Question-Answering System with Linguistic Summarization

The increased popularity of Linked Open Data (LOD) and advances in Natural Language Processing techniques have led to the development of Question Answering Systems (QASs) that utilize Knowledge Graphs as data sources. QASs perform well on simple questions providing precise and concise answers. Yet, most of them cannot process answers that contain a large volume of numerical values and are not able to provide users with answers in a human-friendly format. In this paper, we propose a user-defined method for constructing linguistic summarization of multi-feature data. It selects suitable summarizers and quantifiers and works with linguistic constraints imposed on the data. The method relies on definitions of linguistic terms constructed by users using an easy and simple graphical interface. Additionally, we introduce a Context-based User-defined Weighted Averaging (CUWA) operator. It allows determining an average value of data that satisfies multiple constraints that are account for the context defined by the user. We include several illustrative examples.