Rule Markup Language Research Articles

A Tool for Transforming Semantic Web Rule Language to SPARQL Infererecing Notation

Semantic web rule language (SWRL) combines web ontology language (OWL) ontologies with horn logic rules of the rule markup language (RuleML) family. Being supported by ontology editors, rule engines and ontology reasoners, it has become a very popular choice for developing rule-based applications on top of ontologies. However, SWRL is probably not going to become a WWW Consortium standard, prohibiting industrial acceptance. On the other hand, SPARQL Inferencing Notation (SPIN) has become a de-facto industry standard to represent SPARQL rules and constraints on semantic web models, building on the widespread acceptance of SPARQL (SPARQL Protocol and RDF Query Language). In this article, we argue that the life of existing SWRL rule-based ontology applications can be prolonged by converting them to SPIN. To this end, we have developed the SWRL2SPIN tool in Prolog that transforms SWRL rules into SPIN rules, considering the object-orientation of SPIN, i.e. linking rules to the appropriate ontology classes and optimizing them, as derived by analysing the rule conditions.

A knowledge-based method for the automatic determination of hydrological model structures

Abstract To determine a suitable hydrological model structure for a specific application context using integrated modelling frameworks, modellers usually need to manually select the required hydrological processes, identify the appropriate algorithm for each process, and couple the algorithms' software components. However, these modelling steps are difficult and require corresponding knowledge. It is not easy for modellers to master all of the required knowledge. To alleviate this problem, a knowledge-based method is proposed to automatically determine hydrological model structures. First, modelling knowledge for process selection, algorithm identification, and component coupling is formalized in the formats of the Rule Markup Language (RuleML) and Resource Description Framework (RDF). Second, the formalized knowledge is applied to an inference engine to determine model structures. The method is applied to three hypothetical experiments and a real experiment. These experiments show how the knowledge-based method could support modellers in determining suitable model structures. The proposed method has the potential to reduce the knowledge burden on modellers and would be conducive to the promotion of integrated modelling frameworks.

A formal method for rule analysis and validation in distributed data aggregation service

The usage of Cloud Serviced has increased rapidly in the last years. Data management systems, behind any Cloud Service, are a major concern when it comes to scalability, flexibility and reliability due to being implemented in a distributed way. A Distributed Data Aggregation Service relying on a storage system meets these demands and serves as a repository back-end for complex analysis and automatic mining of any type of data. In this paper we continue our previous work on data management in Cloud storage. We present a formal approach to express retrieval and aggregation rules with a compact, yet powerful tool called Rule Markup Language. Our extended solution proposes a standard form to schemes and uses the tool to match the rules to the XML form of the structured data in order to obtain the unstructured entries from BlobSeer data storage system. This allows the Distributed Data Aggregation Service (DDAS) to bypass several steps when processing a retrieval request. Our new architecture is more loosely-coupled with a separate module, the new tool, used for transforming the XML entries to standard XML files which represent the final result. We model the dynamic behavior of the system using this new standard to ensure a simpler and efficient representation of the operations performed by the client while maintaining the constraints imposed by a distributed system running in the Cloud. Furthermore we prove that this method correctly performs the translation between the storage model's unstructured view of data and the client's structured objects.

Ontological user modelling and semantic rule-based reasoning for personalisation of Help-On-Demand services in pervasive environments

Existing context-aware applications are limited in their support of user personalisation. Nevertheless, the increase in the use of context-aware technologies has sparked the growth in assistive applications resulting in a need to enable adaptation to reflect the changes in user behaviours. This paper introduces a systematic approach to service personalisation for mobile users in pervasive environments and presents a service-oriented distributed system architecture. The developed approach makes use of semantic technologies for user modelling and personalisation reasoning. In the paper we characterise user behaviours and needs in pervasive environments upon which ontological user models are created with special emphasis being placed on ontological modelling of dynamic and adaptive user profiles. We develop a rule-based personalisation mechanism that exploits semantic web rule mark-up language for rule design and a combination of semantic and rule-based reasoning for personalisation. We use two case studies focusing on providing personalised travel assistance for people using Help-on-Demand services deployed on a smart-phone to contextualise the discussions within the paper. The proposed approach is implemented in a prototype system, which includes Help-on-Demand services, content management services, user models and personalisation mechanisms in addition to application specific rules. Experiments have been designed and conducted to test and evaluate the approach with initial results demonstrating the functionality of the approach.

Rule Interchange in the Semantic Web

Rule interchange has become one of the most important issues in the Semantic Web. As a recommendation of the W3C (WWW Consortium), RIF (Rule Interchange Format) is dedicated to rule interchange between different rule languages. To be a general rule interchange format, RIF should first support rule interchange with SWRL (Semantic Web Rule Language), RuleML (Rule Markup Language), R2ML (REWERSE Rule Markup Language) and F-logic (Frame Logic), before completely implementing rule interchange with other rule languages. In the paper, we propose and construct RIA(subscript XML) (Rule Interchange Architecture based on XML Syntaxes) - a rule interchange structure centered on RIF, which enables bidirectional rule interchange between RIF and SWRL, RuleML, R2ML and F-logic. Furthermore, we explore the issue of information loss and present corresponding remedial measures. More importantly, we design and implement a prototype system of RIA(subscript XML), and give its experimental analysis.

Rule-based personalized comparison shopping including delivery cost

Comparison shopping allows customers to reduce time and effort when searching for product information and prices. However, traditional comparison sites mainly compare product prices without using precise information on delivery cost. To overcome this limitation, we adopted a rule-based comparison shopping framework using the eXtensible Rule Markup Language (XRML) architecture, which computes the exact personalized delivery cost at comparison sites. The prototype ConsiderD, which was developed for this purpose, computes the exact delivery costs considering the shipping rules, destination, delivery speed, and shipping rates. The XRML architecture effectively maintains the consistency of formal rules with the original Web pages. To demonstrate the performance of rule-based comparisons, we conducted an experiment on the purchase of books based on real-world data from five leading online bookstores. This experiment shows that rule-based comparison can significantly outperform data-based comparison in terms of the total cost of product and delivery. We also found that the comparison of delivery cost is very important because the variance of delivery cost can be as big as the variance of book prices itself.

Neurosymbolic Alerting Rules

Future building automation will require complex (humanlike) perception and decision-making processes not being feasible with classical approaches. In this paper, we address both the perception and the decision-making process and present an alerting model that reacts to perceived situations in a building with decisions about possible alerts. Perception is based on the neurosymbolic information-processing model, which detects candidate alerts. Integrated with perception, decision making is based on the rule model of the Rule Markup Language, which computes alerts to relevant building occupants about current opportunities and risks. A general model of neurosymbolic alerting rules is developed and exemplified with a use case of building alerts.

Modeling languages for business processes and business rules: A representational analysis

Process modeling and rule modeling languages are both used to document organizational policies and procedures. To date, their synergies and overlap are under-researched. Understanding the relationship between the two modeling types would allow organizations to maximize synergies, avoid content duplication, and thus reduce their overall modeling effort. In this paper, we use the Bunge–Wand–Weber (BWW) representation theory to compare the representation capabilities of process and rule modeling languages. We perform a representational analysis of four rule modeling specifications: The Simple Rule Markup Language (SRML), the Semantic Web Rules Language (SWRL), the Production Rule Representation (PRR), and the Semantics of Business Vocabulary and Business Rules (SBVR) specification. We compare their BWW representation capabilities with those of four popular conceptual process modeling languages. In our analysis, we focus on the aspects of maximum ontological completeness and minimum ontological overlap. The outcome of this study shows that no single language is internally complete with respect to the BWW representation model. We also show that a combination of two languages, in particular SRML and BPMN, appears to be better suited for combined process and rule modeling than any of these modeling languages used independently.

