Image selection and annotation for an environmental knowledge base

Abstract

Images play an important role in the representation and acquisition of specialized knowledge. Not surprisingly, terminological knowledge bases (TKBs) often include images as a way to enhance the information in concept entries. However, the selection of these images should not be random, but rather based on specific guidelines that take into account the type and nature of the concept being described. This paper presents a proposal on how to combine the features of images with the conceptual propositions in EcoLexicon, a multilingual TKB on the environment. This proposal is based on the following: (1) the combinatory possibilities of concept types; (2) image types, such as photographs, drawings and flow charts; (3) morphological features or visual knowledge patterns (VKPs), such as labels, colours, arrows, and their effect on the functional nature of each image type. Currently, images are stored in association with concept entries according to the semantic content of their definitions, but they are not described or annotated according to the parameters that guided their selection, which would undoubtedly contribute to the systematization and automatization of the process. First, the images included in EcoLexicon were analyzed in terms of their adequateness, the semantic relations expressed, the concept types and their VKPs. Then, with these data, guidelines for image selection and annotation were created. The final aim is twofold: (1) to systematize the selection of images and (2) to start annotating old and new images so that the system can automatically allocate them in different concept entries based on shared conceptual propositions.