Much has been written regarding the importance of combining visual and textual information to enhance knowledge acquisition (Paivio, 1971, 1986; Mayer & Anderson, 1992). However, the combination of images and text still needs further analysis (Faber, 2012; Prieto, 2008; Prieto & Faber, 2012). An in-depth analysis of the features of images provides the means to develop selection criteria for specific representation purposes. The combination of conceptual content, image type based on morphological characteristics, and functional criteria can be used to enhance the selection and annotation of images that explicitly focus on the conceptual propositions that best define concepts in a knowledge base. Manzanilla is an image annotation tool specifically created for EcoLexicon, a multilingual and multimodal terminological knowledge base (TKB) on the environment. It is powered by Camomile (Poignant et al., 2016) according to the selection and annotation criteria resulting from ten years of research on multimodality within the framework of Frame-Based Terminology (FBT; Faber, León-Araúz & Reimerink, 2014). The tool was created to enhance the consistency of knowledge representation through images with the conceptual knowledge in EcoLexicon and to improve image reusability.
@InProceedings{REIMERINK18.248, author = {Arianne Reimerink and Pilar León-Araúz}, title = "{Manzanilla: An Image Annotation Tool for TKB Building}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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