In research and policies, the identification of trends as well as emerging topics and topics in decline is an important source of information for both academic and innovation management. Since at present policy analysis mostly employs qualitative research methods, the following article presents and assesses different approaches – trend analysis based on questionnaires, quantitative bibliometric surveys, the use of computer-linguistic approaches and machine learning and qualitative investigations. Against this backdrop, this article examines digital applications in cultural heritage and, in particular, built heritage via various investigative frameworks to identify topics of relevance and trendlines, mainly for European Union (EU)-based research and policies. Furthermore, this article exemplifies and assesses the specific opportunities and limitations of the different methodical approaches against the backdrop of data-driven vs. data-guided analytical frameworks. As its major findings, our study shows that both research and policies related to digital applications for cultural heritage are mainly driven by the availability of new technologies. Since policies focus on meta-topics such as digitisation, openness or automation, the research descriptors are more granular. In general, data-driven approaches are promising for identifying topics and trendlines and even predicting the development of near future trends. Conversely, qualitative approaches are able to answer “why” questions with regard to whether topics are emerging due to disruptive innovations or due to new terminologies or whether topics are becoming obsolete because they are common knowledge, as is the case for the term “internet”.
In this paper, I will describe a system that was developed for the task of Visual Question Answering. The system uses the rich type universe of Type Theory with Records (TTR) to model utterances about the image, the image itself, and classifications made relating the outcomes of these two tasks. At its most basic, the decision of whether any given predicate can be assigned to an object in the image is delegated to a CNN. Consequently, images can be taken as evidence for propositional judgments. The end result is a model whose application of perceptual classifiers to a given image is guided by the accompanying utterance.
Abstract. This article looks at approaches, software solutions, standards, workflows, and quality criteria to create a multimodal dataset including images, textual information, and 3D models for a small urban area. The goal is to improve art historical research on architectural elements relying on the three data entities. A specific dataset with manually created annotations is introduced and made available to the public. The paper provides an overview of the available data and detailed information on the preparation of the different types of data as well as the process of connecting everything through annotations. It mentions the relevance and creation of a controlled vocabulary. Furthermore, point cloud processing as well as neural network approaches are discussed which may replace manual labelling. Another focus is the analysis of linguistic similarities to identify whether annotations are actually connected and therefore relevant. Additionally, research scenarios will highlight the relevance of the approach for art history and the contributions, which come from computer linguistics and computer science.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.