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Massive amounts of data, manifesting in various forms, are being produced on the Web every minute and becoming the new standard. Exploring these information sources distributed in different Web segments in a unified way is becoming a core task for a variety of users’ and companies’ scenarios. However, knowledge creation and exploration from distributed Web data sources is a challenging task. Several data integration conflicts need to be resolved and the knowledge needs to be visualized in an intuitive manner. The purpose of this paper is to extend the authors’ previous integration works to address semantic knowledge exploration of enterprise data combined with heterogeneous social and linked Web data sources.

The authors synthesize information in the form of a knowledge graph to resolve interoperability conflicts at integration time. They begin by describing KGMap, a mapping model for leveraging knowledge graphs to bridge heterogeneous relational, social and linked web data sources. The mapping model relies on semantic similarity measures to connect the knowledge graph schema with the sources' metadata elements. Then, based on KGMap, this paper proposes KeyFSI, a keyword-based semantic search engine. KeyFSI provides a responsive faceted navigating Web user interface designed to facilitate the exploration and visualization of embedded data behind the knowledge graph. The authors implemented their approach for a business enterprise data exploration scenario where inputs are retrieved on the fly from a local customer relationship management database combined with the DBpedia endpoint and the Facebook Web application programming interface (API).

The authors conducted an empirical study to test the effectiveness of their approach using different similarity measures. The observed results showed better efficiency when using a semantic similarity measure. In addition, a usability evaluation was conducted to compare KeyFSI features with recent knowledge exploration systems. The obtained results demonstrate the added value and usability of the contributed approach.

Most state-of-the-art interfaces allow users to browse one Web segment at a time. The originality of this paper lies in proposing a cost-effective virtual on-demand knowledge creation approach, a method that enables organizations to explore valuable knowledge across multiple Web segments simultaneously. In addition, the responsive components implemented in KeyFSI allow the interface to adequately handle the uncertainty imposed by the nature of Web information, thereby providing a better user experience.

This paper introduces an approach to minimize the total designer effort for building XML data integration systems. Since fully automatic schema mapping generation is infeasible, in our view such an approach can be used as a semi‐automatic tool for XML schemas mediation. A method is proposed to query XML documents through a mediation layer. Such a layer is introduced to describe the mappings between global XML schema and local heterogeneous XML schemas. It produces a uniform interface over the local XML data sources, and provides the required functionality to query these sources in a uniform way. It involves two important units: the XML Metadata Document (XMD) and the Query Translator. The XMD is an XML document containing metadata, in which the mappings between global and local schemas are defined. The XML Query Translator which is an integral part of the system is introduced to translate a global user query into local queries by using the mappings that are defined in the XMD.

Research in the area of technology‐enhanced learning (TEL) throughout the last decade has largely focused on sharing and reusing educational resources and data. This effort has led to a fragmented landscape of competing metadata schemas, or interface mechanisms. More recently, semantic technologies were taken into account to improve interoperability. The linked data approach has emerged as the de facto standard for sharing data on the web. To this end, it is obvious that the application of linked data principles offers a large potential to solve interoperability issues in the field of TEL. This paper aims to address this issue.

In this paper, approaches are surveyed that are aimed towards a vision of linked education, i.e. education which exploits educational web data. It particularly considers the exploitation of the wealth of already existing TEL data on the web by allowing its exposure as linked data and by taking into account automated enrichment and interlinking techniques to provide rich and well‐interlinked data for the educational domain.

So far web‐scale integration of educational resources is not facilitated, mainly due to the lack of take‐up of shared principles, datasets and schemas. However, linked data principles increasingly are recognized by the TEL community. The paper provides a structured assessment and classification of existing challenges and approaches, serving as potential guideline for researchers and practitioners in the field.

Being one of the first comprehensive surveys on the topic of linked data for education, the paper has the potential to become a widely recognized reference publication in the area.

In recent years, readers have limited amounts of time to pick the right books to read from a market that is filled with similar types of books. Aiming to read only good books, readers tend to check book reviews written by others. However, it is very difficult to find good book reviews. The aim of this paper is to present a book review recommendation system that collects reviews from heterogeneous sources on the Internet and performs quality judgments automatically. Users can then read the top‐ranked reviews suggested by this recommendation system.

