Linked Open Dataset Research Articles

OpenBiodiv for Users: Applications and Approaches to Explore a Biodiversity Knowledge Graph

OpenBiodiv is a biodiversity database—knowledge graph based on Resource Description Framework (RDF)—that contains information extracted from the scientific literature. It provides access to an ecosystem of tools and services, including a Linked Open Dataset, an ontology (OpenBiodiv-O) and а website (Dimitrova et al. 2021). Using the available data, OpenBiodiv discovers links between various biodiversity data types (e.g., taxon names, treatments, specimens, sequences, people and institutions), to answer a user’s questions about specific taxa, scientific articles, materials examined and others. The full-text XML content is converted into Linked Open Data from journals on the ARPHA Publishing Platform and treatments extracted by Plazi’s TreatmentBank (stored in the Biodiversity Literature Repository at Zenodo). The database is updated and indexed daily using a workflow based on the Apache Kafka event-streaming platform. The workflow was developed during the European Union-funded Biodiversity Community Integrated Knowledge Library (BiCIKL) project (Penev et al. 2022b). By 1 of August 2023, the graph consisted of 24,939 articles; 167,471 treatments; 130,359 authors; 736,809 taxon names; 129,257 sequences; 1,390 institutions and collections, 117,854 figures; 18,585 tables, and 90,008 materials examined sections. Each semantic statement (e.g., authors, articles, treatments, taxonomic names, localities) has its own globally unique, persistent and resolvable identifier (GUPRI). There are four ways a user can explore the data on OpenBiodiv: General search The search engine is accessible from the OpenBiodiv homepage. The user needs to type in a key term, (e.g., a taxonomic name, authority or an article title), and the system retrieves information about it. Errors caused by misspellings are avoided due to the Elasticsearch index. It can also determine the semantic type of the searched entity. Application Programing Interface (API) OpenBiodiv can be used through a RESTful API for programmatic access. The documentation of the API is described on Swagger. The API construction and functionalities follow the recommendations elaborated by the Technical Research Infrastructures forum of the BiCIKL project (Addink et al. 2023). User applications based on a query algorithm This function can be applied for any data class. The method uses the relationships between an element type (e.g., taxon name) and the type of the section, where it can be found. An application example is Literature exploration, designed to answer the question: Give me information about X mentioned within article section type Y. The results show the number of mentions of the entity (e.g., taxon name) in the section(s) of interest (e.g., Title, Abstract, Treatment). A click navigates the user to the place in the article that mentions the item (Fig. 1). SPARQL queries in a thematic context OpenBiodiv provides a SPARQL endpoint through the Ontotext GraphDB solution*1. Several sample SPARQL queries*2 are also available on the OpenBiodiv website.

Machine-Generated Colon Class Numbers: Automatic Classification of Indian Literary Works in the Wikidata Environment

Wikidata is emerging rapidly as an omnipresent information space. The main objective of Wikidata is to create a structure to represent all human knowledge in many different languages. It is a free, Linked Open Dataset (LOD) that can interact with humans and machines and act as a bridge between them. Wikidata serves as a central repository for structured data of all other Wiki-based applications (like Wikipedia, Wikibooks, and Wikinews), integrating them into a coherent unit. It is a multilingual, collaboratively editable central repository of structured and linked open data. Interestingly, Wikidata is based on a facet-based linked open data approach like, the theory of facet analysis proposed by Ranganathan. The literary works available in Wikidata are linked with the author of the work, the language of the work, and other important attributes like genre, publication year, and so on. The author of the work has properties like, date of birth, occupation, native language, writing language, and so on. These faceted linked data elements of literary works can be joined together based on the rule base of the Colon Classification (6th edition). The synthesis of the Colon class number is therefore amenable to automation in the Wikidata environment by using a set of suitable JavaScripts (these JavaScripts are called gadgets in the Wikidata environment). One such gadget is CCLitBox, developed by Stefano Bargioni (a librarian) and Carlo Bianchini (a teacher in LIS). This research study explores the applications of CCLitBox in generating CC 6th edition-based class numbers for Indian literary works.

