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XML Schema is used to define schema of XML documents that have become standards for data exchange in various Web-based information applications. The main problem of XML Schema is that it emphasizes syntax and format rather than semantics and knowledge representation. Hence, even though having the advantage of describing the structure and constraining the contents of XML documents, XML Schema lacks the computer-interpretability to support knowledge representation for existing information systems. The purpose of this study is to propose role-mapping annotations for XML Schema (RMAXS) to integrate Semantic Web with XML Schema, which allows the facilitation interoperability between adjoining layers of the Semantic Web stack.

The XML, XML Schema, ontology, and rule can be completely integrated into a multi-layered intelligent framework (MIF) for XML-based applications in the current web environment. This work presents a semantic-role-mapping intelligent system, called SRMIS, based on the MIF. SRMIS consists of XML-based document repository, search engine, inference engine and transformation engine, which provides different approaches to present the various metadata and knowledge representations.

The traditional Semantic Web stack has three gaps between adjoining layers. The first gap, between the XML and XML Schema layers can be bridged with an XMLSchema-instance mechanism. The third gap, between the ontology and rule layers can be connected by building rules on top of ontologies. This study proposes RMAXS to couple the second gap, between the XML schema and ontology layers. The proposed multi-layered intelligent framework (MIF) adopts these coupling technologies to facilitate interoperability between adjoining layers. Therefore, the XML, XML Schema, ontology, and rule can be completely integrated into the MIF for intelligent applications in the web environment.

To demonstrate the SRMIS applications, this work implements a prototype that helps researchers to find interested papers.

This work presents a semantic-role-mapping intelligent system, called SRMIS, based on the MIF. SRMIS consists of XML-based document repository, search engine, inference engine and transformation engine, which provides different approaches to present the various metadata and knowledge representations. The proposed SRMIS can be applied in various application domains.

The 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.

As 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.

The 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).

The 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.

Although the particular scenario is discussed in the present paper, the proposed approach can be applied to other tasks related to enabling semantic interoperability.

The purpose of this paper is to demonstrate how the semantic rules in conjunction with ontology can be applied for inferring new facts to dispatch news into corresponding departments.

Under a specific task domain, the proposed design comprises finding a glossary from electronic resources, gathering organization functions as controlled vocabularies, and linking relationships between the glossary and controlled vocabularies. Web ontology language is employed to represent this knowledge as ontology, and semantic web rule language is utilized to infer implicit facts among instances.

Document dispatching is highly domain dependent. Human perspectives being adopted as predefined knowledge in understanding document meanings are important. Knowledge‐intensive approaches such as ontology can model and represent expertise as reusable components. Ontology and rules together extend inference capabilities in semantic relationships between instances.

Empirical lessons reveal that ontology with semantic rules can be utilized to model human subjective judgement as knowledge bases. An example, including ontology and rules, based on news dispatching is provided.

An organization can classify and deliver documents to corresponding departments based on known facts by following the described procedure.

The purpose of the paper is to provide a two‐tier framework for managing semantic‐aware distributed firewall policies to be applied to the devices existing in one administrative domain.

Special attention is paid to the CIM‐based information model defined as the ontology to be used in this framework and the AI‐based reasoning mechanisms and components used to perform the conflict discovery tasks over the distributed firewall policies.

Mechanisms presented allow the solving some of the current issues of the network‐centric security model being used in the Internet. The two‐tier framework designed provides semantic‐aware mechanisms to perform conflict detection and automatic enforcement of policy rules in the distributed firewall scenario. This framework is based on the use of a standard information model and a semantic‐aware policy language to formally define (and then process) firewall policies.

Ongoing work is focused on identifying all kind of conflicts and anomalies that may exist in firewall systems; in parallel to this task a semi‐automatic resolver of conflicting policies is currently under design.

Network and security administrators can specify firewall policies and validate them to find syntactic and semantic errors (i.e. policy conflicts). A framework for automated validation and distribution of policies at different levels is included. This ensures that firewall policies produce the desired effects, facilitating the creation and maintenance of firewall rules in one administrative domain.

A practical and novel two‐tier system that provides detection of conflicts in rules existing in a distributed firewall scenario and the automatic and secure deployment of these rules. A packet‐filtering model, which is simple and powerful enough for the conflict discovery and rule analysis processes, has been proposed. Moreover, ontology and rule reasoning are being proposed as techniques for the conflict detection problem in this particular scenario.

The purpose of this article is to explore and conceptualize the Semantic Web as a term that has been widely mentioned in the literature of library and information science. More specifically, its aim is to shed light on the evolution of the Web and to highlight a previously proposed means of attempting to improve automated manipulation of Web-based data in the context of a rapidly expanding base of both users and digital content.

The conceptual analysis presented in this paper adopts a three-dimensional model for the discussion of Semantic Web. The first dimension focuses on Semantic Web’s basic nature, purpose and history, as well as the current state and limitations of modern search systems and related software agents. The second dimension focuses on critical knowledge structures such as taxonomies, thesauri and ontologies which are understood as fundamental elements in the creation of a Semantic Web architecture. In the third dimension, an alternative conceptual model is proposed, one, which unlike more commonly prevalent Semantic Web models, offers a greater emphasis on describing the proposed structure from an interpretive viewpoint, rather than a technical one. This paper adopts an interpretive, historical and conceptual approach to the notion of the Semantic Web by reviewing the literature and by analyzing the developments associated with the Web over the past three decades. It proposes a simplified conceptual model for easy understanding.

