatus for answering the problem of the manipulators and it will also give the permission to write requests about data from RDF contented of ontology. SPARQL sanctions four kinds of forms of  request, namely, SELECT, CONSTRUCT, DESCRIBE, and ASK (Harris and Seaborne, 2013), as follows;

·         TOP QUALITY proclamations and outcomes pf this can be received as the variables of interrogation can be mentioned in the form.

·         CONSTRACT testimonials can be presented on the graph and can be mentioned on the thousands of templets.

·         DESIGNATE forms the possible ways of outcomes can be presented on the graph of RDF

·         ASK this type of inquiry would be give the  result as in the form of right and wrong for a demand on a last point of SPARQL (Allemang and Hendler, 2011)

SPARQL had been presented as the outstanding technique and being famous on the world wide web (W3C) since January 2008. The definition of data set can be described as the ways on the graphs with the BEGINNING division and it may be called as ON OR AFTER ENTITLED passage. The boundaries are another method that can be defined as the part that expresses the equivalent outlines for possessions, as well as it also provides the assets in increases with the WHERE clause.  The elementary arrangement of a SPARQL probe customsmultiple configurations that contain the exact inconstant in a WHERE clause. It also delivers a chain ({}) for by means of a wedge of multi designed (more than one triple pattern), and a solitary blotch (.) divides the triple patterns in a bracelet. In this way, a more complex expression is possible in a triple pattern, i.e. SPARQL allows the retrieval of complex queries. The following figure x shows a very simple snippet of the SPARQL query using the protégé editor with the built-in SPARQL Query tab. The query is retrieving data from the smart- SCCS ontology implemented about a SEND student that has an EHCP and receives a social care package.

5.6.2   DL Queries and DL Query Editor

The Description Logic (DL) inquiry is a method that can be used to define the language or grammar. That can be recycled to square the reliability for all demarcated individuals (classes and individuals) within the ontology model. DL interrogations can be developed to made the rational to accomplish the categorization automatic to describe the connections (i.e. property assertions) that can be discuss and can be employed in the ontology. As it can be discussed in the study of ontology that how to calculate the uses the investigation of DL and there establish the contingent modules for the customers within the Smart- SCCS branch of ontology. The enquiries were executed to patterned that all individuals were accurately elaborates the importance of ontology has been right, and tumbles within the widespread constraint of reliability and they can be defined as the consequence. Subsequently, there are no peccadillo of data or repeatable disputing unearthing. DL interrogations perform instinctive arrangement and, or repossession of the development individuals within the ontology. In OWL reasoning, many tasks correspond to standard DL reasoning tasks. Such as checking the semantic knowledge consistency or determine whether the individuals in knowledgebase do not violate descriptions andaxioms described by the developed ontology. However, more tasks can be achieved by the DL reasoning and querying process as described below (Bock et al., 2008);

·         Checking the satisfaction related to the concept and this can be attained through the depiction of the model and it has incongruous or an distinct can exist that can be used as anexample of the impression.

·         Checking the concepts’ and assumptions of the concept can be obtained despite from this notion X considersconcept Y or whether the explanation of class X is more general than thedescription of Y.

·         Checking whether the separable is an instance of a concept without violating thedescriptions of the concept.

·         Individuals classification by retrieving a property filler according to some constraintson relationships between individuals’ classes.

In the Protégé-OWL development editor (versions four and above), the (DL Query tab)²⁴  comes as a standard distribution,  both as a tab and as a view widget that can be placed into any other tab. It provides a powerful and easy-to-use feature for searching a classified ontology. The query language (class expression) supported by the plugin is based on the Manchester OWL syntax. A user-friendly syntax for OWL DL that is fundamentally based on collecting all information about a particular class, property, or individual into a single construct, called a frame. The DL query is employed in this research performs simple queries to check the consistency of assorted and inferred classes and associated individuals. The DL query tab shown in figure x below provides an interface for querying and searching the developed ontology. The ontology must be classified by a reasoner (as mentioned above) before the DL query can be executed in the query tab.

