A community based approach for managing ontology alignments

The Semantic Web is rapidly becoming a defacto distributed repository for semantically represented data, thus leveraging on the added on value of the network effect. Various ontology mapping techniques and tools have been devised to facilitate the bridging and integration of distributed data repositories. Nevertheless, ontology mapping can benefitfrom human supervision to increase accuracy of results. The spread of Web 2.0 approaches demonstrate the possibility of using collaborative techniques for reaching consensus. While a number of prototypes for collaborative ontology construction are being developed, collaborative ontology mapping is not yet well investigated. In this paper, we describe a prototype that combines off-the-shelf ontology mapping tools with social software techniques to enable users to collaborate on mapping ontologies

A linked data-driven & service-oriented architecture for sharing educational resources

The two fundamental aims of managing educational resources are to enable resources to be reusable and interoperable and to enable Web-scale sharing of resources across learning communities. Currently, a variety of approaches have been proposed to expose and manage educational resources and their metadata on the Web. These are usually based on heterogeneous metadata standards and schemas, such as IEEE LOM or ADL SCORM, and diverse repository interfaces such as OAI-PMH or SQI. Also, there is still a lack of usage of controlled vocabularies and available data sets that could replace the widespread use of unstructured text for describing resources. On the other hand, the Linked Data approach has proven that it offers a set of successful principles that have the potential to alleviate the aforementioned issues. In this paper, we introduce an architecture and prototype which is fundamentally based on (a) Linked Data principles and (b) Service-orientation to resolve the integration issues for sharing educational resources

Using semantic indexing to improve searching performance in web archives

The sheer volume of electronic documents being published on the Web can be overwhelming for users if the searching aspect is not properly addressed. This problem is particularly acute inside archives and repositories containing large collections of web resources or, more precisely, web pages and other web objects. Using the existing search capabilities in web archives, results can be compromised because of the size of data, content heterogeneity and changes in scientific terminologies and meanings. During the course of this research, we will explore whether semantic web technologies, particularly ontology-based annotation and retrieval, could improve precision in search results in multi-disciplinary web archives

A lightweight web video model with content and context descriptions for integration with linked data

The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud

Towards a killer app for the Semantic Web

Killer apps are highly transformative technologies that create new markets and widespread patterns of behaviour. IT generally, and the Web in particular, has benefited from killer apps to create new networks of users and increase its value. The Semantic Web community on the other hand is still awaiting a killer app that proves the superiority of its technologies. There are certain features that distinguish killer apps from other ordinary applications. This paper examines those features in the context of the Semantic Web, in the hope that a better understanding of the characteristics of killer apps might encourage their consideration when developing Semantic Web applications

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Demo: A community based approach for managing ontology alignments

The Semantic Web is rapidly becoming a defacto distributed repository for semantically represented data, thus leveraging on the added on value of the network effect. Various ontology mapping techniques and tools have been devised to facilitate the bridging and integration of distributed data repositories. Nevertheless, ontology mapping can benefit from human supervision to increase accuracy of results. The spread of Web 2.0 approaches demonstrate the possibility of using collaborative techniques for reaching consensus. While a number of prototypes for collaborative ontology construction are being developed, collaborative ontology mapping is not yet well investigated. In this paper, we describe aprototype that combines off-the-shelf ontology mapping tools with social software techniques to enable users to collaborate on mapping ontologies. Emphasis is put on the reuse of user generated mappings to improve the accuracy of automatically generated ones

Linked education: interlinking educational resources and the web of data

Research on interoperability of technology-enhanced learning (TEL) repositories throughout the last decade has led to a fragmented landscape of competing approaches, such as metadata schemas and interface mechanisms. However, so far Web-scale integration of resources is not facilitated, mainly due to the lack of take-up of shared principles, datasets and schemas. On the other hand, the Linked Data approach has emerged as the de-facto standard for sharing data on the Web and offers a large potential to solve interoperability issues in the field of TEL. In this paper, we describe a general approach to exploit 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. This approach has been implemented in the context of the mEducator project where data from a number of open TEL data repositories has been integrated, exposed and enriched by following Linked Data principles

Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web

Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C’s Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers’ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun