SPARQL Query Language Research Articles

Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation

BackgroundElectronic Laboratory Notebooks (ELNs) are used to document experiments and investigations in the wet-lab. Protocols in ELNs contain a detailed description of the conducted steps including the necessary information to understand the procedure and the raised research data as well as to reproduce the research investigation. The purpose of this study is to investigate whether such ELN protocols can be used to create semantic documentation of the provenance of research data by the use of ontologies and linked data methodologies.MethodsBased on an ELN protocol of a biomedical wet-lab experiment, a retrospective provenance model of the raised research data describing the details of the experiment in a machine-interpretable way is manually engineered. Furthermore, an automated approach for knowledge acquisition from ELN protocols is derived from these results. This structure-based approach exploits the structure in the experiment’s description such as headings, tables, and links, to translate the ELN protocol into a semantic knowledge representation. To satisfy the Findable, Accessible, Interoperable, and Reuseable (FAIR) guiding principles, a ready-to-publish bundle is created that contains the research data together with their semantic documentation.ResultsWhile the manual modelling efforts serve as proof of concept by employing one protocol, the automated structure-based approach demonstrates the potential generalisation with seven ELN protocols. For each of those protocols, a ready-to-publish bundle is created and, by employing the SPARQL query language, it is illustrated that questions about the processes and the obtained research data can be answered.ConclusionsThe semantic documentation of research data obtained from the ELN protocols allows for the representation of the retrospective provenance of research data in a machine-interpretable way. Research Object Crate (RO-Crate) bundles including these models enable researchers to easily share the research data including the corresponding documentation, but also to search and relate the experiment to each other.

A survey of RDF stores & SPARQL engines for querying knowledge graphs

RDF has seen increased adoption in recent years, prompting the standardization of the SPARQL query language for RDF, and the development of local and distributed engines for processing SPARQL queries. This survey paper provides a comprehensive review of techniques and systems for querying RDF knowledge graphs. While other reviews on this topic tend to focus on the distributed setting, the main focus of the work is on providing a comprehensive survey of state-of-the-art storage, indexing and query processing techniques for efficiently evaluating SPARQL queries in a local setting (on one machine). To keep the survey self-contained, we also provide a short discussion on graph partitioning techniques used in the distributed setting. We conclude by discussing contemporary research challenges for further improving SPARQL query engines. An extended version also provides a survey of over one hundred SPARQL query engines and the techniques they use, along with twelve benchmarks and their features.

Synospecies, an application to reflect changes in taxonomic names based on a triple store based on taxonomic data liberated from publication

Taxonomic treatments, sections of publications documenting the features or distribution of a related group of organisms (called a “taxon”, plural “taxa”) in ways adhering to highly formalized conventions, and published in scientific journals, shape our understanding of global biodiversity (Catapano 2019). Treatments are the building blocks of the evolving scientific consensus on taxonomic entities. The semantics of these treatments and their relationships are highly structured: taxa are introduced, merged, made obsolete, split, renamed, associated with specimens and so on. Plazi makes this content available in machine-readable form using Resource Description Framework (RDF) . RDF is the standard model for Linked Data and the Semantic Web. RDF can be exchanged in different formats (aka concrete syntaxes) such as RDF/XML or Turtle. The data model describes graph structures and relies on Internationalized Resource Identifiers (IRIs) , ontologies such as Darwin Core basic vocabulary are used to assign meaning to the identifiers. For Synospecies, we unite all treatments into one large knowledge graph, modelling taxonomic knowledge and its evolution with complete references to quotable treatments. However, this knowledge graph expresses much more than any individual treatment could convey because every referenced entity is linked to every other relevant treatment. On synospecies.plazi.org, we provide a user-friendly interface to find the names and treatments related to a taxon. An advanced mode allows execution of queries using the SPARQL query language.

