Semantic Web Standards Research Articles

From Clinical Information Systems to Personalized Health Knowledge Graphs.

This paper presents a versatile solution to formally represent the contents of electronic health records. It is based on the knowledge graph paradigm, and semantic web standards RDF and OWL. It employs the established semantic standards SNOMED CT and FHIR, which warrant international interoperability. A graph-based form is not only useful to feed different target visualizations, but it can also be subject to AI-powered services such as (fuzzy) retrieval and summarization.

Semantic Web Usage in the Tourism Industry in Andalusia, Spain

Andalusia got the 4th place of tourists’ favorite destinations among 17 autonomous communities in Spain in September 2023 and almost returned to pre-pandemic normality regarding tourism. Although Spain is famous for its “sun and beach”, its tourist industry is facing new challenges in socio-economic, environmental and territorial issues; therefore, the role of artificial intelligence cannot be ignored in the evolution and transformation of the tourism business. AI algorithms reinforce decision-making capabilities through analysing real-time data. Effectiveness and efficiency of AI algorithms are dependent on well-structured, well-described and machine-readable meta data network which can be provided by Semantic Web standards. This article empirically study the current usage of Semantic Web formats, ontologies and touristic topics in 65 tourist-relevant web sites in Andalusia. Results show that just 57 % of examined sites used semantic markup with most of the semantic annotations (98.5%) made in three formats: Microdata, JSON-LD and RDFa. Furthermore, the Ontologies used in almost 90% of markups are ‘Schema.org’, ‘Data Vocabulary’ and ‘RDF’, but none of the special tourism ontologies appeared in examined tourism web sites. Therefore, Andalusia tourism websites should be enhanced with much more widespread Semantic Web in order to achieve a better performance in a future in which AI technologies will be widespread.

LinkedDataOps:quality oriented end-to-end geospatial linked data production governance

This work describes the application of semantic web standards to data quality governance of data production pipelines in the architectural, engineering, and construction (AEC) domain for Ordnance Survey Ireland (OSi). It illustrates a new approach to data quality governance based on establishing a unified knowledge graph for data quality measurements across a complex, heterogeneous, quality-centric data production pipeline. It provides the first comprehensive formal mappings between semantic models of data quality dimensions defined by the four International Organization for Standardization (ISO) and World Wide Web Consortium (W3C) data quality standards applied by different tools and stakeholders. It provides an approach to uplift rule-based data quality reports into quality metrics suitable for aggregation and end-to-end analysis. Current industrial practice tends towards stove-piped, vendor-specific and domain-dependent tools to process data quality observations however there is a lack of open techniques and methodologies for combining quality measurements derived from different data quality standards to provide end-to-end data quality reporting, root cause analysis or visualisation. This work demonstrated that it is effective to use a knowledge graph and semantic web standards to unify distributed data quality monitoring in an organisation and present the results in an end-to-end data dashboard in a data quality standards-agnostic fashion for the Ordnance Survey Ireland data publishing pipeline.

OpenCitations Meta

Abstract OpenCitations Meta is a new database for open bibliographic metadata of scholarly publications involved in the citations indexed by the OpenCitations infrastructure, adhering to Open Science principles and published under a CC0 license to promote maximum reuse. It presently incorporates bibliographic metadata for publications recorded in Crossref, DataCite, and PubMed, making it the largest bibliographic metadata source using Semantic Web technologies. It assigns new globally persistent identifiers (PIDs), known as OpenCitations Meta Identifiers (OMIDs) to all bibliographic resources, enabling it both to disambiguate publications described using different external PIDS (e.g., a DOI in Crossref and a PMID in PubMed) and to handle citations involving publications lacking external PIDs. By hosting bibliographic metadata internally, OpenCitations Meta eliminates its former reliance on API calls to external resources and thus enhances performance in response to user queries. Its automated data curation, following the OpenCitations Data Model, includes deduplication, error correction, metadata enrichment, and full provenance tracking, ensuring transparency and traceability of data and bolstering confidence in data integrity, a feature unparalleled in other bibliographic databases. Its commitment to Semantic Web standards ensures superior interoperability compared to other machine-readable formats, with availability via a SPARQL endpoint, REST APIs, and data dumps.

IMGT/mAb-KG: the knowledge graph for therapeutic monoclonal antibodies.

