2,868 research outputs found
Hypermedia-based discovery for source selection using low-cost linked data interfaces
Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed-even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness
Enabling Web-scale data integration in biomedicine through Linked Open Data
The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems
Reply to: Soils need to be considered when assessing the impacts of land-use change on carbon sequestration
Industrial Ecolog
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
DECORAIT -- DECentralized Opt-in/out Registry for AI Training
We present DECORAIT; a decentralized registry through which content creators
may assert their right to opt in or out of AI training as well as receive
reward for their contributions. Generative AI (GenAI) enables images to be
synthesized using AI models trained on vast amounts of data scraped from public
sources. Model and content creators who may wish to share their work openly
without sanctioning its use for training are thus presented with a data
governance challenge. Further, establishing the provenance of GenAI training
data is important to creatives to ensure fair recognition and reward for their
such use. We report a prototype of DECORAIT, which explores hierarchical
clustering and a combination of on/off-chain storage to create a scalable
decentralized registry to trace the provenance of GenAI training data in order
to determine training consent and reward creatives who contribute that data.
DECORAIT combines distributed ledger technology (DLT) with visual
fingerprinting, leveraging the emerging C2PA (Coalition for Content Provenance
and Authenticity) standard to create a secure, open registry through which
creatives may express consent and data ownership for GenAI.Comment: Proc. of the 20th ACM SIGGRAPH European Conference on Visual Media
Productio
Digital Perspectives in History
This article outlines the state of digital perspectives in historical research, some of the methods and tools in use by digital historians, and the possible or even necessary steps in the future development of the digital approach. We begin by describing three main computational approaches: digital databases and repositories, network analysis, and Machine Learning. We also address data models and ontologies in the larger context of the demand for sustainability and linked research data. The section is followed by a discussion of the (much needed) standards and policies concerning data quality and transparency. We conclude with a consideration of future scenarios and challenges for computational research
Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines
A cross-disciplinary examination of the user behaviours involved in seeking
and evaluating data is surprisingly absent from the research data discussion.
This review explores the data retrieval literature to identify commonalities in
how users search for and evaluate observational research data. Two analytical
frameworks rooted in information retrieval and science technology studies are
used to identify key similarities in practices as a first step toward
developing a model describing data retrieval
A Web GIS-based Integration of 3D Digital Models with Linked Open Data for Cultural Heritage Exploration
This PhD project explores how geospatial semantic web concepts, 3D web-based visualisation, digital interactive map, and cloud computing concepts could be integrated to enhance digital cultural heritage exploration; to offer long-term archiving and dissemination of 3D digital cultural heritage models; to better interlink heterogeneous and sparse cultural heritage data.
The research findings were disseminated via four peer-reviewed journal articles and a conference article presented at GISTAM 2020 conference (which received the âBest Student Paper Awardâ)
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