REWOrD: Semantic Relatedness in the Web of Data

This paper presents REWOrD, an approach to compute semantic relatedness between entities in the Web of Data representing real word concepts. REWOrD exploits the graph nature of RDF data and the SPARQL query language to access this data. Through simple queries, REWOrD constructs weighted vectors keeping the informativeness of RDF predicates used to make statements about the entities being compared. The most informative path is also considered to further refine informativeness. Relatedness is then computed by the cosine of the weighted vectors. Differently from previous approaches based on Wikipedia, REWOrD does not require any prepro- cessing or custom data transformation. Indeed, it can lever- age whatever RDF knowledge base as a source of background knowledge. We evaluated REWOrD in different settings by using a new dataset of real word entities and investigate its flexibility. As compared to related work on classical datasets, REWOrD obtains comparable results while, on one side, it avoids the burden of preprocessing and data transformation and, on the other side, it provides more flexibility and applicability in a broad range of domains.

A National, Semantic-Driven, Three-Pillar Strategy to Enable Health Data Secondary Usage Interoperability for Research Within the Swiss Personalized Health Network: Methodological Study.

BackgroundInteroperability is a well-known challenge in medical informatics. Current trends in interoperability have moved from a data model technocentric approach to sustainable semantics, formal descriptive languages, and processes. Despite many initiatives and investments for decades, the interoperability challenge remains crucial. The need for data sharing for most purposes ranging from patient care to secondary uses, such as public health, research, and quality assessment, faces unmet problems.ObjectiveThis work was performed in the context of a large Swiss Federal initiative aiming at building a national infrastructure for reusing consented data acquired in the health care and research system to enable research in the field of personalized medicine in Switzerland. The initiative is the Swiss Personalized Health Network (SPHN). This initiative is providing funding to foster use and exchange of health-related data for research. As part of the initiative, a national strategy to enable a semantically interoperable clinical data landscape was developed and implemented.MethodsA deep analysis of various approaches to address interoperability was performed at the start, including large frameworks in health care, such as Health Level Seven (HL7) and Integrating Healthcare Enterprise (IHE), and in several domains, such as regulatory agencies (eg, Clinical Data Interchange Standards Consortium [CDISC]) and research communities (eg, Observational Medical Outcome Partnership [OMOP]), to identify bottlenecks and assess sustainability. Based on this research, a strategy composed of three pillars was designed. It has strong multidimensional semantics, descriptive formal language for exchanges, and as many data models as needed to comply with the needs of various communities.ResultsThis strategy has been implemented stepwise in Switzerland since the middle of 2019 and has been adopted by all university hospitals and high research organizations. The initiative is coordinated by a central organization, the SPHN Data Coordination Center of the SIB Swiss Institute of Bioinformatics. The semantics is mapped by domain experts on various existing standards, such as Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), Logical Observation Identifiers Names and Codes (LOINC), and International Classification of Diseases (ICD). The resource description framework (RDF) is used for storing and transporting data, and to integrate information from different sources and standards. Data transformers based on SPARQL query language are implemented to convert RDF representations to the numerous data models required by the research community or bridge with other systems, such as electronic case report forms.ConclusionsThe SPHN strategy successfully implemented existing standards in a pragmatic and applicable way. It did not try to build any new standards but used existing ones in a nondogmatic way. It has now been funded for another 4 years, bringing the Swiss landscape into a new dimension to support research in the field of personalized medicine and large interoperable clinical data.

DeepGOWeb: fast and accurate protein function prediction on the (Semantic) Web.

Understanding the functions of proteins is crucial to understand biological processes on a molecular level. Many more protein sequences are available than can be investigated experimentally. DeepGOPlus is a protein function prediction method based on deep learning and sequence similarity. DeepGOWeb makes the prediction model available through a website, an API, and through the SPARQL query language for interoperability with databases that rely on Semantic Web technologies. DeepGOWeb provides accurate and fast predictions and ensures that predicted functions are consistent with the Gene Ontology; it can provide predictions for any protein and any function in Gene Ontology. DeepGOWeb is freely available at https://deepgo.cbrc.kaust.edu.sa/.

