798 research outputs found
ChartCheck: An Evidence-Based Fact-Checking Dataset over Real-World Chart Images
Data visualizations are common in the real-world. We often use them in data
sources such as scientific documents, news articles, textbooks, and social
media to summarize key information in a visual form. Charts can also mislead
its audience by communicating false information or biasing them towards a
specific agenda. Verifying claims against charts is not a straightforward
process. It requires analyzing both the text and visual components of the
chart, considering characteristics such as colors, positions, and orientations.
Moreover, to determine if a claim is supported by the chart content often
requires different types of reasoning. To address this challenge, we introduce
ChartCheck, a novel dataset for fact-checking against chart images. ChartCheck
is the first large-scale dataset with 1.7k real-world charts and 10.5k
human-written claims and explanations. We evaluated the dataset on
state-of-the-art models and achieved an accuracy of 73.9 in the finetuned
setting. Additionally, we identified chart characteristics and reasoning types
that challenge the models
A systematic literature review on Wikidata
To review the current status of research on Wikidata and, in particular, of articles that either describe applications of Wikidata or provide empirical evidence, in order to uncover the topics of interest, the fields that are benefiting from its applications and which researchers and institutions are leading the work
A matter of words: NLP for quality evaluation of Wikipedia medical articles
Automatic quality evaluation of Web information is a task with many fields of
applications and of great relevance, especially in critical domains like the
medical one. We move from the intuition that the quality of content of medical
Web documents is affected by features related with the specific domain. First,
the usage of a specific vocabulary (Domain Informativeness); then, the adoption
of specific codes (like those used in the infoboxes of Wikipedia articles) and
the type of document (e.g., historical and technical ones). In this paper, we
propose to leverage specific domain features to improve the results of the
evaluation of Wikipedia medical articles. In particular, we evaluate the
articles adopting an "actionable" model, whose features are related to the
content of the articles, so that the model can also directly suggest strategies
for improving a given article quality. We rely on Natural Language Processing
(NLP) and dictionaries-based techniques in order to extract the bio-medical
concepts in a text. We prove the effectiveness of our approach by classifying
the medical articles of the Wikipedia Medicine Portal, which have been
previously manually labeled by the Wiki Project team. The results of our
experiments confirm that, by considering domain-oriented features, it is
possible to obtain sensible improvements with respect to existing solutions,
mainly for those articles that other approaches have less correctly classified.
Other than being interesting by their own, the results call for further
research in the area of domain specific features suitable for Web data quality
assessment
Biological Systems Workbook: Data modelling and simulations at molecular level
Nowadays, there are huge quantities of data surrounding the different fields of biology derived from experiments and theoretical simulations, where results are often stored in biological databases that are growing at a vertiginous rate every year. Therefore, there is an increasing research interest in the application of mathematical and physical models able to produce reliable predictions and explanations to understand and rationalize that information. All these investigations are helping to overcome biological questions pushing forward in the solution of problems faced by our society.
In this Biological Systems Workbook, we aim to introduce the basic pieces allowing life to take place, from the 3D structural point of view. We will start learning how to look at the 3D structure of molecules from studying small organic molecules used as drugs. Meanwhile, we will learn some methods that help us to generate models of these structures. Then we will move to more complex natural organic molecules as lipid or carbohydrates, learning how to estimate and reproduce their dynamics. Later, we will revise the structure of more complex macromolecules as proteins or DNA. Along this process, we will refer to different computational tools and databases that will help us to search, analyze and model the different molecular systems studied in this course
Discovery and publishing of primary biodiversity data associated with multimedia resources: The Audubon Core strategies and approaches
The Audubon Core Multimedia Resource Metadata Schema is a representation-free vocabulary for the description of biodiversity multimedia resources and collections, now in the final stages as a proposed Biodiversity Informatics Standards (TDWG) standard. By defining only six terms as mandatory, it seeks to lighten the burden for providing or using multimedia useful for biodiversity science. At the same time it offers rich optional metadata terms that can help curators of multimedia collections provide authoritative media that document species occurrence, ecosystems, identification tools, ontologies, and many other kinds of biodiversity documents or data. About half of the vocabulary is re-used from other relevant controlled vocabularies that are often already in use for multimedia metadata, thereby reducing the mapping burden on existing repositories. A central design goal is to allow consuming applications to have a high likelihood of discovering suitable resources, reducing the human examination effort that might be required to decide if the resource is fit for the purpose of the application
NFDI4Culture - Consortium for research data on material and immaterial cultural heritage
Digital data on tangible and intangible cultural assets is an essential part of daily life, communication and experience. It has a lasting influence on the perception of cultural identity as well as on the interactions between research, the cultural economy and society. Throughout the last three decades, many cultural heritage institutions have contributed a wealth of digital representations of cultural assets (2D digital reproductions of paintings, sheet music, 3D digital models of sculptures, monuments, rooms, buildings), audio-visual data (music, film, stage performances), and procedural research data such as encoding and annotation formats. The long-term preservation and FAIR availability of research data from the cultural heritage domain is fundamentally important, not only for future academic success in the humanities but also for the cultural identity of individuals and society as a whole. Up to now, no coordinated effort for professional research data management on a national level exists in Germany. NFDI4Culture aims to fill this gap and create a usercentered, research-driven infrastructure that will cover a broad range of research domains from musicology, art history and architecture to performance, theatre, film, and media studies.
The research landscape addressed by the consortium is characterized by strong institutional differentiation. Research units in the consortium's community of interest comprise university institutes, art colleges, academies, galleries, libraries, archives and museums. This diverse landscape is also characterized by an abundance of research objects, methodologies and a great potential for data-driven research. In a unique effort carried out by the applicant and co-applicants of this proposal and ten academic societies, this community is interconnected for the first time through a federated approach that is ideally suited to the needs of the participating researchers. To promote collaboration within the NFDI, to share knowledge and technology and to provide extensive support for its users have been the guiding principles of the consortium from the beginning and will be at the heart of all workflows and decision-making processes. Thanks to these principles, NFDI4Culture has gathered strong support ranging from individual researchers to highlevel cultural heritage organizations such as the UNESCO, the International Council of Museums, the Open Knowledge Foundation and Wikimedia. On this basis, NFDI4Culture will take innovative measures that promote a cultural change towards a more reflective and sustainable handling of research data and at the same time boost qualification and professionalization in data-driven research in the domain of cultural heritage. This will create a long-lasting impact on science, cultural economy and society as a whole
Bravo MaRDI: A Wikibase Powered Knowledge Graph on Mathematics
Mathematical world knowledge is a fundamental component of Wikidata. However,
to date, no expertly curated knowledge graph has focused specifically on
contemporary mathematics. Addressing this gap, the Mathematical Research Data
Initiative (MaRDI) has developed a comprehensive knowledge graph that links
multimodal research data in mathematics. This encompasses traditional research
data items like datasets, software, and publications and includes semantically
advanced objects such as mathematical formulas and hypotheses. This paper
details the abilities of the MaRDI knowledge graph, which is based on Wikibase,
leading up to its inaugural public release, codenamed Bravo, available on
https://portal.mardi4nfdi.de.Comment: Accepted at Wikidata'23: Wikidata workshop at ISWC 202
Abstract Images Have Different Levels of Retrievability Per Reverse Image Search Engine
Much computer vision research has focused on natural images, but technical
documents typically consist of abstract images, such as charts, drawings,
diagrams, and schematics. How well do general web search engines discover
abstract images? Recent advancements in computer vision and machine learning
have led to the rise of reverse image search engines. Where conventional search
engines accept a text query and return a set of document results, including
images, a reverse image search accepts an image as a query and returns a set of
images as results. This paper evaluates how well common reverse image search
engines discover abstract images. We conducted an experiment leveraging images
from Wikimedia Commons, a website known to be well indexed by Baidu, Bing,
Google, and Yandex. We measure how difficult an image is to find again
(retrievability), what percentage of images returned are relevant (precision),
and the average number of results a visitor must review before finding the
submitted image (mean reciprocal rank). When trying to discover the same image
again among similar images, Yandex performs best. When searching for pages
containing a specific image, Google and Yandex outperform the others when
discovering photographs with precision scores ranging from 0.8191 to 0.8297,
respectively. In both of these cases, Google and Yandex perform better with
natural images than with abstract ones achieving a difference in retrievability
as high as 54\% between images in these categories. These results affect anyone
applying common web search engines to search for technical documents that use
abstract images.Comment: 20 pages; 7 figures; to be published in the proceedings of the
Drawings and abstract Imagery: Representation and Analysis (DIRA) Workshop
from ECCV 202
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