scholarly journals Linking Data and Descriptions on Moths Using the Wikimedia Ecosystem

2021 ◽  
Vol 5 ◽  
Author(s):  
Andra Waagmeester ◽  
Paul Braun ◽  
Manoj Karingamadathil ◽  
Jose Emilio Labra Gayo ◽  
Siobhan Leachman ◽  
...  
Keyword(s):  
New Zealand ◽  
Semantic Web ◽  
Data Model ◽  
Linked Data ◽  
Data Models ◽  
Data Reuse ◽  
Data Sources ◽  
Scientific Article ◽  
Global Biodiversity Information Facility ◽  
Data Repositories

Moths form a diverse group of species that are predominantly active at night. They are colourful, have an ecological role, but are less well described compared to their closest relatives, the butterflies. Much remains to be understood about moths, which is shown by the many issues within their taxonomy, including being a paraphyletic group and the inability to clearly distinguish them from butterflies (Fig. 1). We present the Wikimedia architecture as a hub of knowledge on moths. This ecosystem consists of 312 language editions of Wikipedia and sister projects such as Wikimedia commons (a multimedia repository), and Wikidata (a public knowledge graph). Through Wikidata, external data repositories can be integrated into this knowledge landscape on moths. Wikidata contains links to (open) data repositories on biodiversity like iNaturalist, Global Biodiversity Information Facility (GBIF) and the Biodiversity Heritage Library (BHL) which in return contain detailed content like species occurrence data, images or publications on moths. We present a workflow that integrates crowd-sourced information and images from iNaturalist, with content from GBIF and BHL into the different language editions of Wikipedia. The Wikipedia articles in turn feed information to other sources. Taxon pages on iNaturalist, for example, have an "About" tab, which is fed by the Wikipedia article describing the respective taxon, where the current language of the (iNaturalist) interface fetches the appropriate language version from Wikipedia. This is a nice example of data reuse, which is one of the pillars of FAIR (Findable, Accessible, Interoperable and Reusable) (Wilkinson et al. 2016). Wikidata provides the linked data hub in this flow of knowledge. Since Wikidata is available in RDF, it aligns well with the data model of the semantic web. This allows rapid integration with other linked data sources, and provides an intuitive portal for non-linked data to be integrated as linked data with this semantic web. rapid integration with other linked data sources, and provides an intuitive portal for non-linked data to be integrated as linked data with this semantic web. Wikidata is includes information on all sorts of things (e.g., people, species, locations, events). Which is why it can structure data in a multitude of ways, thus leading to 9000+ properties. Because all those different domains and communities use the same source for different things it is important to have good structure and documentation for a specific topic so we and others can interpret the data. We present a schema that describes data about moth taxa on Wikidata. Since 2019, Wikidata has an EntitySchema namespace that allows contributors to specify applicable linked-data schemas. The schemas are expressed using Shape Expressions (ShEx) (Thornton et al. 2019), which is a formal modelling language for RDF, one of the data formats used on the Semantic Web. Since Wikidata is also rendered as RDF, it is possible to use ShEx to describe data models and user expectations in Wikidata (Waagmeester et al. 2021). These schemas can then be used to verify if a subset of Wikidata conforms to an expected or described data model. Starting from a document that describes an expected schema on moths, we have developed an EntitySchema (E321) for moths in Wikidata. This schema provides unambiguous guidance for contributors who have data they are not sure how to model. For example, a user with data about a particular species of moth may be working from a scientific article that states that the species is only found in New Zealand, and may be unsure of how to model that fact as a statement in Wikidata. After consulting Schema E321, the user will find out about Property P183 “endemic_to” and then use that property to state that the species is endemic to New Zealand. As more contributors follow the data model expressed in schema E321, there will be structural consistency across items for moths in Wikidata. This reduces the risk of contributors using different combinations of properties and qualifiers to express the same meaning. If a contributor needs to express something that is not yet represented in Schema E321 they can extend the schema itself, as each schema can be edited. The multilingual affordances of the Wikidata platform allow users to edit in over 300 languages. In this way, contributors edit in their preferred language and see the structure of the data as well as the schemas in their language of choice. This broadens the range of people who can contribute to these data models and reduces the dominance of English. There are approximately 160K+ estimated moth species. This number is equal to the number of moths described in iNaturalist, while Wikidata contains 220K items on moths. As the biggest language edition, the English Wikipedia contains 65K moth articles; other language editions contain far fewer Wikipedia articles. The higher number of items on moths in Wikidata can be partly explained by Wikidata taxon synonyms being treated as distinct taxa. Wikidata, as a proxy of knowledge on moths, is instrumental in getting them better described in Wikipedia and other (FAIR) sources. While in return, curation in Wikidata happens by a large community. This approach to data modelling has the advantage of allowing multilingual collaboration and iterative extension and improvement over time.

Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web

2018 ◽  
Vol 14 (3) ◽  
pp. 134-166 ◽  
Author(s):  
Amit Singh ◽  
Aditi Sharan
Keyword(s):  
Fuzzy Logic ◽  
Semantic Web ◽  
Linked Data ◽  
State Of The Art ◽  
Fuzzy Programming ◽  
Data Sources ◽  
Fuzzy Approach ◽  
Entity Linking ◽  
Efficient Information ◽  
Domain Independent

This article describes how semantic web data sources follow linked data principles to facilitate efficient information retrieval and knowledge sharing. These data sources may provide complementary, overlapping or contradicting information. In order to integrate these data sources, the authors perform entity linking. Entity linking is an important task of identifying and linking entities across data sources that refer to the same real-world entities. In this work, they have proposed a genetic fuzzy approach to learn linkage rules for entity linking. This method is domain independent, automatic and scalable. Their approach uses fuzzy logic to adapt mutation and crossover rates of genetic programming to ensure guided convergence. The authors' experimental evaluation demonstrates that our approach is competitive and make significant improvements over state of the art methods.

scholarly journals What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

10.2196/17687 ◽  
2020 ◽  
Vol 4 (8) ◽  
pp. e17687
Author(s):  
Kristina K Gagalova ◽  
M Angelica Leon Elizalde ◽  
Elodie Portales-Casamar ◽  
Matthias Görges
Keyword(s):  
Data Processing ◽  
Clinical Data ◽  
Data Reuse ◽  
Data Sources ◽  
Primary Data ◽  
Data Repositories ◽  
Planning Stage ◽  
Common Features ◽  
Comparative Review ◽  
High Level

Background Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade. Objective The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation. Methods We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices. Results Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making). Conclusions IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

scholarly journals Sequential linked data: The state of affairs

Semantic Web ◽  
2021 ◽  
pp. 1-36
Author(s):  
Enrico Daga ◽  
Albert Meroño-Peñuela ◽  
Enrico Motta
Keyword(s):  
Semantic Web ◽  
Computer Science ◽  
Data Structures ◽  
Linked Data ◽  
Data Models ◽  
The State ◽  
Sequential Data ◽  
Linked List ◽  
Knowledge Graphs ◽  
State Of Affairs

Sequences are among the most important data structures in computer science. In the Semantic Web, however, little attention has been given to Sequential Linked Data. In previous work, we have discussed the data models that Knowledge Graphs commonly use for representing sequences and showed how these models have an impact on query performance and that this impact is invariant to triplestore implementations. However, the specific list operations that the management of Sequential Linked Data requires beyond the simple retrieval of an entire list or a range of its elements – e.g. to add or remove elements from a list –, and their impact in the various list data models, remain unclear. Covering this knowledge gap would be a significant step towards the realization of a Semantic Web list Application Programming Interface (API) that standardizes list manipulation and generalizes beyond specific data models. In order to address these challenges towards the realization of such an API, we build on our previous work in understanding the effects of various sequential data models for Knowledge Graphs, extending our benchmark and proposing a set of read-write Semantic Web list operations in SPARQL, with insert, update and delete support. To do so, we identify five classic list-based computer science sequential data structures (linked list, double linked list, stack, queue, and array), from which we derive nine atomic read-write operations for Semantic Web lists. We propose a SPARQL implementation of these operations with five typical RDF data models and compare their performance by executing them against six increasing dataset sizes and four different triplestores. In light of our results, we discuss the feasibility of our devised API and reflect on the state of affairs of Sequential Linked Data.

