Keyword-based linked data information retrieval is an easy choice for general-purpose users, but the implementation of such an approach is a challenge because mere keywords do not hold semantic information. Some studies have incorporated templates in an effort to bridge this gap, but most such approaches have proven ineffective because of inefficient template management. Because linked data can be presented in a structured format, we can assume that the data's internal statistics can be used to effectively influence template management. In this work, we explore the use of this influence for template creation, ranking, and scaling. Then, we demonstrate how our proposal for automatic linked data information retrieval can be used alongside familiar keyword-based information retrieval methods, and can also be incorporated alongside other techniques, such as ontology inclusion and sophisticated matching, in order to achieve increased levels of performance.

This paper develops an efficient mechanism for extracting primary information requests from 'Seek-Object' type query messages. The mechanism consists of three steps. The first step extracts sentences which signal that the query is 'Seek-Object' type by recognizing distinctive surface expressions. The second step, biased by the expression patterns, analyzes their internal structures. The third step integrates these fragments by a partial discourse processing and represents writers' goal-directed information request; as these sentences often include referential expressions and the referred expressions are in background goal descriptions. We claim the mechanism can extract information requests fairly accurately, by showing evaluation results.