Optimizing storage of RDF archives using bidirectional delta chains

Linked Open Datasets on the Web that are published as RDF can evolve over time. There is a need to be able to store such evolving RDF datasets, and query across their versions. Different storage strategies are available for managing such versioned datasets, each being efficient for specific types of versioned queries. In recent work, a hybrid storage strategy has been introduced that combines these different strategies to lead to more efficient query execution for all versioned query types at the cost of increased ingestion time. While this trade-off is beneficial in the context of Web querying, it suffers from exponential ingestion times in terms of the number of versions, which becomes problematic for RDF datasets with many versions. As such, there is a need for an improved storage strategy that scales better in terms of ingestion time for many versions. We have designed, implemented, and evaluated a change to the hybrid storage strategy where we make use of a bidirectional delta chain instead of the default unidirectional delta chain. In this article, we introduce a concrete architecture for this change, together with accompanying ingestion and querying algorithms. Experimental results from our implementation show that the ingestion time is significantly reduced. As an additional benefit, this change also leads to lower total storage size and even improved query execution performance in some cases. This work shows that modifying the structure of delta chains within the hybrid storage strategy can be highly beneficial for RDF archives. In future work, other modifications to this delta chain structure deserve to be investigated, to further improve the scalability of ingestion and querying of datasets with many versions.

OpenBiodiv-O Ontology: Bridging the Gap Between Biodiversity Data and Biodiversity Publishing

Communication of research findings is the last and arguably the most influential step of the scientific process. This is especially true for biodiversity science, in which new species descriptions and introduction of new taxonomic names happens through publication, as governed by the International Codes. Despite the strict rules for naming new taxa and revising existing taxonomic nomenclatures within scholarly literature, there is no system for keeping track of these changes and information often remains locked within the text of thousands of scattered journal articles. This talk presents OpenBiodiv-O, the first ontology which conceptually models the biodiversity publishing domain and through its application in the semantic graph database OpenBiodiv contributes to knowledge management of this domain. In combination with already existing ontologies for biodiversity and publishing (e.g. DarwinCore-based ontologies, SPAR ontologies), resource types introduced by OpenBiodiv-O help to create a link between these two domains. The ontology models the general structure of a research article, including sections specific to taxonomic articles, such as the treatment section, as well as other conceptual entities from taxonomy, like scientific names and taxonomic concepts. Thus, OpenBiodiv-O links scientific names to the corresponding article section in which they are mentioned via the class Taxonomic Name Usage and helps to discover hidden relationships between names. In addition, OpenBiodiv-O models the article metadata, such as the author names, affiliations and unique identifiers. The orcid class from the recently introduced Datacite ontology within OpenBiodiv-O models the ORCID of authors and will enable the future disambiguation of authors and linking with other platforms using ORCID. OpenBiodiv-O has been applied to the biodiversity knowledge graph OpenBiodiv, which is based on a Linked Open Dataset, created from Pensoft's journal articles and Plazi's treatments. Publishing of semantically enhanced scholarly literature as XML enables the conversion of semi-structured narrative into connected Resource Description Framework (RDF) statements. The ontology serves as a skeleton for the transformation of more than 729 million statements into a Linked Open Dataset. Reusing of existing ontologies within OpenBiodiv-O helps to establish a link between OpenBiodiv-O and other ontologies and facilitates federated querying between OpenBiodiv and other knowledge graphs. The application of OpenBiodiv-O towards a working solution for the biodiversity publishing domain demonstrates the potential of ontology modelling for data organisation and management.

