Applying Linked Data Technologies in the Social Sciences

2010 IEEE Sixth …, 2010

Scientific data stands to represent a significant portion of the linked open data cloud and science itself stands to benefit from the data fusion capability that this will afford. However, simply publishing linked data into the cloud does not necessarily meet the requirements of reuse. Publishing has requirements of provenance, quality, credit, attribution, methods in order to provide the reproducibility that allows validation of results. In this paper we make the case for a scientific data publication model on top of linked data and introduce the notion of Research Objects as first class citizens for sharing and publishing.

2012

Springer eBooks, 2019

This chapter presents Linked Data, a new form of distributed data on the web which is especially suitable to be manipulated by machines and to share knowledge. By adopting the linked data publication paradigm, anybody can publish data on the web, relate it to data resources published by others and run artificial intelligence algorithms in a smooth manner. Open linked data resources may democratize the future access to knowledge by the mass of internet users, either directly or mediated through algorithms. Governments have enthusiastically adopted these ideas, which is in harmony with the broader open data movement.

Edulearn14 Proceedings

Semantic Web encourages digital libraries, including open access journals, to collect, link and share their data across the Web in order to ease its processing by machines and humans to get better queries and results. Linked Data technologies enable connecting related data across the Web using the principles and recommendations set out by Tim Berners-Lee in 2006. Several universities develop knowledge through scholarship and research with open access policies for the generated knowledge, using several ways to disseminate information. Open access journals collect, preserve and publish scientific information in digital form related to a particular academic discipline in a peer review process having a big potential for exchanging and spreading their data linked to external resources using Linked Data technologies. Linked Data can increase those benefits with better queries about the resources and their relationships. This paper reports a process for publishing scientific data on the Web using Linked Data technologies. Furthermore, methodological guidelines are presented with related activities. The proposed process was applied extracting data from a university Open Journal System and publishing in a SPARQL endpoint using the open source edition of OpenLink Virtuoso. In this process, the use of open standards facilitates the creation, development and exploitation of knowledge

2015

While the amount of data on the Web grows at 57 % per year, the Web of Science maintains a considerable amount of inertia, as yearly growth varies between 1.6 % and 14 %. On the other hand, the Web of Science consists of high quality information created and reviewed by the international community of researchers. While it is a complicated process to switch from traditional publishing methods to methods, which enable data publishing in machine-readable formats, the situation can be improved by at least exposing metadata about the scientific publications in machine-readable format. In this paper we aim at metadata, hidden inside universities’ internal databases, reports and other hard to discover sources. We extend the VIVO ontology and create the VIVO+ ontology. We define and describe a framework for automatic conversion of university data to RDF. We showcase the VIVO+ ontology and the framework using the example of the ITMO university.

Lecture Notes in Computer Science, 2015

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 widespread 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.

Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age

The number of Open Statistical Data available for reuse is rapidly increasing. Linked open data technology enables easy reuse and linking of data residing in different locations in a simple and straightforward manner. Yet, many people are not familiar with the technology standards and tools for making use of open statistical data. In this tutorial, we will introduce Linked Open Statistical Data (LOSD) and demonstrate the use of LOSD technologies and tools to visualize open data obtained from various European Countries. We will also give the participants the opportunity to use these tools thus obtaining a personal experience on their capabilities.

Lecture Notes in Computer Science, 2014

The idea of Linked Data is to aggregate, harmonize, integrate, enrich, and publish data for re-use on the Web in a cost-efficient way using Semantic Web technologies. We concern two major hindrances for re-using Linked Data: It is often difficult for a re-user to 1) understand the characteristics of the dataset and 2) evaluate the quality the data for the intended purpose. This paper introduces the "Linked Data Finland" platform LDF.fi addressing these issues. We extend the famous 5-star model of Tim Berners-Lee, with the sixth star for providing the dataset with a schema that explains the dataset, and the seventh star for validating the data against the schema. LDF.fi also automates data publishing and provides data curation tools. The first prototype of the platform is available on the web as a service, hosting tens of datasets and supporting several applications.

2012

Abstract. Within complex scientific domains such as pharmacology, operational equivalence between two concepts is often context-, user-and task-specific. Existing Linked Data integration procedures and equivalence services do not take the context and task of the user into account. We present a vision for enabling users to control the notion of operational equivalence by applying scientific lenses over Linked Data. The scientific lenses vary the links that are activated between the datasets which affects the data returned to the user.

Semantic Services, Interoperability and Web Applications

The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

2012

Research Information Systems (RIS) play a critical role in the sharing of scientific information and provide researchers, professionals and decision makers with the required data for their activities. Existing RIS standards have proposed data models to represent the main entities for storage and exchange. These account for the needs of multiple stakeholders through a high flexibility based on a formal syntax and declared semantics, but for techno-historical reasons they assume the completeness of information within system boundaries.

Cornell University - arXiv, 2019

Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identified by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want to benefit from it as background knowledge for supporting their tasks. LOD has emerged as the backbone of applications in diverse fields such as Natural Language Processing, Information Retrieval, Computer Vision, Speech Recognition, and many more. Nevertheless, regardless of the specific tasks that LOD-based tools aim to address, the reuse of such knowledge may be challenging for diverse reasons, e.g. semantic heterogeneity, provenance, and data quality. As aptly stated by Heath et al. "Linked Data might be outdated, imprecise, or simply wrong": there arouses a necessity to investigate the problem of linked data validity. This work reports a collaborative effort performed by nine teams of students, guided by an equal number of senior researchers, attending the International Semantic Web Research School (ISWS 2018) towards addressing such investigation from different perspectives coupled with different approaches to tackle the issue.

In this demo paper we introduce a Linked Data-driven, Semantically-enabled Journal Portal (SEJP) that offers a variety of interactive scientometrics modules. SEJP allows editors, reviewers, authors, and readers to explore and analyze (meta)data published by a journal. Besides Linked Data created from the journal's internal data, SEJP also links out to other sources and includes them to develop more powerful modules. These modules range from simple descriptive statistics, over the spatial analysis of visitors and authors, to topic trending modules. While SEJP will be available for multiple journals, this paper shows its deployment to the Semantic Web journal by IOS Press. Due to its open & transparent review process, SWJ offers a wide variety of additional information, e.g., about reviewers, editors, paper decisions, and so forth.

Scientific Data, 2021

While the biomedical community has published several "open data" sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from multiple sources. To tackle these challenges, the community has experimented with Semantic Web and linked data technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from more than 80 biomedical linked open data sources into an LSLOD schema graph and conduct an empirical meta-analysis to evaluate the extent of semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective. We envision that the LSLOD schema graph and the findings from this research will aid researchers who wish to query and integrate data and knowledge from multiple biomedical sources simultaneously on the Web.

Lecture Notes in Computer Science, 2016

OpenAIRE, the Open Access Infrastructure for Research in Europe, enables search, discovery and monitoring of the publications and datasets from 100,000+ research projects. Increasing the reusability of the OpenAIRE research metadata, connecting them to other open data about projects, publications, people and organizations, and reaching out to further related domains requires better technical interoperability, which we aim at achieving by exposing the OpenAIRE Information Space as Linked Data. We present a scalable and maintainable architecture that converts the OpenAIRE data from its original HBase NoSQL source to RDF. We furthermore explore how this novel integration of data about research can facilitate scholarly communication. 3 4