Temporal Semistructured Data Models and Data Warehouses

2011 ◽  
pp. 277-297 ◽  
Author(s):  
Carlo Combi ◽  
Barbara Oliboni
Keyword(s):  
Data Warehouse ◽  
Data Model ◽  
Heterogeneous Data ◽  
Data Models ◽  
Semistructured Data ◽  
Data Sources ◽  
Time Varying ◽  
Data Warehouses ◽  
Time Dimension ◽  
Heterogeneous Data Sources

This chapter describes a graph-based approach to represent information stored in a data warehouse, by means of a temporal semistructured data model. We consider issues related to the representation of semistructured data warehouses, and discuss the set of constraints needed to manage in a correct way the warehouse time, i.e. the time dimension considered storing data in the data warehouse itself. We use a temporal semistructured data model because a data warehouse can contain data coming from different and heterogeneous data sources. This means that data stored in a data warehouse are semistructured in nature, i.e. in different documents the same information can be represented in different ways, and moreover, the document schemata can be available or not. Moreover, information stored into a data warehouse is often time varying, thus as for semistructured data, also in the data warehouse context, it could be useful to consider time.

scholarly journals Towards the Representation of Etymological Data on the Semantic Web

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 304 ◽  
Author(s):  
Anas Khan
Keyword(s):  
Semantic Web ◽  
Data Model ◽  
Linked Data ◽  
New Model ◽  
Data Framework ◽  
Preceding Discussion ◽  
Rdf Data ◽  
Resource Data

In this article, we look at the potential for a wide-coverage modelling of etymological information as linked data using the Resource Data Framework (RDF) data model. We begin with a discussion of some of the most typical features of etymological data and the challenges that these might pose to an RDF-based modelling. We then propose a new vocabulary for representing etymological data, the Ontolex-lemon Etymological Extension (lemonETY), based on the ontolex-lemon model. Each of the main elements of our new model is motivated with reference to the preceding discussion.

scholarly journals Linked Data Driven Information Systems as an Enabler for Integrating Financial Data

2011 ◽  
pp. 239-270 ◽  
Author(s):  
Seán O’Riain ◽  
Andreas Harth ◽  
Edward Curry
Keyword(s):  
Semantic Web ◽  
Data Integration ◽  
Information Integration ◽  
Financial Services ◽  
Linked Data ◽  
Financial Data ◽  
Data Sources ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Multiple Data

With increased dependence on efficient use and inclusion of diverse corporate and Web based data sources for business information analysis, financial information providers will increasingly need agile information integration capabilities. Linked Data is a set of technologies and best practices that provide such a level of agility for information integration, access, and use. Current approaches struggle to cope with multiple data sources inclusion in near real-time, and have looked to Semantic Web technologies for assistance with infrastructure access, and dealing with multiple data formats and their vocabularies. This chapter discusses the challenges of financial data integration, provides the component architecture of Web enabled financial data integration and outlines the emergence of a financial ecosystem, based upon existing Web standards usage. Introductions to Semantic Web technologies are given, and the chapter supports this with insight and discussion gathered from multiple financial services use case implementations. Finally, best practice for integrating Web data based on the Linked Data principles and emergent areas are described.

scholarly journals What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions (Preprint)

2020 ◽  
Author(s):  
Kristina K Gagalova ◽  
M Angelica Leon Elizalde ◽  
Elodie Portales-Casamar ◽  
Matthias Görges
Keyword(s):  
Data Processing ◽  
Clinical Data ◽  
Data Reuse ◽  
Data Sources ◽  
Primary Data ◽  
Data Repositories ◽  
Planning Stage ◽  
Common Features ◽  
Comparative Review ◽  
High Level

