ODArchive – Creating an Archive for Structured Data from Open Data Portals

2020 ◽  
pp. 311-327
Author(s):  
Thomas Weber ◽  
Johann Mitöhner ◽  
Sebastian Neumaier ◽  
Axel Polleres
Keyword(s):  
Open Data ◽  
Structured Data

scholarly journals The Pensoft Data Publishing Workflow: The FAIRway from articles to Linked Open Data

2019 ◽  
Vol 3 ◽  
Author(s):  
Lyubomir Penev ◽  
Teodor Georgiev ◽  
Viktor Senderov ◽  
Mariya Dimitrova ◽  
Pavel Stoev
Keyword(s):  
Open Data ◽  
Structured Data ◽  
Linked Open Data ◽  
Data Publishing ◽  
Knowledge Graph ◽  
Supplementary File ◽  
Biodiversity Data ◽  
Text Format ◽  
Biodiversity Knowledge ◽  
Data Elements

As one of the first advocates of open access and open data in the field of biodiversity publishiing, Pensoft has adopted a multiple data publishing model, resulting in the ARPHA-BioDiv toolbox (Penev et al. 2017). ARPHA-BioDiv consists of several data publishing workflows and tools described in the Strategies and Guidelines for Publishing of Biodiversity Data and elsewhere: Data underlying research results are deposited in an external repository and/or published as supplementary file(s) to the article and then linked/cited in the article text; supplementary files are published under their own DOIs and bear their own citation details. Data deposited in trusted repositories and/or supplementary files and described in data papers; data papers may be submitted in text format or converted into manuscripts from Ecological Metadata Language (EML) metadata. Integrated narrative and data publishing realised by the Biodiversity Data Journal, where structured data are imported into the article text from tables or via web services and downloaded/distributed from the published article. Data published in structured, semanticaly enriched, full-text XMLs, so that several data elements can thereafter easily be harvested by machines. Linked Open Data (LOD) extracted from literature, converted into interoperable RDF triples in accordance with the OpenBiodiv-O ontology (Senderov et al. 2018) and stored in the OpenBiodiv Biodiversity Knowledge Graph. Data underlying research results are deposited in an external repository and/or published as supplementary file(s) to the article and then linked/cited in the article text; supplementary files are published under their own DOIs and bear their own citation details. Data deposited in trusted repositories and/or supplementary files and described in data papers; data papers may be submitted in text format or converted into manuscripts from Ecological Metadata Language (EML) metadata. Integrated narrative and data publishing realised by the Biodiversity Data Journal, where structured data are imported into the article text from tables or via web services and downloaded/distributed from the published article. Data published in structured, semanticaly enriched, full-text XMLs, so that several data elements can thereafter easily be harvested by machines. Linked Open Data (LOD) extracted from literature, converted into interoperable RDF triples in accordance with the OpenBiodiv-O ontology (Senderov et al. 2018) and stored in the OpenBiodiv Biodiversity Knowledge Graph. The above mentioned approaches are supported by a whole ecosystem of additional workflows and tools, for example: (1) pre-publication data auditing, involving both human and machine data quality checks (workflow 2); (2) web-service integration with data repositories and data centres, such as Global Biodiversity Information Facility (GBIF), Barcode of Life Data Systems (BOLD), Integrated Digitized Biocollections (iDigBio), Data Observation Network for Earth (DataONE), Long Term Ecological Research (LTER), PlutoF, Dryad, and others (workflows 1,2); (3) semantic markup of the article texts in the TaxPub format facilitating further extraction, distribution and re-use of sub-article elements and data (workflows 3,4); (4) server-to-server import of specimen data from GBIF, BOLD, iDigBio and PlutoR into manuscript text (workflow 3); (5) automated conversion of EML metadata into data paper manuscripts (workflow 2); (6) export of Darwin Core Archive and automated deposition in GBIF (workflow 3); (7) submission of individual images and supplementary data under own DOIs to the Biodiversity Literature Repository, BLR (workflows 1-3); (8) conversion of key data elements from TaxPub articles and taxonomic treatments extracted by Plazi into RDF handled by OpenBiodiv (workflow 5). These approaches represent different aspects of the prospective scholarly publishing of biodiversity data, which in a combination with text and data mining (TDM) technologies for legacy literature (PDF) developed by Plazi, lay the ground of an entire data publishing ecosystem for biodiversity, supplying FAIR (Findable, Accessible, Interoperable and Reusable data to several interoperable overarching infrastructures, such as GBIF, BLR, Plazi TreatmentBank, OpenBiodiv and various end users.