IDSM ChemWebRDF: SPARQLing small-molecule datasets

The Resource Description Framework (RDF), together with well-defined ontologies, significantly increases data interoperability and usability. The SPARQL query language was introduced to retrieve requested RDF data and to explore links between them. Among other useful features, SPARQL supports federated queries that combine multiple independent data source endpoints. This allows users to obtain insights that are not possible using only a single data source. Owing to all of these useful features, many biological and chemical databases present their data in RDF, and support SPARQL querying. In our project, we primary focused on PubChem, ChEMBL and ChEBI small-molecule datasets. These datasets are already being exported to RDF by their creators. However, none of them has an official and currently supported SPARQL endpoint. This omission makes it difficult to construct complex or federated queries that could access all of the datasets, thus underutilising the main advantage of the availability of RDF data. Our goal is to address this gap by integrating the datasets into one database called the Integrated Database of Small Molecules (IDSM) that will be accessible through a SPARQL endpoint. Beyond that, we will also focus on increasing mutual interoperability of the datasets. To realise the endpoint, we decided to implement an in-house developed SPARQL engine based on the PostgreSQL relational database for data storage. In our approach, data are stored in the traditional relational form, and the SPARQL engine translates incoming SPARQL queries into equivalent SQL queries. An important feature of the engine is that it optimises the resulting SQL queries. Together with optimisations performed by PostgreSQL, this allows efficient evaluations of SPARQL queries. The endpoint provides not only querying in the dataset, but also the compound substructure and similarity search supported by our Sachem project. Although the endpoint is accessible from an internet browser, it is mainly intended to be used for programmatic access by other services, for example as a part of federated queries. For regular users, we offer a rich web application called ChemWebRDF using the endpoint. The application is publicly available at https://idsm.elixir-czech.cz/chemweb/.

An Effective Discrete Artificial Bee Colony Based SPARQL Query Path Optimization by Reordering Triples

Semantic Web has emerged to make web content machine-readable, and with the rapid increase in the number of web pages, its importance has increased. Resource description framework (RDF) is a special data graph format where Semantic Web data are stored and it can be queried by SPARQL query language. The challenge is to find the optimal query order that results in the shortest period of time. In this paper, the discrete Artificial Bee Colony (dABCSPARQL) algorithm is proposed, based on a novel heuristic approach, namely reordering SPARQL queries. The processing time of queries with different shapes and sizes is minimized using the dABCSPARQL algorithm. The performance of the proposed method is evaluated on chain, star, cyclic, and chain-star queries of different sizes from the Lehigh University Benchmark (LUBM) dataset. The results obtained by the proposed method are compared with those of ARQ (a SPARQL processor for Jena) query engine, the Ant System, the Elitist Ant System, and MAX-MIN Ant System algorithms. The experiments demonstrate that the proposed method significantly reduces the processing time, and in most queries, the reduction rate is higher compared with other optimization methods.

A Hybrid Approach Combining R*-Tree and k-d Trees to Improve Linked Open Data Query Performance

Semantic Web has recently gained traction with the use of Linked Open Data (LOD) on the Web. Although numerous state-of-the-art methodologies, standards, and technologies are applicable to the LOD cloud, many issues persist. Because the LOD cloud is based on graph-based resource description framework (RDF) triples and the SPARQL query language, we cannot directly adopt traditional techniques employed for database management systems or distributed computing systems. This paper addresses how the LOD cloud can be efficiently organized, retrieved, and evaluated. We propose a novel hybrid approach that combines the index and live exploration approaches for improved LOD join query performance. Using a two-step index structure combining a disk-based 3D R*-tree with the extended multidimensional histogram and flash memory-based k-d trees, we can efficiently discover interlinked data distributed across multiple resources. Because this method rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. We also propose a hot-cold segment identification algorithm to identify regions of high interest. The proposed method is compared with existing popular methods on real RDF datasets. Results indicate that our method outperforms the existing methods because it can quickly obtain target results by reducing unnecessary data scanning and reduce the amount of main memory required to load filtering results.