Therapeutic monoclonal antibodies (mAbs) have demonstrated promising outcomes in diverse clinical indications, including but not limited to graft rejection, cancer, and autoimmune diseases lately.Recognizing the crucial need for the scientific community to quickly and easily access dependable information on monoclonal antibodies (mAbs), IMGT®, the international ImMunoGeneTics information system®, provides a unique and invaluable resource: IMGT/mAb-DB, a comprehensive database of therapeutic mAbs, accessible via a user-friendly web interface. However, this approach restricts more sophisticated queries and segregates information from other databases. To connect IMGT/mAb-DB with the rest of the IMGT databases, we created IMGT/mAb-KG, a knowledge graph for therapeutic monoclonal antibodies connected to IMGT structures and genomics databases. IMGT/mAb-KG is developed using the most effective methodologies and standards of semantic web and acquires data from IMGT/mAb-DB. Concerning interoperability, IMGT/mAb-KG reuses terms from biomedical resources and is connected to related resources. In February 2024, IMGT/mAb-KG, encompassing a total of 139,629 triplets, provides access to 1,489 mAbs, approximately 500 targets, and over 500 clinical indications. It offers detailed insights into the mechanisms of action of mAbs, their construction, and their various products and associated studies. Linked to other resources such as Thera-SAbDab (Therapeutic Structural Antibody Database), PharmGKB (a comprehensive resource curating knowledge on the impact of genetic variation on drug response), PubMed, and HGNC (HUGO Gene Nomenclature Committee), IMGT/mAb-KG is an essential resource for mAb development. A user-friendly web interface facilitates the exploration and analyse of the content of IMGT/mAb-KG.

Leveraging Ontology Development to Enhance Corrosion Visualisation in Engineering Design

Designing automated solutions for understanding, visualising, and detecting damage has become a key area in the era of digitalisation and advanced engineering. The use of deep learning and other artificial intelligence approaches has proven very innovative and rendered manual methods of damage inspection primitive. However, there remains a critical gap in creating a unified knowledge base that helps in understanding, conceptualising, and promoting collaborative engineering design, particularly in the context of automated corrosion detection through images. This research addresses this gap by presenting a conceptualised model for understanding corrosion detection through ontology development. This is implemented in an ontology development environment using Protege 5.5.0 and ELK 0.5.0 reasoner. Evaluation is done using expert competency questions and hypothetical scenarios thereby establishing a robust framework that is beneficial to Engineering design in terms of terminology standardisation, design process facilitation, and building corrosion inspection systems that are interoperable. The Ontology also enables sharing and reusing knowledge between automated corrosion detection systems as well as integration with existing industrial standards such as Industrial Ontology Foundry (IOF) and semantic web standards. Hence, offering a significant contribution to the digitalisation of engineering design. Besides the enhancement of damage detection, this work also advances the engineering field to utilise visual data more effectively in design, maintenance, and product lifecycle management.

Shape Expressions (ShEx) schemas for the FHIR R5 specification

This work continues along a visionary path of using Semantic Web standards such as RDF and ShEx to make healthcare data easier to integrate for research and leading-edge patient care. The work extends the ability to use ShEx schemas to validate FHIR RDF data, thereby enhancing the semantic web ecosystem for working with FHIR and non-FHIR data using the same ShEx validation framework. It updates FHIR's ShEx schemas to fix outstanding issues and reflect changes in the definition of FHIR RDF. In addition, it experiments with expressing FHIRPath constraints (which are not captured in the XML or JSON schemas) in ShEx schemas. These extended ShEx schemas were incorporated into the FHIR R5 specification and used to successfully validate FHIR R5 examples that are included with the FHIR specification, revealing several errors in the examples.

Development of an intelligent information resource model based on modern natural language processing methods

<span lang="EN-US">Currently, there is an avalanche-like increase in the need for automatic text processing, respectively, new effective methods and tools for processing texts in natural language are emerging. Although these methods, tools and resources are mostly presented on the internet, many of them remain inaccessible to developers, since they are not systematized, distributed in various directories or on separate sites of both humanitarian and technical orientation. All this greatly complicates their search and practical use in conducting research in computational linguistics and developing applied systems for natural text processing. This paper is aimed at solving the need described above. The paper goal is to develop model of an intelligent information resource based on modern methods of natural language processing (IIR NLP). The main goal of IIR NLP is to render convenient valuable access for specialists in the field of computational linguistics. The originality of our proposed approach is that the developed ontology of the subject area “NLP” will be used to systematize all the above knowledge, data, information resources and organize meaningful access to them, and semantic web standards and technology tools will be used as a software basis.</span>

FUKG: answering flexible queries over knowledge graphs

PurposeThere is an increasing interest in the use of knowledge graphs to represent real-world knowledge and a common need to manage imprecise knowledge in many real-world applications. This paper aims to study approaches to solve flexible queries over knowledge graphs.Design/methodology/approachBy introducing fuzzy logic in the query answering process, the authors are able to obtain a novel algorithm to solve flexible queries over knowledge graphs. This approach is implemented in the FUzzy Knowledge Graphs system, a software tool with an intuitive user-graphical interface.FindingsThis approach makes it possible to reuse semantic web standards (RDF, SPARQL and OWL 2) and builds a fuzzy layer on top of them. The application to a use case shows that the system can aggregate information in different ways by selecting different fusion operators and adapting to different user needs.Originality/valueThis approach is more general than similar previous works in the literature and provides a specific way to represent the flexible restrictions (using fuzzy OWL 2 datatypes).

