WikiNEXT: a wiki for exploiting the web of data

International audienceThis paper presents WikiNEXT, a semantic application wiki. WikiNEXT lies on the border between application wikis and modern web based IDEs (Integrated Development Environments) like jsbin.com, jsfiddle.net, cloud9ide.com, etc. It has been initially created for writing documents that integrate data from external data sources of the web of data, such as DBPedia.org or FreeBase.com, or for writing interactive tutorials (e.g. an HTML5 tutorial, a semantic web programming tutorial) that mix text and interactive examples in the same page. The system combines some powerful aspects from (i) wikis, such as ease of use, collaboration and openness, (ii) semantic web/wikis such as making information processable by machines and (iii) web-based IDEs such as instant development and code testing in a web browser. WikiNEXT is for writing documents/pages as well as for writing web applications that manipulate semantic data, either locally or coming from the web of data. These applications can be created, edited or cloned in the browser and can be used for integrating data visualizations in wiki pages, for annotating content with metadata, or for any kind of processing. WikiNEXT is particularly suited for teaching web technologies or for writing documents that integrate data from the web of data

KnowledgePuzzle: a browsing tool to adapt the web navigation process to the learner's mental model

This article presents KnowledgePuzzle, a browsing tool for knowledge construction from the web. It aims to adapt the structure of web content to the learner’s information needs regardless of how the web content is originally delivered. Learners are provided with a meta-cognitive space (eg, a concept mapping tool) that enables them to plan navigation paths and visualize the semantic processing of knowledge in their minds. Once the learner’s viewpoint becomes visually represented, it will be transformed to a layer of informative hyperlinks and annotations over previously visited pages. The attached layer causes the web content to be explicitly structured to accommodate the learner’s interests by interlinking and annotating chunks of information that make up the learner’s knowledge. Finally, a hypertext version of the whole knowledge is generated to enable fast and easy reviewing. A discussion about the

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 Machine Learning Based Analytical Framework for Semantic Annotation Requirements

The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as multilinguality, scalability, and issues which are related to diversity and inconsistency in content of different web pages. Due to the wide range of domains and the dynamic environments that the Semantic Annotation systems must be performed on, the problem of automating annotation process is one of the significant challenges in this domain. To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learning have been utilized. In this paper we present an inclusive layered classification of Semantic Annotation challenges and discuss the most important issues in this field. Also, we review and analyze machine learning applications for solving semantic annotation problems. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a framework that can map machine learning techniques into the Semantic Annotation challenges and requirements

Complete Semantics to empower Touristic Service Providers

The tourism industry has a significant impact on the world's economy, contributes 10.2% of the world's gross domestic product in 2016. It becomes a very competitive industry, where having a strong online presence is an essential aspect for business success. To achieve this goal, the proper usage of latest Web technologies, particularly schema.org annotations is crucial. In this paper, we present our effort to improve the online visibility of touristic service providers in the region of Tyrol, Austria, by creating and deploying a substantial amount of semantic annotations according to schema.org, a widely used vocabulary for structured data on the Web. We started our work from Tourismusverband (TVB) Mayrhofen-Hippach and all touristic service providers in the Mayrhofen-Hippach region and applied the same approach to other TVBs and regions, as well as other use cases. The rationale for doing this is straightforward. Having schema.org annotations enables search engines to understand the content better, and provide better results for end users, as well as enables various intelligent applications to utilize them. As a direct consequence, the region of Tyrol and its touristic service increase their online visibility and decrease the dependency on intermediaries, i.e. Online Travel Agency (OTA).Comment: 18 pages, 6 figure