Model transformations to bridge concrete and abstract syntax of web rule languages

This paper presents a solution to bridging the abstract and concrete syntax of a Web rule languages by using model transformations. Current specifications of Web rule languages such as Semantic Web Rule Language (SWRL) or RuleML define their abstract syntax (e.g., metamodel) and concrete syntax (e.g., XML schema) separately. Although the recent research in the area of Model-Driven Engineering (MDE) demonstrates that such a separation of two types of syntax is a good practice (due to the complexity of languages), one should also have tools that check validity of rules written in a concrete syntax with respect to the abstract syntax of the rule language. In this study, we use the REWERSE I1 Rule Markup Language (R2ML), SWRL, and Object Constraint Language (OCL), whose abstract syntax is defined by using metamodeling, while their textual concrete syntax is defined by using either XML/RDF schema or Extended Backus-Naur Form (EBNF) syntax. We bridge this gap by a bi-directional transformation defined in a model transformation language (ATLAS Transformation Language, ATL). This transformation allowed us to discover a number of issues in both web rule language metamodels and their corresponding concrete syntax, and thus make them fully compatible. This solution also enables for sharing web rules between different web rule languages.

Extended RDF as a Semantic Foundation of Rule Markup Languages

Ontologies and automated reasoning are the building blocks of the Semantic Web initiative. Derivation rules can be included in an ontology to define derived concepts, based on base concepts. For example, rules allow to define the extension of a class or property, based on a complex relation between the extensions of the same or other classes and properties. On the other hand, the inclusion of negative information both in the form of negation-as-failure and explicit negative information is also needed to enable various forms of reasoning. In this paper, we extend RDF graphs with weak and strong negation, as well as derivation rules. The ERDF stable model semantics of the extended framework (Extended RDF) is defined, extending RDF(S) semantics. A distinctive feature of our theory, which is based on Partial Logic, is that both truth and falsity extensions of properties and classes are considered, allowing for truth value gaps. Our framework supports both closed-world and open-world reasoning through the explicit representation of the particular closed-world assumptions and the ERDF ontological categories of total properties and total classes.

RuleML‐based learning object interoperability on the Semantic Web

PurposeThe present paper aims to describe an approach for building the Semantic Web rules for interoperation between heterogeneous learning objects, namely course outlines from different universities, and one of the rule uses: identifying (in)compatibilities between course descriptions.Design/methodology/approachAs proof of concept, a rule set is implemented using the rule markup language (RuleML), a member of XML‐based languages. This representation in RuleML allows the rule base to be platform‐independent, flexibly extensible and executable.FindingsThe RuleML source representation is easily converted to other XML‐based languages (such as RDF, OWL and XMI) as well as incorporated into, and extracted from, existing XML‐based repositories (such as IEEE LOM and CanLOM) using XSL Transformations (XSLT).Practical implicationsThe RuleML facts and rules represented in the positional slotted language are used by the OO jDREW reasoning engine to detect and map between semantically equivalent components of course outlines as the key step in their interoperation. In particular, this will enable the precise delivery of learning objects (e.g. course outlines) from repositories to a specific learner's context.Originality/valueAlthough the particular scenario is discussed in the present paper, the proposed approach can be applied to other tasks related to enabling semantic interoperability.

Rule identification using ontology while acquiring rules from Web pages

As research on the Semantic Web actively progresses, a more intelligent Web environment is expected in various domains including rule-based systems and intelligent agents. However, rule acquisition is still a bottleneck in the utilization of rule-based systems. To extract rules from Web pages, the framework of eXtensible Rule Markup Language (XRML) has been developed. XRML allows the identification of rules from Web pages and generates rules automatically. However, the knowledge engineer's burden is still high because rule identification requires considerable manual work. In order to reduce the knowledge engineer's burden, we proposed an ontology-based methodology of enhanced rule identification. First, we have designed an ontology OntoRule for automated rule identification. Also, we proposed a procedure of rule identification using OntoRule. Lastly, we showed the performance of our approach with an experiment.

Integration of rules and policies for Semantic Web Services

Multimedia service providers on the web need their services to be well protected and easily accessible worldwide. This has initiated several lines of research to provide semantically rich policy-aware web services. However, the existence of various technologies to define and protect services has made the communication of services questionable. Currently, it is ambiguous how a broker agent looking for a special object would communicate to an online service provider, with different policy and service definition languages, to obtain the appropriate object. Here, we propose a solution to such problems by introducing an interchange framework to transform business rules and policies between different enterprises using REWERSE Rule Markup Language.