Neural machine translating from natural language to SPARQL

SPARQL is a highly powerful query language for an ever-growing number of resources and knowledge graphs represented in the Resource Description Framework (RDF) data format. Using it requires a certain familiarity with the entities in the domain to be queried as well as expertise in the language’s syntax and semantics, none of which average human web users can be assumed to possess. To overcome this limitation, automatically translating natural language questions to SPARQL queries has been a vibrant field of research. However, to this date, the vast success of deep learning methods has not yet been fully propagated to this research problem. This paper contributes to filling this gap by evaluating the utilization of eight different Neural Machine Translation (NMT) models for the task of translating from natural language to the structured query language SPARQL. While highlighting the importance of high-quantity and high-quality datasets, the results show a dominance of a Convolutional Neural Network (CNN)-based architecture with a Bilingual Evaluation Understudy (BLEU) score of up to 98 and accuracy of up to 94%.

Neural Machine Translation for Semantic-Driven Q&A Systems in the Factory Planning

Shorter lifecycles, increasing product variance and the integration of new products and technologies into existing factories lead to a high complexity in today’s factory planning. In order to master this complexity, many companies attempt to improve their processes by using digitalization tools. This generates enormous amounts of data, which are currently only partially managed centrally in the company. In order to simplify the associated difficulties regarding the access to information, semantic technologies for the generation and application of knowledge graphs (KG) are currently being investigated intensively by research and industry. To retrieve information - stored in the KG - query languages such as SPARQL are used which require knowledge regarding the structure of the KG as well as a profound knowledge of query languages. To also enable non-expert users to retrieve the stored information in the KG, an intuitive user interface for question answering (Q&A) is needed.In this context, we propose a translation model using artificial neural networks (ANN), which translates questions in german into the query language SPARQL. Due to a lack of suitable datasets, we first developed a method to automatically generate synthetic data sets for training the translation model based on existing ontologies. Based on similar approaches from research, we develop an ANN architecture for the translation model. The method for data generation and the ANN were tested and validated at a german car manufacturer using a knowledge graph. It was shown that the developed architecture is particularly suitable for our field of application.

Integration of Open Educational Resources Using Semantic Platform

Universities around the world have been taking advantage of open educational resources (OERs) for publishing educational material. The teachers and students can access, the published material for teaching, learning and research through institutional repositories. This article proposes the design, construction and validation of the semantic platform (MIIDAS), considering two case studies of higher-level educational institutions (civil and military schools). The MIIDAS Platform integrates educational resources in institutional repositories related to the areas of Computing and Electronics engineering through the incorporation of Semantic Web technologies. The descriptions of resources consider metadata of the OAI-PMH protocol and also add educational metadata. The platform uses RDF resource descriptions, ontologies and the SPARQL query language to improve resource retrieval, in addition to proposing guidelines for the management of educational resources. The results of validation of the platform are presented through the development of evaluation instruments, which suggest possible ways to improve the platform. The article provides the basis for developing platforms that help in spreading open educational resources and allow the construction of semantically enrich data sets.

An Algebraic Graph Transformation Approach for RDF and SPARQL

We consider the recommendations of the World Wide Web Consortium (W3C) about RDF framework and its associated query language SPARQL. We propose a new formal framework based on category theory which provides clear and concise formal definitions of the main basic features of RDF and SPARQL. We define RDF graphs as well as SPARQL basic graph patterns as objects of some nested categories. This allows one to clarify, in particular, the role of blank nodes. Furthermore, we consider basic SPARQL CONSTRUCT and SELECT queries and formalize their operational semantics following a novel algebraic graph transformation approach called POIM.

