Leveraging the web of data via linked widgets

Abstract

Machine-readable datasets that have increasingly become available in open formats in recent years have great potential as a foundation for innovative applications and services. Linked Data in particular-a set of best practices for publishing and connecting structured data on the Web-has facilitated significant progress in evolving the Web of documents into a Web of Data. However, although this concept has opened up many opportunities for data sharing and collaboration, integrating data is still a challenging task that requires considerable technical expertise and a profound understanding of the underlying datasets. In this paper, we introduce a novel approach to provide knowledge workers with the necessary tools to leverage the fast growing Linked Data Cloud by creating semantic-aware dataflow processes. To this end, we introduce the "Linked Widget" concept as an enhancement of standard Web Widgets. These widgets are based on a semantic data model that facilitates powerful mechanisms for gathering, processing, integration, and visualization of data in a user-friendly Mashup environment. By allowing knowledge workers to easily create complex Linked Data applications in an adhoc manner, our approach should contribute towards reducing existing barriers of Linked Data adoption.

Models to represent linguistic linked data

“…• Second, and related to what is described as the lack of expressivity in the LOD cloud (Jain et al 2010), the datasets in the LLD cloud are usually not annotated using the full potential that semantic formalisms allow. This, together with the lack of schema level links (Jain et al 2010;Millard et al 2010;Anjomshoaa et al 2014), highlighted in this survey in the case of lexical semantic models, prevents reasoners from inferring more knowledge, which in turn would help in showing the full potential of LLD. A way to mitigate this issue will come from the application of ontology matching techniques to establish links among the different linguistic models and from the development of specific reasoners ready to deal with linguistic knowledge.…”

“…2010; Anjomshoaa et al . 2014), highlighted in this survey in the case of lexical semantic models, prevents reasoners from inferring more knowledge, which in turn would help in showing the full potential of LLD. A way to mitigate this issue will come from the application of ontology matching techniques to establish links among the different linguistic models and from the development of specific reasoners ready to deal with linguistic knowledge.A third reason here would be that the gap between linguistic knowledge in the sub-disciplines of linguistics and the linguistic knowledge available in linguistic models in the Semantic Web is still very wide.…”