RDF Format Research Articles

Arquitetura da informação para publicação de dados abertos conectados

Publication of linked open data is not widespread, preventing the web from reaching its full potential. Thus, the objective is to propose structuring the data directly in the information systems in which they are generated, through the inclusion of a step for representing the business metadata in RDF format, in the methodology of information architecture for business processes (AIPN). An analysis of the literature and documentation of the W3C was carried out, in addition to applied research. It was observed that there are analogous elements between the RDF structure and the AIPN, which enables the inclusion of a semantic structure in the production of data through an RDF description step of the business metadata.

MatKG: An autonomously generated knowledge graph in Material Science

In this paper, we present MatKG, a knowledge graph in materials science that offers a repository of entities and relationships extracted from scientific literature. Using advanced natural language processing techniques, MatKG includes an array of entities, including materials, properties, applications, characterization and synthesis methods, descriptors, and symmetry phase labels. The graph is formulated based on statistical metrics, encompassing over 70,000 entities and 5.4 million unique triples. To enhance accessibility and utility, we have serialized MatKG in both CSV and RDF formats and made these, along with the code base, available to the research community. As the largest knowledge graph in materials science to date, MatKG provides structured organization of domain-specific data. Its deployment holds promise for various applications, including material discovery, recommendation systems, and advanced analytics.

Semantic Modelling Approach for Safety-Related Traffic Information Using DATEX II

The significance of Linked Open Data datasets for traffic information extends beyond just including open traffic data. It incorporates links to other relevant thematic datasets available on the web. This enables federated queries across different data platforms from various countries and sectors, such as transport, geospatial, environmental, weather, and more. Businesses, researchers, national operators, administrators, and citizens at large can benefit from having dynamic traffic open data connected to heterogeneous datasets across Member States. This paper focuses on the development of a semantic model that enhances the basic service to access open traffic data through a LOD-enhanced Traffic Information System in alignment with the ITS Directive (2010/40/EU). The objective is not limited to just viewing or downloading data but also to improve the extraction of meaningful information and enable other types of services that are only achievable through LOD. By structuring the information using the RDF format meant for machines and employing SPARQL for querying, LOD allows for comprehensive and unified access to all datasets. Considering that the European standard DATEX II is widely used in many priority areas and services mentioned in the ITS Directive, LOD DATEX II was developed as a complementary approach to DATEX II XML. This facilitates the accessibility and comprehensibility of European traffic data and services. As part of this development, an ontological model called dtx_srti, based on the DATEX II Ontology, was created to support these efforts.

Efficient compliance checking of RDF data

Abstract Automated compliance checking, i.e. the task of automatically assessing whether states of affairs comply with normative systems, has recently received a lot of attention from the scientific community, also as a consequence of the increasing investments in Artificial Intelligence technologies for the legal domain (LegalTech). The authors of this paper deem as crucial the research and implementation of compliance checkers that can directly process data in RDF format, as nowadays more and more (big) data in this format are becoming available worldwide, across a multitude of different domains. Among the automated technologies that have been used in recent literature, to the best of our knowledge, only two of them have been evaluated with input states of affairs encoded in RDF format. This paper formalizes a selected use case in these two technologies and compares the implementations, also in terms of simulations with respect to shared synthetic datasets.

An approach towards removal of data heterogeneity in SDN-based IoT framework

IoT is an emerging technology that provides innovative solutions to our day-to-day life challenges. With billions of diverse smart objects, IoT produces gigantic amount of heterogeneous data that mostly need to be processed in real time and shared across systems and applications. For IoT data to be truly accessable and interpretable, the data heterogeneity problem needs to be resolved first. Semantic technologies is one of the most used solutions for data integration across systems. Data represented as knowledge graphs enables easier querying and matching. To address this, we propose an SDN based IoT architectural framework utilizing semantics for data integration and sharing using Kafka and Spark Streaming for real-time data handling. One main contribution of this work is that any consistent IoT ontology, defined using Web Ontology Language(OWL) which explicitly specify its classes, objects and data properties, can be used to convert the stream data into RDF format automatically using Natural Language Processing endeavor. SDN helps in dynamically locating the data producing devices such as sensors for providing flexible data routing. We have presented a testbed experimentation framework and evaluated the results with simulated virtual sensors. The homogeneous results from diverse datasets was generated in acceptable minimal time. A use-case for Air Quality Monitoring is given and SPARQL queries executed have shown satisfactory response time with over thousands of triples.