In this paper, a book review recommendation system is constructed to collect, process, and judge the quality of book reviews from various heterogeneous sources. The quality measurement of book reviews uses review‐evaluation techniques. The prediction results were validated with a ranking list produced by experts.

The proposed system is effective and suitable for recommending quality book reviews from heterogeneous sources. The proposed quality measurement method is more effective than other more commonly used methods.

This paper is one of the first to apply review evaluation techniques to the process of book review recommendation. The proposed system can collect and recognize book reviews from different websites with various forms of presentation. This evaluation shows that the quality measurement method produces better results than do other methods, such as ranking by rating score or by the date that the review was posted. Those methods are primarily used by commercial websites.

The purpose of this paper is to establish an application platform that addresses expensive development cost and effort of indoor location-aware application (InL-Apps) problems caused by tightly coupling between InL-App and indoor positioning systems (IPSs).

To achieve this purpose, in this paper, the authors proposes a Web-based integration framework called Web-based Integration Framework for Indoor Location (WIF4InL). With a common data model, WIF4InL integrates indoor location data obtained from heterogeneous IPS. It then provides application-neutral application programming interface (API) for various InL-Apps.

The authors integrate two different IPS (RedPin and BluePin) using WIF4InL and conduct a comparative study which is based on sufficiency of essential capabilities of location-dependent queries among three systems: RedPin, BluePin and WIF4InL. WIF4InL supports more capabilities for the location-dependent queries. Through the data and operation integration, WIF4InL even enriches the existing proprietary IPS.

As WIF4InL allows the loose coupling between IPS and InL-Apps, it significantly improves reusability of indoor location information and operation.

Smart city services are supported by information and communication technologies (ICT) referred to as digital technologies which increasingly promise huge opportunities for growth but are faced with system alignment and data integration issues when providing digital services. Therefore, this study aims to use enterprise architecture (EA) in digital transformation of cities by developing an architecture to address system alignment and data integration in digital transformation of cities.

Qualitative method is applied to evaluate the presented architecture based on electric-mobility (e-mobility) scenario, and data was collected using case study via interviews from a municipality in Norway to validate the applicability of EA for digital transformation of city services.

Findings from the interviews were represented in ArchiMate language to model the digital transformation of e-mobility in smart cities. Findings suggest that the architecture serves as a guide to recommend urban administrators of the potential of EA and digital transformation in addressing system alignment and data integration issues in smart cities.

Data used in this study is from a single case, hence there is a need to evaluate the application of EA for digital transformation of city services with data collected from multi-cases.

This study adopts enterprise architecture approach to support city transformation as it has been widely applied by institutions to align business and ICT components.

This study provides implication on how municipalities can use EA and digital transformations towards a sustainable smart city.

An architecture is presented that can be used as a guide to help urban developers and designers in deploying sustainable transport policies for smart cities. Additionally, EA is used to foster digitalization towards achieving system alignment and data integration in cities to support urban environment as they digitally transform services provided to citizens.

To identify cases related to design of ICT platforms for industrial alliances, where the use of Ontology‐driven architectures based on Semantic web standards is more advantageous than application of conventional modeling together with XML standards.

A comparative analysis of the two latest and the most obvious use cases (NASA and Nordic Process Industry Data Exchange Alliance) concerned with development of an environment for integration and collaboration of industrial partners, has been used as a basis for the research results. Additionally, dynamics of changes in a domain data model and their consequences have been analyzed on a couple of typical use cases.

Ontology‐driven architectures of a collaboration and integration ICT platforms have been recognized as more appropriate for a technical support of industrial alliances around a supply‐chains with a long life cycles.

More typical cases related to changes in domain data/knowledge models and to necessity of their integration, have to be considered and analyzed in search of advantageous of ontological modeling over conventional modeling approaches. Ways of a gradual change from conventional domain models to ontological ones in ICT systems have to be studied. The significance of existing XML‐based tools and the popularity of XML has to be estimated for the wide adoption of Semantic web principles.

The modeling approach which will be used as a core for building a collaboration and integration ICT platforms has to be carefully selected. Incorrect choice (e.g. UML together with XML) can cause consequences that will be hard to reform. The paper is anticipated to facilitate faster adoption of the Semantic web approach by industry.