Leveraging Semantic Technologies for Collaborative Inference of Threatening IoT Dependencies

IoT Device Management (DM) refers to the remote administration of customer devices. In practice, DM is ensured by multiple actors such as operators or device manufacturers, each operating independently via their DM solution. These siloed DM solutions are limited in addressing IoT threats related to device dependencies, such as cascading failures, as these threats spread across devices managed by different DM actors, and their mitigation can no longer be performed without collaborative DM efforts. The first step toward collaborative mitigation of these threats is the identification of threatening dependency topology. However, this task is challenging, requiring the inference of dependencies from the data held by different actors. In this work, we propose a collaborative framework that infers the threatening topology of dependencies by accessing and aggregating data from legacy DM solutions. It combines the assets of Semantic Web standards and Digital Twin technology to capture on-demand the topology of dependencies, and it is designed to be used in business applications such as customer care to enhance customer Quality of Experience. We integrate our solution within the in-use Orange's Digital Twin platform Thing in the future and demonstrate its effectiveness by automatically inferring threatening dependencies in the two settings: a simulated smart home scenario managed by ground-truth DM solutions, such as Orange's implementation of the USP Controller and Samsung's SmartThings Platform , and a realistic smart home called DOMUS testbed.

Experience in Integration of University Knowledge Bases Based on Semantic Web Technologies

The paper is devoted to the study of optimal algorithms for virtual integration of university knowledge bases in the field of computer science and programming with external data sources in Russian and English. Data from external sources can be presented in RDF, OWL, XML, HTML, JSON, CSV formats, in the form of relational, graph databases, or not structured at all. The proposed algorithms will provide a methodological and technological basis for creating problem-oriented knowledge bases as artificial intelligence systems, as well as prerequisites for the development of semantic technologies for acquiring new knowledge on the Internet without direct human participation. Testing of the studied machine learning algorithms is carried out by the method of sliding control (cross-validation) on specialized text corpora. The novelty of the presented study is due to the application of the Paretos optimality principle for multicriteria evaluation and ranking of the studied algorithms in the absence of a priori information about the comparative significance of the criteria. The project is implemented in accordance with semantic web standards. The architecture of the semantic web portal and usage examples are given. The proposed software solutions are based on cloud computing using DBaaS and PaaS service models to ensure scalability of data warehouses and network services. The created software is publicly available and can be freely used.

FAIR-Checker: supporting digital resource findability and reuse with Knowledge Graphs and Semantic Web standards

The current rise of Open Science and Reproducibility in the Life Sciences requires the creation of rich, machine-actionable metadata in order to better share and reuse biological digital resources such as datasets, bioinformatics tools, training materials, etc. For this purpose, FAIR principles have been defined for both data and metadata and adopted by large communities, leading to the definition of specific metrics. However, automatic FAIRness assessment is still difficult because computational evaluations frequently require technical expertise and can be time-consuming. As a first step to address these issues, we propose FAIR-Checker, a web-based tool to assess the FAIRness of metadata presented by digital resources. FAIR-Checker offers two main facets: a “Check” module providing a thorough metadata evaluation and recommendations, and an “Inspect” module which assists users in improving metadata quality and therefore the FAIRness of their resource. FAIR-Checker leverages Semantic Web standards and technologies such as SPARQL queries and SHACL constraints to automatically assess FAIR metrics. Users are notified of missing, necessary, or recommended metadata for various resource categories. We evaluate FAIR-Checker in the context of improving the FAIRification of individual resources, through better metadata, as well as analyzing the FAIRness of more than 25 thousand bioinformatics software descriptions.

The Hidden History of the Like Button: From Decentralized Data to Semantic Enclosure

Artificial intelligence relies on the use of semantic technologies to represent the shared world of humanity. The story of how this came to be is exemplified by the use of Semantic Web standards by the Facebook “Like” button. In the case of the “Like” button, a decentralized and open Semantic Web was used to fuel the accumulation of personal data for advertising throughout the entire Web. The advent of the “Like button” was shortly followed by Google’s creation of the Google Knowledge Graph, a private corporate version of the Semantic Web. In fact, every major company in Silicon Valley soon created its own knowledge graph. The Semantic Web was transformed from a democratic project for standardized open knowledge to a project of control, collapsing semantics and erasing the difference between the object qua object and the object as represented in a knowledge graph.