FAIR data station for lightweight metadata management and validation of omics studies.

The life sciences are one of the biggest suppliers of scientific data. Reusing and connecting these data can uncover hidden insights and lead to new concepts. Efficient reuse of these datasets is strongly promoted when they are interlinked with a sufficient amount of machine-actionable metadata. While the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles have been accepted by all stakeholders, in practice, there are only a limited number of easy-to-adopt implementations available that fulfill the needs of data producers. We developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It implements the ISA metadata framework and uses minimal information metadata standards to capture experiment metadata. The FAIR Data Station consists of 3 modules. Based on the minimal information model(s) selected by the user, the "form generation module" creates a metadata template Excel workbook with a header row of machine-actionable attribute names. The Excel workbook is subsequently used by the data producer(s) as a familiar environment for sample metadata registration. At any point during this process, the format of the recorded values can be checked using the "validation module." Finally, the "resource module" can be used to convert the set of metadata recorded in the Excel workbook in RDF format, enabling (cross-project) (meta)data searches and, for publishing of sequence data, in an European Nucleotide Archive-compatible XML metadata file. Turning FAIR into reality requires the availability of easy-to-adopt data FAIRification workflows that are also of direct use for data producers. As such, the FAIR Data Station provides, in addition to the means to correctly FAIRify (omics) data, the means to build searchable metadata databases of similar projects and can assist in ENA metadata submission of sequence data. The FAIR Data Station is available at https://fairbydesign.nl.

Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata.

Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides an ideal forum for exchanging structured data that can be verified and consolidated using validation schemas and bot edits. In this research article, we catalog an automatable task set necessary to assess and validate the portion of Wikidata relating to the COVID-19 epidemiology. These tasks assess statistical data and are implemented in SPARQL, a query language for semantic databases. We demonstrate the efficiency of our methods for evaluating structured non-relational information on COVID-19 in Wikidata, and its applicability in collaborative ontologies and knowledge graphs more broadly. We show the advantages and limitations of our proposed approach by comparing it to the features of other methods for the validation of linked web data as revealed by previous research.

Transforming Points of Single Contact Data into Linked Data

Open data portals contain valuable information for citizens and business. However, searching for information can prove to be tiresome even in portals tackling domains similar information. A typical case is the information residing in the European Commission’s portals supported by Member States aiming to facilitate service provision activities for EU citizens and businesses. The current work followed the FAIR principles (Findability, Accessibility, Interoperability, and Reuse of digital assets) as well as the GO-FAIR principles and tried to transform raw data into fair data. The innovative part of this work is the mapping of information residing in various governmental portals (Points of Single Contacts) by transforming information appearing in them in RDF format (i.e., as Linked data), in order to make them easily accessible, exchangeable, interoperable and publishable as linked open data. Mapping was performed using the semantic model of a single portal, i.e., the enriched Greek e-GIF ontology and by retrieving and analyzing raw, i.e., non-FAIR data, by defining the semantic model and by making data linkable. The Data mapping process proved to require a significant manual effort and revealed that data value remains unexplored due to poor data representation. It also highlighted the need for appropriately designing and implementing horizontal actions addressing an important number of recipients in an interoperable way.

Development of an Information Research Platform for Data-Driven Agriculture

Comprehensive research data are acknowledged as a necessity for research acceleration. Research institutes and universities are engaged in developing research data management systems. The National Agriculture and Food Research Organization of Japan (NARO) developed NARO-linked databases (Narolin DBs) in addition to a supercomputer. In the Narolin DB various research data on agriculture are cataloged using common metadata. The relationship between complicated data in natural science is described in RDF, property graph, or RDB format to facilitate the application of statistical analysis and machine learning. Our system is unique in that it is connected to a data catalog, a private cloud database, a supercomputer for data analysis, and a data/service portal for business applications, such as a data pipeline. Through the development of agricultural information research platforms, NARO will accelerate data-driven agricultural research at various stages in the agricultural supply chain, ranging from genome analysis to plant breeding, cultivation, food processing, and food distribution.

