8,623 research outputs found
vSPARQL: A View Definition Language for the Semantic Web
Translational medicine applications would like to leverage the biological and biomedical ontologies, vocabularies, and data sets available on the semantic web. We present a general solution for RDF information set reuse inspired by database views. Our view definition language, vSPARQL, allows applications to specify the exact content that they are interested in and how that content should be restructured or modified. Applications can access relevant content by querying against these view definitions. We evaluate the expressivity of our approach by defining views for practical use cases and comparing our view definition language to existing query languages
Semantic Storage: Overview and Assessment
The Semantic Web has a great deal of momentum behind it. The promise of a âbetter webâ, where information is given well defined meaning and computers are better able to work with it has captured the imagination of a significant number of people, particularly in academia. Language standards such as RDF and OWL have appeared with remarkable speed, and development continues apace. To back up this development, there is a requirement for âsemantic databasesâ, where this data can be conveniently stored, operated upon, and retrieved. These already exist in the form of triple stores, but do not yet fulfil all the requirements that may be made of them, particularly in the area of performing inference using OWL. This paper analyses the current stores along with forthcoming technology, and finds that it is unlikely that a combination of speed, scalability, and complex inferencing will be practical in the immediate future. It concludes by suggesting alternative development routes
Semantic query languages for knowledge-based web services in a construction context
Since the early 2000s, different frameworks were set up to enable web-based collaboration in building projects. Unfortunately, none of these initiatives was granted a long life. Recently, however, the use of web technologies in the building industry has been gaining momentum again, considered some promising technologies for reaching a more interoperable BIM practice. Specifically, this relates to (1) Linked Data and Semantic Web technologies, and (2) cloud-based applications. In order to combine these into a network of interlinked applications and datastores, an agreed-upon mechanism for automatic communication and data retrieval needs to be used. Apart from the W3C standard SPARQL, often considered too high a threshold for developers to implement, there are some recent GraphQL-based solutions that simplify the querying process and its implementation into web services. In this paper, we review two recent open source technologies based on GraphQL, that enable to query Linked Data on the web: GraphQL-LD and HyperGraphQL
BioCloud Search EnGene: Surfing Biological Data on the Cloud
The massive production and spread of biomedical data around the web introduces new challenges related to identify computational approaches for providing quality search and browsing of web resources. This papers presents BioCloud Search EnGene (BSE), a cloud application that facilitates searching and integration of the many layers of biological information offered by public large-scale genomic repositories. Grounding on the concept of dataspace, BSE is built on top of a cloud platform that severely curtails issues associated with scalability and performance. Like popular online gene portals, BSE adopts a gene-centric approach: researchers can find their information of interest by means of a simple âGoogle-likeâ query interface that accepts standard gene identification as keywords. We present BSE architecture and functionality and discuss how our strategies contribute to successfully tackle big data problems in querying gene-based web resources. BSE is publically available at: http://biocloud-unica.appspot.com/
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Multi Visualization and Dynamic Query for Effective Exploration of Semantic Data
Semantic formalisms represent content in a uniform way according to ontologies. This enables manipulation and reasoning via automated means (e.g. Semantic Web services), but limits the userâs ability to explore the semantic data from a point of view that originates from knowledge representation motivations. We show how, for user consumption, a visualization of semantic data according to some easily graspable dimensions (e.g. space and time) provides effective sense-making of data. In this paper, we look holistically at the interaction between users and semantic data, and propose multiple visualization strategies and dynamic filters to support the exploration of semantic-rich data.
We discuss a user evaluation and how interaction challenges could be overcome to create an effective user-centred framework for the visualization and manipulation of semantic data. The approach has been implemented and evaluated on a real company archive
A Graph-structured Dataset for Wikipedia Research
Wikipedia is a rich and invaluable source of information. Its central place
on the Web makes it a particularly interesting object of study for scientists.
Researchers from different domains used various complex datasets related to
Wikipedia to study language, social behavior, knowledge organization, and
network theory. While being a scientific treasure, the large size of the
dataset hinders pre-processing and may be a challenging obstacle for potential
new studies. This issue is particularly acute in scientific domains where
researchers may not be technically and data processing savvy. On one hand, the
size of Wikipedia dumps is large. It makes the parsing and extraction of
relevant information cumbersome. On the other hand, the API is straightforward
to use but restricted to a relatively small number of requests. The middle
ground is at the mesoscopic scale when researchers need a subset of Wikipedia
ranging from thousands to hundreds of thousands of pages but there exists no
efficient solution at this scale.
In this work, we propose an efficient data structure to make requests and
access subnetworks of Wikipedia pages and categories. We provide convenient
tools for accessing and filtering viewership statistics or "pagecounts" of
Wikipedia web pages. The dataset organization leverages principles of graph
databases that allows rapid and intuitive access to subgraphs of Wikipedia
articles and categories. The dataset and deployment guidelines are available on
the LTS2 website \url{https://lts2.epfl.ch/Datasets/Wikipedia/}
Twelve Theses on Reactive Rules for the Web
Reactivity, the ability to detect and react to events, is an
essential functionality in many information systems. In particular, Web
systems such as online marketplaces, adaptive (e.g., recommender) systems,
and Web services, react to events such as Web page updates or
data posted to a server.
This article investigates issues of relevance in designing high-level programming
languages dedicated to reactivity on the Web. It presents
twelve theses on features desirable for a language of reactive rules tuned
to programming Web and Semantic Web applications
Investigating the use of semantic technologies in spatial mapping applications
Semantic Web Technologies are ideally suited to build context-aware information retrieval applications. However, the geospatial aspect of context awareness presents unique challenges such as the semantic modelling of geographical references for efficient handling of spatial queries, the reconciliation of the heterogeneity at the semantic and geo-representation levels, maintaining the quality of service and scalability of communicating, and the efficient rendering of the spatial queries' results. In this paper, we describe the modelling decisions taken to solve these challenges by analysing our implementation of an intelligent planning and recommendation tool that provides location-aware advice for a specific application domain. This paper contributes to the methodology of integrating heterogeneous geo-referenced data into semantic knowledgebases, and also proposes mechanisms for efficient spatial interrogation of the semantic knowledgebase and optimising the rendering of the dynamically retrieved context-relevant information on a web frontend
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