The paper provides a conceptual model of the Semantic Web that encompasses four key strata, namely, the body of human users, the body of software applications facilitating creation and consumption of documents, the body of documents themselves and a proposed layer that would improve automated manipulation of Web-based data by the software applications.

This paper will facilitate a better conceptual understanding of the Semantic Web, and thereby contribute, in a small way, to the larger body of discourse surrounding it. The conceptual model will provide a reference point for education and research purposes.

This paper provides an original analysis of both conceptual and technical aspects of Semantic Web. The proposed conceptual model provides a new perspective on this subject.

Learning objects strive for reusability in e‐Learning to reduce cost and allow personalization of content. We show why learning objects require adapted Information Retrieval systems. In the spirit of the Semantic Web, we discuss the semantic description, discovery, and composition of learning objects. As part of our project, we tag learning objects with both objective (e.g., title, date, and author) and subjective (e.g., quality and relevance) metadata. We present the RACOFI (Rule‐Applying Collaborative Filtering) Composer prototype with its novel combination of two libraries and their associated engines: a collaborative filtering system and an inference rule system. We developed RACOFI to generate context‐aware recommendation lists. Context is handled by multidimensional predictions produced from a database‐driven scalable collaborative filtering algorithm. Rules are then applied to the predictions to customize the recommendations according to user profiles. The RACOFI Composer architecture has been developed into the contextaware music portal inDiscover.

The purpose of this paper is to explore the feasibility that an ontological approach can be applied to formalize the construction regulation constraint knowledge in a computer-interpretable way, for construction quality checking, during construction stage.

The ontological and semantic web technologies are used to model the construction quality constraints knowledge into Axioms/OWL and SWRL rules. Protégé platform is selected to illustrate how the construction quality checking, based on the Axioms/OWL and SWRL rules, is achieved.

The ontology and semantic web technologies can be an alternative way for modeling the construction regulation constraints in a computer-interpretable way, and can be implemented for the regulation-based construction quality checking.

The approach is illustrated only with given specific technical constraints examples, the generality and practicality of the approach need further investigation.

The paper introduces an ontological and semantic approach to model and formalize the construction regulation constraints for construction quality checking, and proves the feasibility by the case studies. The proposed approach enables the regulations can be understood and retrieved semantically by computers, which facilitates the using of regulation codes.

Semantic Web is an extension of the World Wide Web by tagging content with “meaning”. In general, question answering systems based on semantic Web face a number of difficult issues. This paper aims to design an experimental environment with custom rules and scalable data sets and evaluate the performance of a proposed optimized backward chaining ontology reasoning system. This study also compares the experimental results with other ontology reasoning systems to show the performance and scalability of this ontology reasoning system.

The authors proposed a semantic question answering system. This system has been built using ontological knowledge base including optimized backward chaining ontology reasoning system and custom rules. With custom rules, the proposed semantic question answering system will be able to answer questions that contain qualitative descriptors such as “groundbreaking” resesarch and “tenurable at university x”. Scalability has been one of the difficult issues faced by an optimized backward chaining ontology reasoning system and semantic question answering system. To evaluate the proposed ontology reasoning system, first, the authors design a number of innovative custom rule sets and corresponding query sets. The innovative custom rule sets and query sets will contribute to the future research on evaluating ontology reasoning systems as well. Then they design an experimental environment including ontologies and scalable data sets and metrics. Furthermore, they evaluate the performance of the proposed optimized backward chaining reasoning system on supporting custom rules. The evaluation results have been compared with other ontology reasoning systems as well.

The proposed innovative custom rules and query sets can be effectively employed for evaluating ontology reasoning systems. The evaluation results show that the scalability of the proposed backward chaining ontology reasoning system is better than in-memory reasoning systems. The proposed semantic question answering system can be integrated in sematic Web applications to solve scalability issues. For light weight applications, such as mobile applications, in-memory reasoning systems will be a better choice.

This paper fulfils an identified need for a study on evaluating an ontology reasoning system on supporting custom rules with and without external storage.

Web site recommendation systems help to get high quality information. The modelling of recommendation systems involves the combination of many features: metrics of quality, quality criteria, recommendation criteria, user profile, and specific domain concepts, among others. At the moment of the specification of a recommendation system it must be guaranteed a right interrelation of all of these features. The purpose of this paper is to model a web site quality‐based recommendation system by an ontology network.

In this paper, the authors propose an ontology network based process for web site recommendation modelling. The ontology network conceptualizes the different domains (web site domain, quality assurance domain, user context domain, recommendation criteria domain, specific domain) in a set of interrelated ontologies. Particularly, this approach is illustrated for the health domain.

Basically, this work introduces the semantic relationships that were used to construct this ontology network. Moreover, it shows the usefulness of this ontology network for the detection of possible inconsistencies when specifying recommendation criteria.

Recommendation systems based on ontologies that model the user profile and the domain of resources to be recommended are quite common. However, it is uncommon to find models that explicitly represent the criteria used by the recommender systems, that express the quality dimensions of resources and on which criteria are applied, and consider the user context at the moment of the query.

Despite several efforts during the last years, the web model and semantic web technologies have not yet been successfully applied to empower Ubiquitous Computing architectures in order to create knowledge‐rich environments populated by interconnected smart devices. In this paper we point out some problems of these previous initiatives and introduce SoaM (Smart Objects Awareness and Adaptation Model), an architecture for designing and seamlessly deploying web‐powered context‐aware semantic gadgets. Implementation and evaluation details of SoaM are also provided in order to identify future research challenges.