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This paper sets out to discuss the use of information extraction (IE), a natural language‐processing (NLP) technique to assist “rich” semantic indexing of diverse archaeological text resources. The focus of the research is to direct a semantic‐aware “rich” indexing of diverse natural language resources with properties capable of satisfying information retrieval from online publications and datasets associated with the Semantic Technologies for Archaeological Resources (STAR) project.

The paper proposes use of the English Heritage extension (CRM‐EH) of the standard core ontology in cultural heritage, CIDOC CRM, and exploitation of domain thesauri resources for driving and enhancing an Ontology‐Oriented Information Extraction process. The process of semantic indexing is based on a rule‐based Information Extraction technique, which is facilitated by the General Architecture of Text Engineering (GATE) toolkit and expressed by Java Annotation Pattern Engine (JAPE) rules.

Initial results suggest that the combination of information extraction with knowledge resources and standard conceptual models is capable of supporting semantic‐aware term indexing. Additional efforts are required for further exploitation of the technique and adoption of formal evaluation methods for assessing the performance of the method in measurable terms.

The value of the paper lies in the semantic indexing of 535 unpublished online documents often referred to as “Grey Literature”, from the Archaeological Data Service OASIS corpus (Online AccesS to the Index of archaeological investigationS), with respect to the CRM ontological concepts E49.Time Appellation and P19.Physical Object.

Lecture recording provides learning material for local and distance education. The TeleTeachingTool uses the very flexible screen recording technique to capture virtually any material displayed during a presentation. With its built‐in annotation system teachers can add freehand notes and emphasize important parts. Unlike other screen recorders, our implementation offers slide‐based navigation, full text search and annotated scripts, which are obtained by automated post‐production. This article presents how automated analysis generates indices for slide‐based navigation on the fly and how to achieve live interlinkage of annotations with slides so that annotations disappear when a slide is changed and are made visible again when returning to that slide later during presentation, although screen recorders generally do not provide an association of annotations with slides.

The purpose of the research is to speed up the process of semantic web services by transformation of current Web services into semantic web services. This can be achieved by applying ontology learning techniques to automatically extract domain ontologies.

The work here presents a Service Ontology Learning Framework (SOLF), the core aspect of which extracts Structured Interpretation Patterns (SIP). These patterns are used to automate the acquisition (from production domain specific Web Services) of ontological concepts and the relations between those concepts.

A Semantic Web of accessible and re‐usable software services is able to support the increasingly dynamic and time‐limited development process. This is premised on the efficient and effective creation of supporting domain ontology.

Though WSDL documents provide important application level service description, they alone are not sufficient for OL however, as: they typically provide technical descriptions only; and in many cases, Web services use XSD files to provide data type definitions. The need to include (and combine) other Web service resources in the OL process is therefore an important one.

Web service domain ontologies are the general means by which semantics are added to Web services; typically used as a common domain model and referenced by annotated or externally described Web artefacts (e.g. Web services). The development and deployment of Semantic Web services by enterprises and the wider business community has the potential to radically improve planned and ad‐hoc service re‐use. The reality is slower however, in good part because the development of an appropriate ontology is an expensive, error prone and labor intensive task. The proposed SOLF framework is aimed to overcome this problem by contributing a framework and a tool that can be used to build web service domain ontologies automatically.

The output of the SOLF process is an automatically generated OWL domain ontology, a basis from which a future Semantic Web Services can be delivered using existing Web services. It can be seen that the ontology created moves beyond basic taxonomy – extracting and relating concepts at a number of levels. More importantly, the approach provides integrated knowledge (represented by the individual WSDL documents) from a number of domain experts across a group of banks.

The previous generations of implemented B2B e‐commerce hub solutions (e‐Marketplaces) did not successfully fulfil the requirements of buyers and suppliers (“Participants”) in different business domains to carry out their daily business and online commercial transactions with one another because of their inappropriateness, and lack of flexibility. The limitations of these provided solutions came from a lot of architectural and technological challenges in the provided technical architectures that were used to build these solutions. This research aims to provide a proposed architecture to build integrated B2B e‐Commerce hub solutions. It also aims to make use of bottom‐up/top‐down approaches to building an integrated solution and to resolve the reasons for the failure of previous generations of B2B e‐commerce hubs.