OpenBiodiv: A Knowledge Graph for Literature-Extracted Linked Open Data in Biodiversity Science

Hundreds of years of biodiversity research have resulted in the accumulation of a substantial pool of communal knowledge; however, most of it is stored in silos isolated from each other, such as published articles or monographs. The need for a system to store and manage collective biodiversity knowledge in a community-agreed and interoperable open format has evolved into the concept of the Open Biodiversity Knowledge Management System (OBKMS). This paper presents OpenBiodiv: An OBKMS that utilizes semantic publishing workflows, text and data mining, common standards, ontology modelling and graph database technologies to establish a robust infrastructure for managing biodiversity knowledge. It is presented as a Linked Open Dataset generated from scientific literature. OpenBiodiv encompasses data extracted from more than 5000 scholarly articles published by Pensoft and many more taxonomic treatments extracted by Plazi from journals of other publishers. The data from both sources are converted to Resource Description Framework (RDF) and integrated in a graph database using the OpenBiodiv-O ontology and an RDF version of the Global Biodiversity Information Facility (GBIF) taxonomic backbone. Through the application of semantic technologies, the project showcases the value of open publishing of Findable, Accessible, Interoperable, Reusable (FAIR) data towards the establishment of open science practices in the biodiversity domain.

Smart Information Retrieval: Domain Knowledge Centric Optimization Approach

In the age of the Internet of Things, online data have witnessed a significant growth in terms of volume and diversity, and research into information retrieval has become one of the important research themes in the Internet-oriented data science research. This paper introduces a novel domain knowledge centric methodology aimed at improving the accuracy of using machine learning methods for relation extraction from text data, which is critical to the accuracy and efficiency of information retrieval-based applications, including recommender systems and sentiment analysis. The proposed methodology makes a significant contribution to the processes of domain knowledge-based relation extraction including interrogating Linked Open Datasets to generate the relation classification training data, addressing the imbalanced classification in the training datasets, determining the probability threshold of the best learning algorithm, and establishing the optimum parameters for genetic algorithms, which were utilized to optimize the feature selection for the learning algorithms. The experimental evaluation of the proposed methodology reveals that the adopted machine-learning algorithms exhibit higher precision and recall in relation extraction in the reduced feature space optimized by our implementation. The considered machine learning includes support vector machine, perceptron algorithm uneven margin, and K-nearest neighbors. The outcome is verified by comparing against the random mutation hill-climbing optimization algorithm using Wilcoxon signed-rank statistical analysis.

Triple storage for random-access versioned querying of RDF archives

When publishing Linked Open Datasets on the Web, most attention is typically directed to their latest version. Nevertheless, useful information is present in or between previous versions. In order to exploit this historical information in dataset analysis, we can maintain history in RDF archives. Existing approaches either require much storage space, or they expose an insufficiently expressive or efficient interface with respect to querying demands. In this article, we introduce an RDF archive indexing technique that is able to store datasets with a low storage overhead, by compressing consecutive versions and adding metadata for reducing lookup times. We introduce algorithmsbased on this technique for efficiently evaluating queries at a certain version, between any two versions, and for versions. Using the BEAR RDF archiving benchmark, we evaluate our implementation, called OSTRICH. Results show that OSTRICH introduces a new tradeoff regarding storage space, ingestion time, and querying efficiency. By processing and storing more metadata during ingestion time, it significantly lowers the average lookup time for versioning queries. OSTRICH performs better for many smaller dataset versions than for few larger dataset versions. Furthermore, it enables efficient offsets in query result streams, which facilitates random access in results. Our storage technique reduces query evaluation time for versioned queries through a preprocessing step during ingestion, which only in some cases increases storage space when compared to other approaches. This allows data owners to store and query multiple versions of their dataset efficiently, lowering the barrier to historical dataset publication and analysis.