The serious revision of existing and emerging domain modeling approaches has been undertaken. More unique arguments in favor of ontological modeling have been discovered. The paper is intended for serious consideration by emerging industrial alliances with regard to their choice in a core technology that will technically enable integration and collaboration between partners.

The purpose of this paper is to present a four-level architecture that aims at integrating, publishing and retrieving ecological data making use of linked data (LD). It allows scientists to explore taxonomical, spatial and temporal ecological information, access trophic chain relations between species and complement this information with other data sets published on the Web of data. The development of ecological information repositories is a crucial step to organize and catalog natural reserves. However, they present some challenges regarding their effectiveness to provide a shared and global view of biodiversity data, such as data heterogeneity, lack of metadata standardization and data interoperability. LD rose as an interesting technology to solve some of these challenges.

Ecological data, which is produced and collected from different media resources, is stored in distinct relational databases and published as RDF triples, using a relational-Resource Description Format mapping language. An application ontology reflects a global view of these datasets and share with them the same vocabulary. Scientists specify their data views by selecting their objects of interest in a friendly way. A data view is internally represented as an algebraic scientific workflow that applies data transformation operations to integrate data sources.

Despite of years of investment, data integration continues offering scientists challenges in obtaining consolidated data views of a large number of heterogeneous scientific data sources. The semantic integration approach presented in this paper simplifies this process both in terms of mappings and query answering through data views.

This work provides knowledge about the Guanabara Bay ecosystem, as well as to be a source of answers to the anthropic and climatic impacts on the bay ecosystem. Additionally, this work will enable evaluating the adequacy of actions that are being taken to clean up Guanabara Bay, regarding the marine ecology.

Mapping complexity is traded by the process of generating the exported ontology. The approach reduces the problem of integration to that of mappings between homogeneous ontologies. As a byproduct, data views are easily rewritten into queries over data sources. The architecture is general and although applied to the ecological context, it can be extended to other domains.

This paper aims to present a collaborative mashup platform for dynamic integration of heterogeneous data sources. The platform encourages sharing and connects data publishers, integrators, developers and end users.

This approach is based on a visual programming paradigm and follows three fundamental principles: openness, connectedness and reusability. The platform is based on semantic Web technologies and the concept of linked widgets, i.e. semantic modules that allow users to access, integrate and visualize data in a creative and collaborative manner.

The platform can effectively tackle data integration challenges by allowing users to explore relevant data sources for different contexts, tackling the data heterogeneity problem and facilitating automatic data integration, easing data integration via simple operations and fostering reusability of data processing tasks.

This research has focused exclusively on conceptual and technical aspects so far; a comprehensive user study, extensive performance and scalability testing is left for future work.

A key contribution of this paper is the concept of distributed mashups. These ad hoc data integration applications allow users to perform data processing tasks in a collaborative and distributed manner simultaneously on multiple devices. This approach requires no server infrastructure to upload data, but rather allows each user to keep control over their data and expose only relevant subsets. Distributed mashups can run persistently in the background and are hence ideal for real-time data monitoring or data streaming use cases. Furthermore, we introduce automatic mashup composition as an innovative approach based on an explicit semantic widget model.

The traditional drug development process is costly, time consuming and risky. Using computational methods to discover drug repositioning opportunities is a promising and efficient strategy in the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds of “omics” data brings opportunities for developing new computational drug repositioning methods based on big data. The paper aims to discuss this issue.

Here, a new computational strategy is proposed for inferring drug–disease associations from rich biomedical resources toward drug repositioning. First, the network embedding (NE) algorithm is adopted to learn the latent feature representation of drugs from multiple biomedical resources. Furthermore, on the basis of the latent vectors of drugs from the NE module, a binary support vector machine classifier is trained to divide unknown drug–disease pairs into positive and negative instances. Finally, this model is validated on a well-established drug–disease association data set with tenfold cross-validation.

This model obtains the performance of an area under the receiver operating characteristic curve of 90.3 percent, which is comparable to those of similar systems. The authors also analyze the performance of the model and validate its effect on predicting the new indications of old drugs.

This study shows that the authors’ method is predictive, identifying novel drug–disease interactions for drug discovery. The new feature learning methods also positively contribute to the heterogeneous data integration.