ІНФОРМАЦІЙНА СИСТЕМА СЕМАНТИЧНОЇ ІДЕНТИФІКАЦІЇ І ДОКУМЕНТУВАННЯ РЕЗУЛЬТАТІВ НЕФОРМАЛЬНОГО Й ІНФОРМАЛЬНОГО НАВЧАННЯ: МЕТОДИ Й ІНСТРУМЕНТИ РОЗРОБКИ

У статті розглядається актуальна проблема – розроблення інформаційної системи семантичної ідентифікації і документування результатів неформального й інформального навчання, яка спрямована на забезпечення ефективної взаємодії ринку праці з ринком освітніх послуг. Проаналізовано стан сучасного ринку праці та економіки, а також необхідність упровадження інформаційних е-засобів для ефективного управління, обробки і використання інформації щодо визнання результатів неформального й інформального навчання, підвищення «видимості» та цінності результатів навчання, які отримані поза системами формального навчання. У роботі розглянуто методи та проаналізовано інструменти розроблення інформаційної системи семантичної ідентифікації і документування результатів неформального й інформального навчання. Зокрема обґрунтовано доцільність використання семантичних технології для обробки даних Semantic Web, головним завданням яких є оброблення інформації на рівні знання; запропоновано в якості основи інформаційної системи використання європейського класифікатора ESCO; модель предметної області описано на основі онтологічних баз знань. У публікації проаналізовано інструменти розробки інформаційної системи, здійснено концептуальне проєктування та розроблення пропонованої системи. Визначено основні можливості та користувачів інформаційної системи, описано основні етапи створення інформаційної системи: створена онтологічна схема; описано процес інтеграції онтології в RDF-сховище; наведені приклади взаємодії з RDF-сховище шляхом застосування мови запитів SPARQL та конекторів GraphD; розроблено архітектуру та інтерфейс користувача запропонованої інформаційної системи. Результати роботи SPARQL-запитів до даних представлені у вигляді наборів результатів. У якості програмних інструментів для розробки вебсервера для надання користувачам доступу до RDF-сховища прийнято мову РНР та PHP фреймворк Laravel. Дослідження обґрунтовують використання бібліотеки React для розробки інтерфейсу користувача інформаційної системи. У публікації визначено, що подальшим напрямком розвитку дослідження може бути імплементація ідей, методів і самої онтології в інші додатки для практичного використання всіма учасниками ринку праці.

How do you Develop a Data Standard? Wikibase might be the Solution…

Data standards allow us to aggregate, compare, compute and communicate data from a wide variety of origins. However, for historical reasons, data are most likely to be stored in many different formats and conform to different models. Every data set might contain a huge amount of information, but it becomes tremendously difficult to compare them without a common way to represent the data. That is when standards development jumps in. Developing a standard is a formidable process, often involving many stakeholders. Typically the initial blueprint of a standard is created by a limited number of people who have a clear view of their use cases. However, as development continues, additional stakeholders participate in the process. As a result, conflicting opinions and interests will influence the development of the standard. Compromises need to be made and the standard might look very different from the initial concept. In order to address the needs of the community, a high level of engagement in the development process is encouraged. However, this does not necessarily increase the usability of the standard. To mitigate this, there is a need to test the standard during the early stages of development. In order to facilitate this, we explored the use of Wikibase to create an initial implementation of the standard. Wikibase is the underlying technology that drives Wikidata. The software is open-source and can be customized for creating collaborative knowledge bases. In addition to containing an RDF (Resource Description Framework) triple store under the hood, it provides users with an easy-to-use graphical user interface (see Fig. 1). This facilitates the use of an implementation of a standard by non-technical users. The Wikibase remains fully flexible in the way data are represented and no data model is enforced. This allows users to map their data onto the standard without any restrictions. Retrieving information from RDF data can be done through the SPARQL query language (W3C 2020). The software package has also a built-in SPARQL endpoint, allowing users to extract the relevant information: Does the standard cover all use cases envisioned? Are parts of the standard underdeveloped? Are the controlled vocabularies sufficient to describe the data? Does the standard cover all use cases envisioned? Are parts of the standard underdeveloped? Are the controlled vocabularies sufficient to describe the data? This strategy was applied during the development of the TDWG Collection Description standard. After completing a rough version of the standard, the different terms that were defined in the first version were transferred to a Wikibase instance running on WBStack (Addshore 2020). Initially, collection data were entered manually, which revealed several issues. The Wikibase allowed us to easily define controlled vocabularies and expand them as needed. The feedback reported from users then flowed back to the further development of the standard. Currently we envisage creating automated scripts that will import data en masse from collections. Using the SPARQL query interface, it will then be straightforward to ensure that data can be extracted from the Wikibase to support the envisaged use cases.