Stochastic Data-to-Text Generation Using Syntactic Dependency Information

Data-to-Text Generation (D2T) is one of the most important sub-fields of Natural Language Generation where structured data is transcribed into natural language text. Several solutions have been proposed for D2T so far with relative success, including template-based, phrase structure grammar-based, and neural attention models. However, these methods also have problems such as grammatical flaws, limited naturalness, and semantic deficiencies. In this work, we propose a stochastic corpus-based model for the data-to-text generation that produces a tree-form structure for sentences based on dependency information. This information includes the dependency relations between words and meaning labels extracted from the aligned training sentences parsed with a dependency parser. By combining the dependency relations and meaning labels to construct a tree structure in an up-down manner, each word is placed into the output sentence based on its preceding and succeeding words. This results in fluent sentences with correct grammatical structures. This approach also ensures that all required semantic information are present in the output sentences while irrelevant or redundant labels are avoided. In addition, by using beam search in producing the structure of sentences, the proposed model can generate highly diverse sentences. We test our model on eight domains in tabular, dialogue act, and RDF formats. Our model improves the BLEU by 30% compared to the corpus-based state-of-the-art methods trained on the tabular datasets and also achieves comparable results with the neural network-based approaches trained on dialogue act, E2E, and WebNLG datasets in the BLEU evaluation metric. Furthermore, the value of ERR metric for our results is always zero; that means our model generates sentences without losing any information. Human evaluations show that our model produces high-quality utterances in aspects of informativeness and naturalness as well as quality.

Building and Applying Knowledge Graph in Edge Analytics Environment

For the scenario of limited hardware resources and restricted software environment in edge computing architecture, the method of building and applying knowledge graph in edge analytics environment is proposed. The main process includes: building knowledge graph in the cloud, storing knowledge base with RDF format at the edge through customization and transformation, and performing query and analytics at the edge with SPARQL graph search language. The method is simulated in a communication anti-jamming test environment, and the results show that the relevant technical solutions can better meet the requirements of construction of knowledge graph and data analysis in a restricted edge analytics environment.

Efficient Retrieval of Data Using Semantic Search Engine Based on NLP and RDF

With the evolution of Web 3.0, the traditional algorithm of searching Web 2.0 would become obsolete and underperform in retrieving the precise and accurate information from the growing semantic web. It is very reasonable to presume that common users might not possess any understanding of the ontology used in the knowledge base or SPARQL query. Therefore, providing easy access of this enormous knowledge base to all level of users is challenging. The ability for all level of users to effortlessly formulate structure query such as SPARQL is very diverse. In this paper, semantic web based search methodology is proposed which converts user query in natural language into SPARQL query, which could be directed to domain ontology based knowledge base. Each query word is further mapped to the relevant concept or relations in ontology. Score is assigned to each mapping to find out the best possible mapping for the query generation. Mapping with highest score are taken into consideration along with interrogative or other function to finally formulate the user query into SPARQL query. If there is no search result retrieved from the knowledge base, then instead of returning null to the user, the query is further directed to the Web 3.0. The top “k” documents are considered to further converting them into RDF format using Text2Onto tool and the corpus of semantically structured web documents is build. Alongside, semantic crawl agent is used to get <Subject-Predicate-Object> set from the semantic wiki. The Term Frequency Matrix and Co-occurrence Matrix are applied on the corpus following by singular Value decomposition (SVD) to find the results relevant for the user query. The result evaluations proved that the proposed system is efficient in terms of execution time, precision, recall and f-measures.