The research uses the EDI reference model, which is provided by the ISO organization to survey and analyze the challenges of previous generations of B2B e‐Commerce hubs solutions and their architectures. The study develops a proposed solution architecture based on the recent approaches to building IOSs to build a B2B e‐commerce hub solution architecture that can be used to implement vertical B2B e‐commerce hubs (vertical e‐Marketplaces). The paper assesses the capabilities of the proposed solution architecture for building vertical B2B e‐Marketplaces by applying the proposed architecture to the building of a vertical B2B e‐Marketplace for the oil and gas sector in Egypt.

Previous B2B e‐Commerce hub initiatives failed to extend their products and services to their “Participants”, and required substantial investment and effort from each “Participant” to join such a B2B e‐Commerce hub. The failure of these IOS projects lies in their inability to integrate B2B e‐Commerce networks based on IOS and consequently, they supported very few partners and “Participants”. These IOS approaches did not resolve the existing challenges of B2B e‐Commerce hubs, especially in the realm of interoperability.

The main contribution of the proposed architecture comes from the creation of a clear automatic path between a business requirements layer and a technology layer by combining both Service Oriented Architecture and management requirements in a single framework to provide dynamic products and flexible services. It provides a complete Multi Channel Framework to resolve the interoperability challenges.

Searching the tender notices that publish every day in open tendering websites is a common way for finding business opportunity in public procurement. The heterogeneity of tender notices from various tendering marketplaces is a challenge for exploiting semantic technologies in the tender search.

Most of the semantic matching approaches require the data to be structured and integrated according to a data model. But the integration process can be expensive and time-consuming especially for multi-source data integration.

In this paper, a product search mechanism that had been developed in an e-procurement platform for matching product e-catalogues is applied to the tender search problem. The search performance has been compared using two procurement vocabularies on searching tender notices from two major tender resources.

The test results show that the matching mechanism is able to find tender notices from heterogeneous resources and different classification systems without transforming the tenders to a uniform data model.

The purpose of this paper is to present an automated ontology mapping and merging algorithm, namely OntoDNA, which employs data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities among distributed data sources in organizational memory and subsequently generate a merged ontology to facilitate resource retrieval from distributed resources for organizational decision making.

The OntoDNA employs unsupervised data mining techniques (FCA, SOM, K‐means) to resolve ontological heterogeneities to integrate distributed data sources in organizational memory. Unsupervised methods are needed as an alternative in the absence of prior knowledge for managing this knowledge. Given two ontologies that are to be merged as the input, the ontologies' conceptual pattern is discovered using FCA. Then, string normalizations are applied to transform their attributes in the formal context prior to lexical similarity mapping. Mapping rules are applied to reconcile the attributes. Subsequently, SOM and K‐means are applied for semantic similarity mapping based on the conceptual pattern discovered in the formal context to reduce the problem size of the SOM clusters as validated by the Davies‐Bouldin index. The mapping rules are then applied to discover semantic similarity between ontological concepts in the clusters and the ontological concepts of the target ontology are updated to the source ontology based on the merging rules. Merged ontology in a concept lattice is formed.

In experimental comparisons between PROMPT and OntoDNA ontology mapping and merging tool based on precision, recall and f‐measure, average mapping results for OntoDNA is 95.97 percent compared to PROMPT's 67.24 percent. In terms of recall, OntoDNA outperforms PROMPT on all the paired ontology except for one paired ontology. For the merging of one paired ontology, PROMPT fails to identify the mapping elements. OntoDNA significantly outperforms PROMPT due to the utilization of FCA in the OntoDNA to capture attributes and the inherent structural relationships among concepts. Better performance in OntoDNA is due to the following reasons. First, semantic problems such as synonymy and polysemy are resolved prior to contextual clustering. Second, unsupervised data mining techniques (SOM and K‐means) have reduced problem size. Third, string matching performs better than PROMPT's linguistic‐similarity matching in addressing semantic heterogeneity, in context it also contributes to the OntoDNA results. String matching resolves concept names based on similarity between concept names in each cluster for ontology mapping. Linguistic‐similarity matching resolves concept names based on concept‐representation structure and relations between concepts for ontology mapping.