5.7 Rule Engines of Smart-SCCS Ontology Modelling

The systematic web defines some traditional rule as well as unconfirmed proclamations, if-then sections. By usingthese divisions, and latest information will also be included regarding to the specific set of testimonials that can be defined as perfect. Thesedomains encompass the systematic and significant performance of the operations that has been presented as the set of rules related to languagesor formats. Not only do rule languages allow describing relations that would not be elaborated as thelanguage usage of OWL ontology, but also they allow distribution and reusing prevailing rules on the Web.

Rule-based reasoning can profitable for the rules of language, which allows the data to corporate with the ontology and it will also describe the difference between retailors and consumers. Supplies of regulation in the linguistic for the systematic processing of web that  involves thearticulateness, imperative transaction, rule amalgamation, and the linguistic incorporation as well as the compatibility with various webs  of systematic standards (Rattanasawad et al., 2018). Rule-based reasoners apply rules with data to reason and find out the latest features. When the dataresembles with  rules’ surroundings, the reasoners can amend the basics for acknowledgement; for example, forinformational proclamation or withdrawal, or to implement occupations. It is good practice to construct rulesfrom concepts included in the ontology. In this way ontology design is the first and necessarystep in the actionable knowledge construction process.Adages may contain of RDF, OWL and rule truisms. A relation can be a URI, a data range,an OWL property or a built-in relation. An object can be a variable, an individual, a literalvalue or a blank node. Additionally, the rule language delivers many sets of already existed purposes such as filament gatherings as well as scientific functions (Rattanasawad et al., 2018).

In this research, the rule machine stage of the developed knowledge base comprises on the   systematic reasoner (Pellet, described in section 5.7.x) and semantic rule language (SWRL). These scientific rules can be used to evaluate the reliability of the interlimk between the modules of categorization as well their properties. While the rules of SWRL has been articulated in terms of OWL thoughts to purpose about OWL entities, mainly in the terms of OWL courses and possessions. When a SWRL rule would be dismissed, it will buil the latest categorization for individuals who were existed. an example of this can be discussed, if the set of rules would be result in as avows a person who was not be qualified as the member on a  specific models, that bt despite from those it os necessary for a person to reaon the member of the OWL whenever  the rule excitements. Similarly, if a SWRL rule asserts that two individuals are related via a particular property, then that property must be associated with each individual that satisfies the rule.

5.7.1    SWRL of Smart-SCCS Ontology Modelling

There are two kinds of knowledge in the knowledge-base system:  static  (domain)  knowledge, represented by ontologies, and dynamic  (inference)  knowledge, dealing with the reasoning process and represented by rules  (Wang et al., 2012).   The present knowledge-base system was developed based on OWL  ontology and  SWRL.  The main reasons for selecting  SWRL  to represent rule knowledge is because of the advantage of its formal model-theoretic semantics. And its close association with  OWL to facilitate the incorporation of the  OWL-based configuration ontology into the rules (Li et al., 2012). Recent work has concentrated on adding rules to OWL to provide an additional layer of expressivity(Horrocks et al., 2014). Subsequently, based on structural knowledge which has been modelled in  OWL, rule knowledge in the particular domain is expressed in  SWRL.  This enables users to create horn-like rules expressed in OWL concepts to infer new knowledge from the existing OWL  individuals (Qiyan, Feng and Hu, 2010).   SWRL rules are written as antecedent consequent pairs. In SWRL terminology, the antecedent is referred to as the rule body, and the consequent is referred to as the head. The head and body consist of a conjunction of one or more atoms. As a result of the execution of   SWRL   rules, newly deduced knowledge will be automatically added to the knowledgebase. Interoperation between OWL  and  SWRL occurs not only semantically and syntactically, but also inferentially.  This means that it is not sufficient to be able to create SWRL rules in OWL which can use the vocabulary and OWL   ontology;   rather,  a vital requirement is to conduct reasoning in a semantically consistent way.   This further indicates the value of exploiting both rule-based knowledge and the ontology to draw inferences.  A combination of OWL  and  SWRL, thus affords powerful inferential reasoning capabilities. The actual configuration reasoning process was then executed using these two components,  with the support of the Pellet rule engine, as explained in section 5.x .