Organizing knowledge to enable faster data interpretation in COVID-19 research

Enormous volumes of COVID-19 research data have been published and this continues to increase daily. This creates challenges for researchers to interpret, prioritize and summarize their own findings in the context of published literature, clinical trials, and a multitude of databases. Overcoming the data interpretation bottleneck is vital to help researchers to be more efficient in their quest to identify COVID-19 risk factors, potential treatments, drug side-effects, and much more. As a proof of concept, we have organized and integrated a range of COVID-19 and human biomedical data and literature into a knowledge graph (KG). Here we present the datasets we have integrated so far and the content of the KG which consists of 674,969 biological concepts and over 1.6 million relationships between them. The COVID-19 KG is available via KnetMiner, an interactive online platform for gene discovery and knowledge mining, or via RDF and Neo4j graph formats which can be searched programmatically through SPARQL and Cypher endpoints. KnetMiner is a road mapped ELIXIR UK service. We hope this integrated resource will enable faster data interpretation and discovery of linkages between genes, drugs, diseases and many more types of information relating to COVID-19.

CogNet: Bridging Linguistic Knowledge, World Knowledge and Commonsense Knowledge

In this paper, we present CogNet, a knowledge base (KB) dedicated to integrating three types of knowledge: (1) linguistic knowledge from FrameNet, which schematically describes situations, objects and events. (2) world knowledge from YAGO, Freebase, DBpedia and Wikidata, which provides explicit knowledge about specific instances. (3) commonsense knowledge from ConceptNet, which describes implicit general facts. To model these different types of knowledge consistently, we introduce a three-level unified frame-styled representation architecture. To integrate free-form commonsense knowledge with other structured knowledge, we propose a strategy that combines automated labeling and crowdsourced annotation. At present, CogNet integrates 1,000+ semantic frames from linguistic KBs, 20,000,000+ frame instances from world KBs, as well as 90,000+ commonsense assertions from commonsense KBs. All these data can be easily queried and explored on our online platform, and free to download in RDF format for utilization under a CC-BY-SA 4.0 license. The demo and data are available at http://cognet.top/.

АРХИТЕКТУРА ПОСТРОЕНИЯ БАЗЫ ЗНАНИЙ ПРОЕКТНОЙ ОРГАНИЗАЦИИ

The article presents the results of software package development to create a knowledge base for a design organization. The definition of the concept of knowledge is presented on the basis of existing definitions in open sources. The architecture of the software package is described, its main modules are listed, as well as third-party software systems used in the development of the software package. The article considers the organization of data storage subsystem of the software complex and describes the data model of this subsystem. An approach to organize a repository of the software package ontologies based on a database in an RDF format is described. The software structure for the implementation of connectors for integration with the existing systems of the design organization is presented. The article also describes a method for expanding a search query using ontology and a full-text search to take into account the features of the subject area in the search process. The information and digital model of the user of the design organization is described with reference to the artifacts of the design organization.

A Linked Open Data-Oriented Sustainable System for Transparency and Open Access to Government Data: A Case Study of the Public’s Response to Women’s Driving in Saudi Arabia

Due to the wide-ranging development of data-oriented sustainable systems in the government and the public sectors, the development of such sustainable systems is replete with potential. The ultimate focus of developing these sustainable systems is to provide citizens with transparency, accountability, awareness as well as a single point of query for asking integrated and smart queries. In view of these benefits, the Saudi government has taken the initiative to publish and develop sustainable open data-oriented information systems. However some major challenges in the Saudi Government Open Data are that the (1) data are published and available in different formats such as Excel sheets, CSV files (Comma Separated Values), images, scanned documents and social media sources such as Twitter, (2) datasets from different government departments are not linked with each other or to existing datasets in Linked Open Data Cloud (even though they have strong links with each other), and (3) there is no SPARQL Endpoint that can be used to pose smart semantic-based queries to Saudi Government Data. This paper is part of an ongoing research project to present a framework that can be used to transfer the government data from different sources to RDF format. The framework can also be used to clean and classify/map the data according to the Saudi Government Ontology. We also describe our approach for semiautomatically linking Saudi Government Datasets with one another as well as with other existing open datasets, thus resulting in the Saudi Linked Open Government Data Cloud (SLOGDC). Finally, taking the topic “Public’s Response to Women’s Driving in Saudi Arabia” as a case study, we demonstrate the SLOGD SPARQL Endpoint as a data-oriented system by executing different queries and analyzing results of these queries. This work also contributes new insights into women’s driving in Saudi Arabia using the SLOGDC, thus suggesting the way forward in shaping policies for decision-making.