Linked Open Biodiversity Data (LOBD): A semantic application for integrating biodiversity information

Scientists frequently collect biological and environmental information over years and store it in database systems to answer their own research questions without exposing it in repositories that make it easy to find and retrieve. While in recent years the community working on biodiversity informatics has made significant strides by creating common shared vocabularies such as the Darwin Core (DwC, Wieczorek et al. 2012) and publishing mechanisms such as the Integrated Publishing Toolkit (IPT, Robertson et al. 2014), integration is largely limited to the aggregation of datasets and full interoperability has still not been achieved. In this context, The Semantic Web (SW) aims to represent information in a way that, in addition to the human-centered display purposes, it can be used autonomously by machines for integration and reuse across applications. From the biodiversity informatics point of view, interoperability and links among data sources would allow integration of information that is otherwise disconnected, enabling scientists to answer broader questions. These considerations provide strong motivations to formulate a web application considering the semantic interoperability that may provide answers to questions such as the following: (Q1) Is it possible to complement taxonomic, bibliographic and environmental information of a particular species without relying on specific Application Programming Interfaces (APIs)? (Q2) How to relate occurrences of species with environmental variables within a specific region? (Q3) What are the bibliographic references associated with a given species? (Q1) Is it possible to complement taxonomic, bibliographic and environmental information of a particular species without relying on specific Application Programming Interfaces (APIs)? (Q2) How to relate occurrences of species with environmental variables within a specific region? (Q3) What are the bibliographic references associated with a given species? With questions such as these in mind, we present the design of a proof-of-concept application: Linked Open Biodiversity Data (LOBD). LOBD uses Linked Data (LD) (Heath and Bizer 2011) to complement species occurrence information previously extracted from GBIF and converted to Resource Description Framework (RDF) (Zárate et al. 2020) with information about the taxa in question from different RDF datasets, such as Wikidata, NCBI Taxonomy, Springer Nature SciGraph and OpenCitation corpus. A simplified view of the architecture is shown in Fig. 1. To achieve semantic interoperability, we use the SPARQL query language, which allows us not to depend on specific APIs to retrieve information. The application consists of three modules: General information, where the Wikidata endpoint is used to retrieve additional information about the selected species, including links to other databases and information about the species extracted from National Center for Biotechnology Information (NCBI) Taxonomy. Bibliography, where all publications related to the species are retrieved and extracted from OpenCitation. Environment, where users can plot species on a map and add layers related to marine regions as well as environmental layers (e.g., temperature, salinity, etc). General information, where the Wikidata endpoint is used to retrieve additional information about the selected species, including links to other databases and information about the species extracted from National Center for Biotechnology Information (NCBI) Taxonomy. Bibliography, where all publications related to the species are retrieved and extracted from OpenCitation. Environment, where users can plot species on a map and add layers related to marine regions as well as environmental layers (e.g., temperature, salinity, etc). For the development of the application, we use the Shiny framework for R, access to SPARQL endpoints is done through the SPARQL package, marine regions are obtained from marineregion.org and the environmental layers are extracted from Bio-ORACLE. The data used for this article were collected by the Center for the Study of Marine Systems at the National Patagonian Sci-Tech Centre (CCT CENPAT-CONICET), and are published and available through the GBIF network. Linked Data is a powerful tool for scientists, as it allows generating new approaches to biodiversity informatics, which can help to address the data integration challenges. Users would benefit from complementing the current prevalent use of vocabularies that are not ontologically defined (like DwC) for sharing biodiversity data. Although this application is a proof of concept, it shows that with little effort, it is possible to achieve greater interoperability between datasets that were not initially represented as LD.