Jess를 이용한 OWL과 SWRL의 통합추론에 관한 연구

OWL(Web Ontology Language) is the Ontology Standard Language and the a lot of Ontologies are being constructed in OWL. But the research on the extension of OWL is also progressing because of the limit of representation power of in OWL language. The W3C suggests the SWRL(Semantic Web Rule Language) based on the combination of OWL and RuleML(Rule Markup Language), which is improved in the representation of rule. Thus, both OWL and SWRL are used for developing ontologies. However, research on inference of ontologies written in both languages is just begun. These day, for the inference of ontologies written in both languages, ontologies and divided in to two parts. The part written in OWL and written in SWRL. For the inference of the part written in OWL, Racer, a DL based inference engine, is used and for the other part Jess, a rule-based engine, is used. In this paper, we will propose three methods for integrated inference of the OWL part and the SWRL part of ontologies using Jess and some tools for ontology inference : OWLJessKB and SWRL Factory

A match‐making system for learners and learning objects

We have proposed and implemented AgentMatcher, an architecture for match‐making in e‐Business applications. It uses arc‐labeled and arc‐weighted trees to match buyers and sellers via our novel similarity algorithm. This paper adapts the architecture for match‐making between learners and learning objects (LOs). It uses the Canadian Learning Object Metadata (CanLOM) repository of the eduSource e‐Learning project. Through AgentMatcher’s new indexing component, known as Learning Object Metadata Generator (LOMGen), metadata is extracted from HTML LOs for use in CanLOM. LOMGen semi‐automatically generates the LO metadata by combining a word frequency count and dictionary lookup. A subset of these metadata terms can be selected from a query interface, which permits adjustment of weights that express user preferences. Web‐based pre‐filtering is then performed over the CanLOM metadata kept in a relational database. Using an XSLT (Extensible Stylesheet Language Transformations) translator, the pre‐filtered result is transformed into an XML representation, called Weighted Object‐Oriented (WOO) RuleML (Rule Markup Language). This is compared to the WOO RuleML representation obtained from the query interface by AgentMatcher’s core Similarity Engine. The final result is presented as a ranked LO list with a user‐specified threshold.

Rule identification from Web pages by the XRML approach

In the world of Web pages, there are oceans of documents in natural language texts and tables. To extract rules from Web pages and maintain consistency between them, we have developed the framework of XRML (eXtensible Rule Markup Language). XRML allows the identification of rules on Web pages and generates the identified rules automatically. For this purpose, we have designed the Rule Identification Markup Language (RIML), which is similar to the formal Rule Structure Markup Language (RSML), both as parts of XRML. RIML 2.0 is designed to identify rules not only from texts, but also from tables on Web pages, and to transform to the formal rules in RSML syntax automatically. While designing RIML 2.0, we considered the features of sharing variables and values, omitted terms, and synonyms. We have conducted an experiment to evaluate the potential benefit of the XRML approach with real world Web pages of Amazon.com, BarnesandNoble.com, and Powells.com. We found that 100.0% of the rules and 99.7% of the rule components could be identified and automatically generated if we do not count the statements for linkages, which generically do not exist on the Web pages. Since the linkage components occupy 11.2% of all components in the rule base, the overall limitation of automatic rule generation is 88.8%. In this setting, 88.5% of the overall rule components could be generated from the identified rules from the Web pages. The result provides solid proof that XRML can facilitate the extraction and maintenance of rules from Web pages while building expert systems in the Semantic Web environment.