An algorithm to generate short sentences in natural language from linked open data based on linguistic templates

The generation of natural language phrases from Linked Open Data can benefit from a significant amount of information available on the internet, as well as from the existence of properties within them, which appears, mostly, in the RDF format. These properties can represent semantic relationships between concepts that might help in creating sentences in natural language. Nevertheless, research in this field tends not to use the information in RDF. We support that this is a factor that might foster the generation of more natural phrases. In this scenario, this research explores these RDF properties for the generation of natural language phrases. The short sentences generated by the algorithm implementation were evaluated regarding their fluency by linguists and native English speakers. The results show that the sentences generated are promising regarding sentence fluency.

An algorithm to generate short sentences in natural language from linked open data based on linguistic templates

The generation of natural language phrases from Linked Open Data can benefit from a significant amount of information available on the internet, as well as from the existence of properties within them, which appears, mostly, in the RDF format. These properties can represent semantic relationships between concepts that might help in creating sentences in natural language. Nevertheless, research in this field tends not to use the information in RDF. We support that this is a factor that might foster the generation of more natural phrases. In this scenario, this research explores these RDF properties for the generation of natural language phrases. The short sentences generated by the algorithm implementation were evaluated regarding their fluency by linguists and native English speakers. The results show that the sentences generated are promising regarding sentence fluency.

Використання семантичних технологій у діяльності ЗВО: прикладний аспект

The article considers the problem of using Big Data in a modern economics and public life. The volumes and complexity of information are growing rapidly, but modern technologies cannot ensure their effective use. There is a lag in technologies, methods, and practices for using Big Data. The imbalance can be changed by semantic technologies, characterized by a different approach to the processing and use of data. This approach is based on the use of knowledge. Proved that despite the rather long time of the existence of semantic technologies and semantic networks, there are many obstacles to their effective application. These are the problems of accessibility of semantic content, accessibility of ontologies, their evolution, scalability and multilingualism. And since far from all the data presented on the network is created in terms of semantic markup and is unlikely to be brought to it in the future, the problem of accessibility of semantic content is one of the main ones. The article shows the difference between the semantic network and the semantic Web, and also indicates the development technologies of the latter. As the subject of study, the module of the courses of the Alfred Nobel University was selected. The composition of a separate module or a separate course is examined in detail: data on the university, lecturer, data on the provision of the course and language of its teaching, acquired skills, abilities, results and the like. A graph of the module of courses has been built on the example of the Alfred Nobel University in terms of ontology, its individual, most significant classes – components are considered. The main classes, subclasses and their contents are considered, data types (date, text, URL) are indicated. The ontological scheme has been converted to the RDF format, such as is necessary for modelling data in the semantic network and further research. The prospects for further research on the application of the selected model for representing knowledge, using the query language, obtaining and interpreting data from other universities, etc. are determined. Keywords: semantic technologies, semantic networks, ontologies, CmapTools, course module graph.

Generating RDF resources from web open data portals

<span>Open data are available from various private and public institutions in different resource formats. There are already great number of open data that are published using open data portals, where datasets and resources are mainly presented in tabular or sheet formats. However, such formats have some barriers with application developments and web standards. One of the web recommenced standards for semantic web application is RDF. There are various research efforts have been focused on presenting open data in RDF formats. However, no framework has transformed tabular open data into RDFs considering the HTML tags and properties of the resources and datasets. Therefore, a methodology is required to generate RDF resources from this type of open data resources. This methodology applies data transformations of open data from a tabular format to RDF files for the Saudi Open Data Portal. The methodology successfully transforms open data resources in sheet format into RDF resources. Recommendations and future work are given to enhance the development of building open data.</span>