Розробка інформаційної системи семантичної ідентифікації і документування результатів неформального й інформального навчання

We analyse modern approaches to the interaction of the educational services market with the labour market through recognition of non-formal and informal learning outcomes. The authors prove the expediency of semantic technologies used for information processing at the knowledge level. ESCO classifier that combines services of the labour market and the educational market is examined in detail. This classifier allows jobseekers and employers from different EU member states to handle information on learning outcomes more efficiently. The analyzes of practical ESCO classifier usage on example of the European Employment Service (EURES) demonstrates that this approach provides all necessary information for jobseekers abroad. The ontological analysis is an efficient way to model the ideas of various subject domains and it allows to represent formally their semantics. Hence, the research analyzes the ontological model for this information system and its components; in particular, the authors provide the described content of the ESCO ontological model which consists of three elements – occupations, skills and qualifications. The paper offers an information system for semantic identification and documentation of non-formal and informal learning outcomes based on the ESCO ontological model. The authors select and analyze the main stages of the system development: an ontology scheme in the Protege editor is created; the process of integrating the ontology in the RDF-repository is described; the architecture and user interface of the suggested information system are developed. Semantic graphs database GraphDB is used for ontology storage. To implement the addition and modification of data in GraphDB repository, connectors are used. Access to the RDF-repository is provided with SPARQL query language, whose outcomes are represented as result sets or RDF-graphs. In addition, the research justifies the use of React framework for development of the information system user interface. The architecture and user interface of information system proposed in this research work allow to carry out the semantic identification and documentation of non-formal and informal learning outcomes. Thus, they increase the efficiency of managing the information about these outcomes.

Modern Users of Libraries and the Linked Open Data Environment

The goal of any library is to ensure high quality and general availability of information retrieval tools. The paper describes the project implemented by the Russian State Library (RSL) to present Library Bibliographic Classification as a Networked Knowledge Organization System. The project goal is to support content and provide tools for ensuring system’s interoperability with other resources of the same nature (i.e. with Linked Data Vocabularies) in the global network environment. The project was partially supported by the Russian Foundation for Basic Research (RFBR).The RSL General Classified Catalogue (GCC) was selected as the main data source for the Classification system of knowledge organization. The meaning of each classification number is expressed by complete string of wordings (captions), rather than the last level caption alone. Data converted to the Resource Description Framework (RDF) files based on the standard set of properties defined in the Simple Knowledge Organization System (SKOS) model was loaded into the semantic storage for subsequent data processing using the SPARQL query language. In order to enrich user queries for search of resources, the RSL has published its Classification System in the form of Linked Open Data (https://lod.rsl.ru) for searching in the RSL electronic catalogue. Currently, the work is underway to enable its smooth integration with other LOD vocabularies. The SKOS mapping tags are used to differentiate the types of connections between SKOS elements (concepts) existing in different concept schemes, for example, UDC, MeSH, authority data.The conceptual schemes of the leading classifications are fundamentally different from each other. Establishing correspondence between concepts is possible only on the basis of lexical and structural analysis to compute the concept similarity as a combination of attributes.The authors are looking forward to working with libraries in Russia and other countries to create a common space of Linked Open Data vocabularies.

RDFFrames

Knowledge graphs represented in RDF are becoming increasingly popular and are essential to many machine learning applications. A rich ecosystem of RDF data management systems and tools has evolved over the years, most notably RDF database management systems that support the SPARQL query language. Surprisingly, machine learning tools for knowledge graphs typically do not use SPARQL despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of expected data model and interface style. Machine learning tools work on data in tabular format and process it using imperative relational API calls, while SPARQL matches graph patterns to RDF triples. To access knowledge graphs for machine learning, we observe that it is more natural to use a navigational paradigm based on graph traversal rather than the SPARQL paradigm based on triple patterns. We demonstrate RDFFrames, a framework that bridges the gap between machine learning tools and RDF database systems by offering the usability and flexibility of machine learning tools together with the performance of a database system. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPARQL query, executes it on the database system, and returns the results in a standard tabular format.

A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL.

The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple data sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the equivalent SPARQLconstructs required to benefit from this data - in particular, recursive property paths. In this article, we provide a hands-on introduction to querying evolutionary data across several data sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different data sources can be compared, through the use of federated SPARQL queries.