Composite event monitoring in XML repositories using generic rule framework for providing reactive e-services

E-services are emerging as a new paradigm to build web applications. Extensible Markup Language (XML) is becoming a dominant standard for exchanging and storing information in XML-enabled repositories. With its increasing use in areas like e-commerce, there is a rapidly growing need for rule-based technology to support reactive functionality on XML repositories. Active (Event-Condition-Action, ECA) rules are used in the form of XML to implement reactive behavior in e-services. In such services, the reactive rules are used to monitor the event(s) of interest of various users and on the occurrence of the same if the conditions were satisfied then appropriate action would be taken. The event of interest of various users may be simple or complex. In e-service scenario, it becomes necessary to facilitate users to monitor complex events, which are associated with various application domains. These complex events include simple, temporal, and composite events. In this paper, a system is proposed that handles such complex events. For this purpose, the system used a specially designed language called Generic Composite Event Rule Markup Language (GCERML). Monitoring of complex events requires a complex logic and hence the system proposed in this paper uses an inference engine called Java Expert System Shell (JESS), to monitor the complex events in an easy and efficient manner.

Composite event monitoring in XML repositories using generic rule framework for providing reactive e-services

E-services are emerging as a new paradigm to build web applications. Extensible Markup Language (XML) is becoming a dominant standard for exchanging and storing information in XML-enabled repositories. With its increasing use in areas like e-commerce, there is a rapidly growing need for rule-based technology to support reactive functionality on XML repositories. Active (Event-Condition-Action, ECA) rules are used in the form of XML to implement reactive behavior in e-services. In such services, the reactive rules are used to monitor the event(s) of interest of various users and on the occurrence of the same if the conditions were satisfied then appropriate action would be taken. The event of interest of various users may be simple or complex. In e-service scenario, it becomes necessary to facilitate users to monitor complex events, which are associated with various application domains. These complex events include simple, temporal, and composite events. In this paper, a system is proposed that handles such complex events. For this purpose, the system used a specially designed language called Generic Composite Event Rule Markup Language (GCERML). Monitoring of complex events requires a complex logic and hence the system proposed in this paper uses an inference engine called Java Expert System Shell (JESS), to monitor the complex events in an easy and efficient manner.

Rule-based generation of requirements traceability relations

The support for traceability between requirement specifications has been recognised as an important task in the development life cycle of software systems. In this paper, we present a rule-based approach to support the automatic generation of traceability relations between documents which specify requirement statements and use cases (expressed in structured forms of natural language), and analysis object models for software systems. The generation of such relations is based on traceability rules of two different types. More specifically, we use requirement-to-object-model rules to trace the requirements and use case specification documents to an analysis object model, and inter-requirements traceability rules to trace requirement and use case specification documents to each other. By deploying such rules, our approach can generate four different types of traceability relations. To implement and demonstrate our approach, we have implemented a traceability prototype system. This system assumes requirement and use case specification documents and analysis object models represented in XML. It also uses traceability rules which are represented in an XML-based rule mark-up language that we have developed for this purpose. This XML-based representation framework makes it easier to deploy our prototype in settings characterised by the use of heterogeneous software engineering and requirements management tools. The developed prototype has been used in a series of experiments that we have conducted to evaluate our approach. The results of these experiments have provided encouraging initial evidence about the plausibility of our approach and are discussed in the paper.

An XML-based active document for intelligent web applications

Abstract The Web is an important source of information. Most Web applications are based on form documents in Hypertext Markup Language (HTML). These HTML-based form documents provide user interfaces only, but do not include business procedures or logics that document form designers assume. However, form documents imply the procedures for processing them, and the implicit procedural knowledge can be positively utilized in automating business processes. In this respect, this paper proposes a new approach to active documents as a method for automating business processes based on form documents. This active document includes declarative knowledge and business rules implied in documents to support the automation of document processing. To realize the active documents, we devised an Executable Rule Markup Language (ERML), an executable meta-language in XML. ERML documents are executable in an inference engine, can be geared with a workflow management system (WfMS), and can be adopted in heterogeneous rule-based applications. Also, this paper suggests a processing framework for the proposed active documents. To demonstrate the usefulness of the proposed framework, we implemented a prototype system called ActiveForm , where active documents are applied to the processing of purchase orders in an Internet procurement system. As a result, our approach is considered to enhance the intelligence of Web applications by active documents.