OpenBiodiv-O: ontology of the OpenBiodiv knowledge management system

BackgroundThe biodiversity domain, and in particular biological taxonomy, is moving in the direction of semantization of its research outputs. The present work introduces OpenBiodiv-O, the ontology that serves as the basis of the OpenBiodiv Knowledge Management System. Our intent is to provide an ontology that fills the gaps between ontologies for biodiversity resources, such as DarwinCore-based ontologies, and semantic publishing ontologies, such as the SPAR Ontologies. We bridge this gap by providing an ontology focusing on biological taxonomy.ResultsOpenBiodiv-O introduces classes, properties, and axioms in the domains of scholarly biodiversity publishing and biological taxonomy and aligns them with several important domain ontologies (FaBiO, DoCO, DwC, Darwin-SW, NOMEN, ENVO). By doing so, it bridges the ontological gap across scholarly biodiversity publishing and biological taxonomy and allows for the creation of a Linked Open Dataset (LOD) of biodiversity information (a biodiversity knowledge graph) and enables the creation of the OpenBiodiv Knowledge Management System.A key feature of the ontology is that it is an ontology of the scientific process of biological taxonomy and not of any particular state of knowledge. This feature allows it to express a multiplicity of scientific opinions. The resulting OpenBiodiv knowledge system may gain a high level of trust in the scientific community as it does not force a scientific opinion on its users (e.g. practicing taxonomists, library researchers, etc.), but rather provides the tools for experts to encode different views as science progresses.ConclusionsOpenBiodiv-O provides a conceptual model of the structure of a biodiversity publication and the development of related taxonomic concepts. It also serves as the basis for the OpenBiodiv Knowledge Management System.

Distant Reading Through Ontologies: The Case Study of Catania’s Benedictines Monastery

In this contribution, we developed a Linked Open Dataset, more specifically an OWL ontology, about the history of the renovation of the Catania’s Benedictines Monastery by the architect Giancarlo De Carlo. We considered a wide subset of public and private documents collected from 1977 to 2006 during the process of restoration and adaptation of the monastery to a campus for the University of Catania. The task of modelling and population of the ontology have been carried out from the analysis of documents stored in the “Archivio del Museo della Fabbrica”, in the new archive of professor Giuseppe Giarrizzo, in the private collection of Antonino Leonardi, and from the conceptual map of the locations of the monastery.

The debates of the European Parliament as Linked Open Data

The European Parliament represents the citizens of the member states of the European Union (EU). The accounts of its meetings and related documents are open data, promoting transparency and accountability, and are used as source data by researchers. However, the official portal of these documents provides limited search facilities. This paper presents LinkedEP, a Linked Open Data translation of the verbatim reports of the plenary meetings of the European Parliament. These data are integrated with a database of political affiliations of the Members of Parliament, and enriched with detected topics from the EU's topic hierarchy and links to four other Linked Open Datasets. The results of this work are available through a SPARQL endpoint and a user interface with extensive browse and search facilities. It is now possible to combine in one query the time and topic of the debate, the spoken words - in any available translation - and information about the speaker uttering these, such as affiliations to countries, parties and committees. This paper discusses the design and creation of the vocabulary, data and links, as well as known use of the data.

A linked open data approach for geolinguistics applications

Geolinguistic systems explore the relationship between language and cultural adaptation and change and they can be used as instructional tools, presenting complex data and relationships in a way accessible to all educational levels. However, the heterogeneity of geolinguistic projects has been recognised as a key problem limiting the reusability of linguistic tools and data collections. We propose an approach based on LOD, which moves the focus from the systems handling the data to the data themselves with the main goal of increasing the level of interoperability of geolinguistic applications and the reuse of the data. We defined an extensible ontology for geolinguistic resources based on the common ground defined by current European linguistic projects. We provide a Geolinguistic Linked Open Dataset based on the data case study of a linguistic project named ASIt. Finally, we show a geolinguistic application, which exploits this dataset for dynamically generating linguistic maps.

Amsterdam Museum Linked Open Data

In this document we describe the Amsterdam Museum Linked Open Data set. The dataset is a five-star Linked Data representation and comprises the entire collection of the Amsterdam Museum consisting of more than 70,000 object descriptions. Furthermore, the institution's thesaurus and person authority files used in the object metadata are included in the Linked Data set. The data is mapped to the Europeana Data Model, utilizing Dublin Core, SKOS, RDA-group2 elements and the OAI-ORE model to represent the museum data. Vocabulary concepts are mapped to GeoNames and DBpedia. The two main contributions of this dataset are the inclusion of internal vocabularies and the fact that the complexity of the original dataset is retained.