Two-staged approach for semantically annotating and brokering TV-related services

Nowadays, more and more distributed digital TV and TV-related resources are published on the Web, such as Electronic Personal TV Guide (EPG) data. To enable applications to access these resources easily, the TV resource data is commonly provided by Web service technologies. The huge variety of data related to the TV domain and the wide range of services that provide it, raises the need to have a broker to discover, select and orchestrate services to satisfy the runtime requirements of applications that invoke these services. The variety of data and heterogeneous nature of the service capabilities makes it a challenging domain for automated web-service discovery and composition. To overcome these issues, we propose a two-stage service annotation approach, which is resolved by integrating Linked Services and IRS-III semantic web services framework, to complete the lifecycle of service annotating, publishing, deploying, discovering, orchestration and dynamic invocation. This approach satisfies both developer's and application's requirements to use Semantic Web Services (SWS) technologies manually and automatically

Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

Developing a Benchmark Suite for Semantic Web Data from Existing Workflows

This paper presents work in progress towards developing a new benchmark for federated query processing systems. Unlike other popular benchmarks, our queryset is not driven by technical evaluation, but is derived from workflows established by the pharmacology community. The value of this queryset is that it is realistic but at the same time it comprises complex queries that test all features of modern query processing systems

Publishing a Knowledge Organization System as Linked Data: The Case of the Universal Decimal Classification

Linked data (LD) technology is hailed as a long-awaited solution in web-based information exchange. Linked Open Data (LOD) bring this to another level by enabling meaningful linking of resources and creating a global, openly accessible knowledge graph. Our case is the Universal Decimal Classification (UDC) and the challenges for a KOS service provider to maintain an LD service. UDC was created during the period 1896--1904 to support systematic organization and information retrieval of a bibliography. When discussing UDC as LD we make a distinction between two types of UDC data or two provenances: UDC source data, and UDC codes as they appear in metadata. To serve the purpose of supplying semantics one has to front--end UDC LD with a service that can parse and interpret complex UDC strings. While the use of UDC is free the publishing and distributing of UDC data is protected by a licence. Publishing of UDC both as LD and as LOD must be provided for within a complex service that would allow open access as well as access through a paywall barrier for different levels of licences. The practical task of publishing the UDC as LOD was informed by the '10Things guidelines'. The process includes conceptual parts and technological parts. The transition to a new technology is never a purely mechanical act but is a research endeavour in its own right. The UDC case has shown the importance of cross-domain, inter-disciplinary collaboration which needs experts well situated in multiple knowledge domains

Aligning restricted access data with FAIR: a systematic review

Understanding the complexity of restricted research data is vitally important in the current new era of Open Science. While the FAIR Guiding Principles have been introduced to help researchers to make data Findable, Accessible, Interoperable and Reusable, it is still unclear how the notions of FAIR and Openness can be applied in the context of restricted data. Many methods have been proposed in support of the implementation of the principles, but there is yet no consensus among the scientific community as to the suitable mechanisms of making restricted data FAIR. We present here a systematic literature review to identify the methods applied by scientists when researching restricted data in a FAIR-compliant manner in the context of the FAIR principles. Through the employment of a descriptive and iterative study design, we aim to answer the following three questions: (1) What methods have been proposed to apply the FAIR principles to restricted data?, (2) How can the relevant aspects of the methods proposed be categorized?, (3) What is the maturity of the methods proposed in applying the FAIR principles to restricted data?. After analysis of the 40 included publications, we noticed that the methods found, reflect the stages of the Data Life Cycle, and can be divided into the following Classes: Data Collection, Metadata Representation, Data Processing, Anonymization, Data Publication, Data Usage and Post Data Usage. We observed that a large number of publications used ‘Access Control‘ and ‘Usage and License Terms’ methods, while others such as ‘Embargo on Data Release’ and the use of ‘Synthetic Data’ were used in fewer instances. In conclusion, we are presenting the first extensive literature review on the methods applied to confidential data in the context of FAIR, providing a comprehensive conceptual framework for future research on restricted access dat

Advancing data sharing and reusability for restricted access data on the Web:introducing the DataSet-Variable Ontology

In response to the increasing volume of research data being generated, more and more data portals have been designed to facilitate data findability and accessibility. However, a significant portion of this data remains confidential or restricted due to its sensitive nature, such as patient data or census microdata. While maintaining confidentiality prohibits its public release, the emergence of portals supporting rich metadata can help enable researchers to at least discover the existence of restricted access data, empowering them to assess the suitability of the data before requesting access. Existing standards, such as CSV on the Web and RDF Data Cube, have been adopted to facilitate data management, integration, and re-use of data on the Web. However, the current landscape still lacks adequate standards not only to effectively describe restricted access data while preserving confidentiality but also to facilitate its discovery. In this work, we investigate the relationship between the structural, statistical, and semantic elements of restricted access tabular data, and we explore how such relationship can be formally modeled in a way that is Findable, Accessible, Interoperable, and Reusable. We introduce the DataSet-Variable Ontology (DSV), that by combining CSV on the Web and RDF Data Cube standards, leveraging semantic technologies and Linked Data principles, and introducing variable-level metadata, aims to capture high-quality metadata to support the management and re-use of restricted access data on the Web. As evaluation, we conducted a case study where we applied DSV to four different datasets from different statistical governmental agencies. We employed a set of competency questions to assess the ontology's ability to support knowledge discovery and data exploration. By describing high-quality metadata, both at the dataset- and variable levels, while maintaining data privacy, this novel ontology facilitates data interoperability, discovery, and re-use and it empowers researchers to manage, integrate, and analyze complex restricted access data sources

AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain Shift

We introduce AmsterTime: a challenging dataset to benchmark visual place recognition (VPR) in presence of a severe domain shift. AmsterTime offers a collection of 2,500 well-curated images matching the same scene from a street view matched to historical archival image data from Amsterdam city. The image pairs capture the same place with different cameras, viewpoints, and appearances. Unlike existing benchmark datasets, AmsterTime is directly crowdsourced in a GIS navigation platform (Mapillary). We evaluate various baselines, including non-learning, supervised and self-supervised methods, pre-trained on different relevant datasets, for both verification and retrieval tasks. Our result credits the best accuracy to the ResNet-101 model pre-trained on the Landmarks dataset for both verification and retrieval tasks by 84% and 24%, respectively. Additionally, a subset of Amsterdam landmarks is collected for feature evaluation in a classification task. Classification labels are further used to extract the visual explanations using Grad-CAM for inspection of the learned similar visuals in a deep metric learning models