Seed, an End-User Text Composition Tool for the Semantic Web

Despite developments of Semantic Web-enabling technologies, the gap between non-expert end-users and the Semantic Web still exists. In the field of semantic content authoring, tools for interacting with semantic content remain directed at highly trained individuals. This adds to the challenges of bringing user-generated content into the Semantic Web. In this paper, we present Seed, short for Semantic Editor, an extensible knowledge-supported natural language text composition tool for non-experienced end-users. It enables automatic as well as semi-automatic creation of standards based semantically annotated textual content with focus on the task of text composition. We point out the structure of Seed, compare it with related work and explain how it excels at utilizing Linked Open Data and state of the art Natural Language Processing to realize user-friendly generation of textual content for the Semantic Web. We also present experimental evaluation results involving a diverse group of 120 participants, which showed that Seed helped end-users easily create and interact with semantic content with nearly no prerequisite knowledge.