The OntoDNA automates ontology mapping and merging without the need of any prior knowledge to generate a merged ontology. String matching is shown to perform better than linguistic‐similarity matching in resolving concept names. The OntoDNA will be valuable for organizations interested in merging ontologies from distributed or different organizational memories. For example, an organization might want to merge their organization‐specific ontologies with community standard ontologies.

In data engineering, view formalisms are used to provide flexibility to users and user applications by allowing them to extract and elaborate data from the stored data sources. Conversely, since the introduction of EXtensible Markup Language (XML), it is fast emerging as the dominant standard for storing, describing, and interchanging data among various web and heterogeneous data sources. In combination with XML Schema, XML provides rich facilities for defining and constraining user‐defined data semantics and properties, a feature that is unique to XML. In this context, it is interesting to investigate traditional database features, such as view models and view design techniques for XML. However, traditional view formalisms are strongly coupled to the data language and its syntax, thus it proves to be a difficult task to support views in the case of semi‐structured data models. Therefore, in this paper we propose a Layered View Model (LVM) for XML with conceptual and schemata extensions. Here our work is three‐fold; first we propose an approach to separate the implementation and conceptual aspects of the views that provides a clear separation of concerns, thus, allowing analysis and design of views to be separated from their implementation. Secondly, we define representations to express and construct these views at the conceptual level. Thirdly, we define a view transformation methodology for XML views in the LVM, which carries out automated transformation to a view schema and a view query expression in an appropriate query language. Also, to validate and apply the LVM concepts, methods and transformations developed, we propose a viewdriven application development framework with the flexibility to develop web and database applications for XML, at varying levels of abstraction.

XML emerges and evolves quick and fast as Web and wireless technology penetrates more into the consumer marketplace. Database technology faces new challenges. It has to change to play the supportive role. Web and wireless applications master the technology paradigm shift. XML and database connectivity and transformation become critical. Heterogeneity and interoperability must be distinctly tackled. In this paper, we provide an in‐depth and technical review of XML and XML database technology. An analytic and comparative framework is developed. Storage method, mapping technique, and transformation paradigm formulate the framework. We collect and compile the IBM, Oracle, Sybase, and Microsoft XML database products. We use the framework and analyze each of these XML database techniques. The comparison and contrast aims to provide an insight into the structural and methodological paradigm shift in XML database technology.

The purpose of this paper is to present a framework for integrating construction supply chain in order to resolve the data heterogeneity and data sharing problems in the construction industry.

Standardized web service technology is used in the proposed framework for data specification, transfer, and integration. Open standard SAWSDL is used to annotate web service descriptions with pointers to concepts defined in ontologies. NoSQL database Cassandra is used for distributed data storage among construction supply chain stakeholders.

Ontology can be used to support heterogeneous data transfer and integration through web services. Distributed data storage facilitates data sharing and enhances data control.

This paper presents examples of two ontologies for expressing construction supply chain information – ontology for material and ontology for purchase order. An example scenario is presented to demonstrate the proposed web service framework for material procurement process involving three parties, namely, project manager, contractor, and material supplier.

The use of web services is not new to construction supply chains (CSCs). However, it still faces problems in channelizing information along CSCs due to data heterogeneity. Trust issue is also a barrier to information sharing for integrating supply chains in a centralized collaboration system. In this paper, the authors present a web service framework, which facilitates storage and sharing of information on a distributed manner mediated through ontology-based web services. Security is enhanced with access control. A data model for the distributed databases is also presented for data storage and retrieval.

This article aims to explore how to incorporate similarity joins into XML database management systems (XDBMSs). The authors aim to provide seamless and efficient integration of similarity joins on tree-structured data into an XDBMS architecture.

The authors exploit XDBMS-specific features to efficiently generate XML tree representations for similarity matching. In particular, the authors push down a large part of the structural similarity evaluation close to the storage layer.

Empirical experiments were conducted to measure and compare accuracy, performance and scalability of the tree similarity join using different similarity functions and on the top of different storage models. The results show that the authors’ proposal delivers performance and scalability without hurting the accuracy.

Similarity join is a fundamental operation for data integration. Unfortunately, none of the XDBMS architectures proposed so far provides an efficient support for this operation. Evaluating similarity joins on XML is challenging, because it requires similarity matching on the text and structure. In this work, the authors integrate similarity joins into an XDBMS. To the best of the authors’ knowledge, this work is the first to leverage the storage scheme of an XDBMS to support XML similarity join processing.