An Unsupervised Approach for Determining Link Specifications

2018 ◽  
Vol 13 (4) ◽  
pp. 104-123
Author(s):  
Khayra Bencherif ◽  
Mimoun Malki ◽  
Djamel Amar Bensaber
Keyword(s):  
Linked Data ◽  
Open Data ◽  
Real Data ◽  
Knowledge Bases ◽  
Structured Data ◽  
Data Sets ◽  
Novel Approach ◽  
Link Discovery ◽  
Unsupervised Approach

This article describes how the Linked Open Data Cloud project allows data providers to publish structured data on the web according to the Linked Data principles. In this context, several link discovery frameworks have been developed for connecting entities contained in knowledge bases. In order to achieve a high effectiveness for the link discovery task, a suitable link configuration is required to specify the similarity conditions. Unfortunately, such configurations are specified manually; which makes the link discovery task tedious and more difficult for the users. In this article, the authors address this drawback by proposing a novel approach for the automatic determination of link specifications. The proposed approach is based on a neural network model to combine a set of existing metrics into a compound one. The authors evaluate the effectiveness of the proposed approach in three experiments using real data sets from the LOD Cloud. In addition, the proposed approach is compared against link specifications approaches to show that it outperforms them in most experiments.

scholarly journals Technical Note: The Linked Paleo Data framework – a common tongue for paleoclimatology

2015 ◽  
Vol 11 (5) ◽  
pp. 4309-4327
Author(s):  
N. P. McKay ◽  
J. Emile-Geay
Keyword(s):  
Open Data ◽  
Technical Note ◽  
Structured Data ◽  
Data Standards ◽  
Knowledge Representations ◽  
Standard Data ◽  
Data Framework ◽  
Online Databases ◽  
Scientific Endeavor ◽  
Paleoclimate Data

Abstract. Paleoclimatology is a highly collaborative scientific endeavor, increasingly reliant on online databases for data sharing. Yet, there is currently no universal way to describe, store and share paleoclimate data: in other words, no standard. Data standards are often regarded by scientists as mere technicalities, though they underlie much scientific and technological innovation, as well as facilitating collaborations between research groups. In this article, we propose a preliminary data standard for paleoclimate data, general enough to accommodate all the proxy and measurement types encountered in a large international collaboration (PAGES2K). We also introduce a vehicle for such structured data (Linked Paleo Data, or LiPD), leveraging recent advances in knowledge representations (Linked Open Data). The LiPD framework enables quick querying and extraction, and we expect that it will facilitate the writing of open-source, community codes to access, analyze, model and visualize paleoclimate observations. We welcome community feedback on this standard, and encourage paleoclimatologists to experiment with the format for their own purposes.

The Researches on the Standardization of Petroleum Exploration and Development Structured Data

2012 ◽  
Vol 461 ◽  
pp. 749-752
Author(s):  
Cheng Fang Li
Keyword(s):  
Traffic Flow ◽  
Open Data ◽  
Structured Data ◽  
Petroleum Exploration ◽  
Data Dictionary ◽  
Oil Exploration ◽  
Data Traffic ◽  
Effective Organization ◽  
Oil Company ◽  
Exploration And Development

On account of the complicated management, complex business, intricate data, complex appliance of the process of the oil exploration and development, the condition that the disunity, imperfect of the data standard of the oil exploration and development, and the ineffective share of the data, the plan of standardized data structure has been put forward in refer to the international standard of POSC and PPDM. The main aim of this plan is to solve the problem of the disunity of the structured standard between different major and different oil company. The plan will follow the principle of the business drive and begin from the production traffic flow, the analysis of the data traffic flow and through the standardization of the name, meaning, type, dereferencing of the data to form an open data dictionary for oil exploration and development. The dictionary will be public by some effective organization in time, which can increase the sharing and authority of the structured data.

scholarly journals Data Journalism: From Social Science Techniques to Data Science Skills

Hipertext.net ◽  
2020 ◽  
pp. 41-54
Author(s):  
Adolfo Antón Bravo ◽  
Ana Serrano Tellería
Keyword(s):  
Social Science ◽  
Data Visualization ◽  
Data Science ◽  
Open Data ◽  
Structured Data ◽  
Scientific Methods ◽  
Data Literacy ◽  
Data Journalism ◽  
Source Of Information

In very few years in journalism, we have gone from looking at social science techniques, what was called precision journalism, to dealing with open data as a huge source of information that lead us to data journalism what connects with data science in the sense of using -again- scientific methods to extract knowledge and insights from structured data. This article offers an overview of that evolution and focuses on some prototypes that have emerged in this new journalistic ecosystem of data journalism, data visualization and data literacy.

scholarly journals Technical note: The Linked Paleo Data framework – a common tongue for paleoclimatology

