SUMMARYLinked data endpoints are online query gateways to semantically annotated linked data sources. In order to query these data sources, SPARQL query language is used as a standard. Although a linked data endpoint (i.e. SPARQL endpoint) is a basic Web service, it provides a platform for federated online querying and data linking methods. For linked data consumers, SPARQL endpoint availability and discovery are crucial for live querying and semantic information retrieval. Current studies show that availability of linked datasets is very low, while the locations of linked data endpoints change frequently. There are linked data respsitories that collect and list the available linked data endpoints or resources. It is observed that around half of the endpoints listed in existing repositories are not accessible (temporarily or permanently offline). These endpoint URLs are shared through repository websites, such as Datahub.io, however, they are weakly maintained and revised only by their publishers. In this study, a novel metacrawling method is proposed for discovering and monitoring linked data sources on the Web. We implemented the method in a prototype system, named SPARQL Endpoints Discovery (SpEnD). SpEnD starts with a "search keyword" discovery process for finding relevant keywords for the linked data domain and specifically SPARQL endpoints. Then, the collected search keywords are utilized to find linked data sources via popular search engines (Google, Bing, Yahoo, Yandex). By using this method, most of the currently listed SPARQL endpoints in existing endpoint repositories, as well as a significant number of new SPARQL endpoints, have been discovered. We analyze our findings in comparison to Datahub collection in detail.

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Model-driven approach for web application development is an important topic in software engineering. There are many existing tools to support model-driven engineering for web application development. However, most tools and techniques are complex and not very practical when it comes to real-life usage. Here we present a simple data model-driven approach for web application development that is based on RDF data model, the basic semantic Web data model, and its reasoning capabilities. We introduce a prototype implementation of the data model-driven Web application development framework that utilizes semantic Web technologies in the backend for the data model. In this framework, the design and development of the application is focused on the RDF data model and its reasoning logic (partially). Developers define the data model online using a Web application front-end and the framework generates different views of the data elements automatically. This enables the developers to change the data model easily whenever it is needed.

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