BACKGROUND Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade. OBJECTIVE The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation. METHODS We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices. RESULTS Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making). CONCLUSIONS IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

scholarly journals SEMANTIC LINKING SPATIAL RDF DATA TO THE WEB DATA SOURCES

2018 ◽  
Vol XLII-4 ◽  
pp. 639-645 ◽  
Author(s):  
D. Ulutaş Karakol ◽  
G. Kara ◽  
C. Yılmaz ◽  
Ç. Cömert
Keyword(s):  
Semantic Web ◽  
Spatial Data ◽  
Linked Data ◽  
Relational Databases ◽  
Data Sources ◽  
Data Conversion ◽  
Web Data ◽  
Link Discovery ◽  
Rdf Data ◽  
The Web

<p><strong>Abstract.</strong> Large amounts of spatial data are hold in relational databases. Spatial data in the relational databases must be converted to RDF for semantic web applications. Spatial data is an important key factor for creating spatial RDF data. Linked Data is the most preferred way by users to publish and share data in the relational databases on the Web. In order to define the semantics of the data, links are provided to vocabularies (ontologies or other external web resources) that are common conceptualizations for a domain. Linking data of resource vocabulary with globally published concepts of domain resources combines different data sources and datasets, makes data more understandable, discoverable and usable, improves data interoperability and integration, provides automatic reasoning and prevents data duplication. The need to convert relational data to RDF is coming in sight due to semantic expressiveness of Semantic Web Technologies. One of the important key factors of Semantic Web is ontologies. Ontology means “explicit specification of a conceptualization”. The semantics of spatial data relies on ontologies. Linking of spatial data from relational databases to the web data sources is not an easy task for sharing machine-readable interlinked data on the Web. Tim Berners-Lee, the inventor of the World Wide Web and the advocate of Semantic Web and Linked Data, layed down the Linked Data design principles. Based on these rules, firstly, spatial data in the relational databases must be converted to RDF with the use of supporting tools. Secondly, spatial RDF data must be linked to upper level-domain ontologies and related web data sources. Thirdly, external data sources (ontologies and web data sources) must be determined and spatial RDF data must be linked related data sources. Finally, spatial linked data must be published on the web. The main contribution of this study is to determine requirements for finding RDF links and put forward the deficiencies for creating or publishing linked spatial data. To achieve this objective, this study researches existing approaches, conversion tools and web data sources for relational data conversion to the spatial RDF. In this paper, we have investigated current state of spatial RDF data, standards, open source platforms (particularly D2RQ, Geometry2RDF, TripleGeo, GeoTriples, Ontop, etc.) and the Web Data Sources. Moreover, the process of spatial data conversion to the RDF and how to link it to the web data sources is described. The implementation of linking spatial RDF data to the web data sources is demonstrated with an example use case. Road data has been linked to the one of the related popular web data sources, DBPedia. SILK, a tool for discovering relationships between data items within different Linked Data sources, is used as a link discovery framework. Also, we evaluated other link discovery tools e.g. LIMES, Silk and results are compared to carry out matching/linking task. As a result, linked road data is shared and represented as an information resource on the web and enriched with definitions of related different resources. By this way, road datasets are also linked by the related classes, individuals, spatial relations and properties they cover such as, construction date, road length, coordinates, etc.</p>

Library Linked Data Models: Library Data in the Semantic Web

2019 ◽  
Vol 57 (5) ◽  
pp. 261-277
Author(s):  
Hyoungjoo Park ◽  
Margaret Kipp
Keyword(s):  
Semantic Web ◽  
Linked Data ◽  
Data Models

NONLINEAR PANEL DATA MODELS WITH DISTRIBUTION-FREE CORRELATED RANDOM EFFECTS

2021 ◽  
pp. 1-25
Author(s):  
Yu-Chin Hsu ◽  
Ji-Liang Shiu
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Conditional Distribution ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Data Models ◽  
Panel Data Model ◽  
Panel Data Models ◽  
Likelihood Functions ◽  
Correlated Random Effects

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.

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