2016 ◽  
Vol 12 (4) ◽  
pp. 1093-1100 ◽  
Author(s):  
Nicholas P. McKay ◽  
Julien Emile-Geay
Keyword(s):  
Open Data ◽  
Technical Note ◽  
Structured Data ◽  
Research Groups ◽  
Data Standards ◽  
Standard Data ◽  
Data Framework ◽  
Online Databases ◽  
Scientific Endeavor ◽  
Paleoclimate Data

Abstract. Paleoclimatology is a highly collaborative scientific endeavor, increasingly reliant on online databases for data sharing. Yet there is currently no universal way to describe, store and share paleoclimate data: in other words, no standard. Data standards are often regarded by scientists as mere technicalities, though they underlie much scientific and technological innovation, as well as facilitating collaborations between research groups. In this article, we propose a preliminary data standard for paleoclimate data, general enough to accommodate all the archive and measurement types encountered in a large international collaboration (PAGES 2k). We also introduce a vehicle for such structured data (Linked Paleo Data, or LiPD), leveraging recent advances in knowledge representation (Linked Open Data).The LiPD framework enables quick querying and extraction, and we expect that it will facilitate the writing of open-source community codes to access, analyze, model and visualize paleoclimate observations. We welcome community feedback on this standard, and encourage paleoclimatologists to experiment with the format for their own purposes.

scholarly journals Annotating a Low-Resource Language with LLOD Technology: Sumerian Morphology and Syntax

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 290 ◽  
Author(s):  
Christian Chiarcos ◽  
Ilya Khait ◽  
Émilie Pagé-Perron ◽  
Niko Schenk ◽  
Jayanth ◽  
...  
Keyword(s):  
Machine Translation ◽  
Open Data ◽  
Automated Analysis ◽  
Structured Data ◽  
Low Resource ◽  
Ur Iii ◽  
Language Technology ◽  
The Rich ◽  
Syntactic Annotation ◽  
Lexical Data

This paper describes work on the morphological and syntactic annotation of Sumerian cuneiform as a model for low resource languages in general. Cuneiform texts are invaluable sources for the study of history, languages, economy, and cultures of Ancient Mesopotamia and its surrounding regions. Assyriology, the discipline dedicated to their study, has vast research potential, but lacks the modern means for computational processing and analysis. Our project, Machine Translation and Automated Analysis of Cuneiform Languages, aims to fill this gap by bringing together corpus data, lexical data, linguistic annotations and object metadata. The project’s main goal is to build a pipeline for machine translation and annotation of Sumerian Ur III administrative texts. The rich and structured data is then to be made accessible in the form of (Linguistic) Linked Open Data (LLOD), which should open them to a larger research community. Our contribution is two-fold: in terms of language technology, our work represents the first attempt to develop an integrative infrastructure for the annotation of morphology and syntax on the basis of RDF technologies and LLOD resources. With respect to Assyriology, we work towards producing the first syntactically annotated corpus of Sumerian.

Bayes-ReCCE

2016 ◽  
Vol 12 (2) ◽  
pp. 25-52 ◽  
Author(s):  
Brian Walshe ◽  
Rob Brennan ◽  
Declan O'Sullivan
Keyword(s):  
Bayesian Inference ◽  
Gold Standard ◽  
Open Data ◽  
Knowledge Bases ◽  
Structured Data ◽  
Linked Open Data ◽  
Large Set ◽  
Informed Decisions ◽  
Standard Set ◽  
The Relationship

Linked Open Data consists of a large set of structured data knowledge bases which have been linked together, typically using equivalence statements. These equivalences usually take the form of owl:sameAs statements linking individuals, but links between classes are far less common. Often, the lack of linking between classes is because the relationships cannot be described as elementary one to one equivalences. Instead, complex correspondences referencing multiple entities in logical combinations are often necessary if we want to describe how the classes in one ontology are related to classes in a second ontology. In this paper the authors introduce a novel Bayesian Restriction Class Correspondence Estimation (Bayes-ReCCE) algorithm, an extensional approach to detecting complex correspondences between classes. Bayes-ReCCE operates by analysing features of matched individuals in the knowledge bases, and uses Bayesian inference to search for complex correspondences between the classes these individuals belong to. Bayes-ReCCE is designed to be capable of providing meaningful results even when only small amounts of matched instances are available. They demonstrate this capability empirically, showing that the complex correspondences generated by Bayes-ReCCE have a median F1 score of over 0.75 when compared against a gold standard set of complex correspondences between Linked Open Data knowledge bases covering the geographical and cinema domains. In addition, the authors discuss how metadata produced by Bayes-ReCCE can be included in the correspondences to encourage reuse by allowing users to make more informed decisions on the meaning of the relationship described in the correspondences.

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

Sign in / Sign up

Export Citation Format

Share Document