Application of machine learning methods for filling and updating nuclear knowledge bases

The paper deals with issues of designing and creating knowledge bases in the field of nuclear science and technology. The authors present the results of searching for and testing optimal classification and semantic annotation algorithms applied to the textual network content for the convenience of computer-aided filling and updating of scalable semantic repositories (knowledge bases) in the field of nuclear physics and nuclear power engineering and, in the future, for other subject areas, both in Russian and English. The proposed algorithms will provide a methodological and technological basis for creating problem-oriented knowledge bases as artificial intelligence systems, as well as prerequisites for the development of semantic technologies for acquiring new knowledge on the Internet without direct human participation. Testing of the studied machine learning algorithms is carried out by the cross-validation method using corpora of specialized texts. The novelty of the presented study lies in the application of the Pareto optimality principle for multi-criteria evaluation and ranking of the studied algorithms in the absence of a priori information about the comparative significance of the criteria. The project is implemented in accordance with the Semantic Web standards (RDF, OWL, SPARQL, etc.). There are no technological restrictions for integrating the created knowledge bases with third-party data repositories as well as metasearch, library, reference or information and question-answer systems. The proposed software solutions are based on cloud computing using DBaaS and PaaS service models to ensure the scalability of data warehouses and network services. The created software is in the public domain and can be freely replicated.

PPIntegrator: semantic integrative system for protein-protein interaction and application for host-pathogen datasets.

Semantic web standards have shown importance in the last 20 years in promoting data formalization and interlinking between the existing knowledge graphs. In this context, several ontologies and data integration initiatives have emerged in recent years for the biological area, such as the broadly used Gene Ontology that contains metadata to annotate gene function and subcellular location. Another important subject in the biological area is protein-protein interactions (PPIs) which have applications like protein function inference. Current PPI databases have heterogeneous exportation methods that challenge their integration and analysis. Presently, several initiatives of ontologies covering some concepts of the PPI domain are available to promote interoperability across datasets. However, the efforts to stimulate guidelines for automatic semantic data integration and analysis for PPIs in these datasets are limited. Here, we present PPIntegrator, a system that semantically describes data related to protein interactions. We also introduce an enrichment pipeline to generate, predict and validate new potential host-pathogen datasets by transitivity analysis. PPIntegrator contains a data preparation module to organize data from three reference databases and a triplification and data fusion module to describe the provenance information and results. This work provides an overview of the PPIntegrator system applied to integrate and compare host-pathogen PPI datasets from four bacterial species using our proposed transitivity analysis pipeline. We also demonstrated some critical queries to analyze this kind of data and highlight the importance and usage of the semantic data generated by our system. https://github.com/YasCoMa/ppintegrator, https://github.com/YasCoMa/ppi_validation_process and https://github.com/YasCoMa/predprin.

ОПИС ЗНАНЬ ПРО ВИХІДНИЙ КОД З ВИКОРИСТАННЯМ ОНТОЛОГІЇ

This research focuses on the use of ontologies as formal knowledge bases for representing source code in automated systems.By creating an ontology that represents the concepts, relationships, and properties of source code elements, it becomes possible to capture and organize knowledge about the codebase. This enables various functional abilities such as automated code analysis and reasoning about code structure and behavior. The aim of this research is to verify the ability to use ontology as a formal knowledge base about source code in automated systems. A review of the recent articles showed that existing tools to work with ontologies does not provide direct functionality to convert the code into a knowledge, so the idea proposed in the article has novelty. The research explores the use of the Web Ontology Language (OWL) as the primary ontology representation language due to its expressiveness, reasoning capabilities, and compatibility with semantic web standards. The research proposes the creation of ontology representations for various code entities, with classes representing main entity types and individuals representing specific instances within the codebase. To validate the concept and demonstrate the expected results, a proof-of-concept system is built, consisting of a parsing client, a RESTful API for communication, and an ontology manager. The system utilizes libraries such as SwiftSyntax for parsing Swift code, Alamofire for HTTP communication, and OWL manipulation libraries (such as OWL-API and ONT-API) for working with ontologies. The system aims to extract knowledge from the source code, convert it into ontology entities and axioms, and reason over the ontology using a reasoner (such as HermiT). The research concludes that ontologies can effectively store knowledge about source code and enable various further manipulations on that knowledge. However, further investigation is needed to determine the optimal set of properties and entities that should be supported by the code ontology extraction system to infer more knowledge about the source code.

Izrada OWL ontologije za prikaz, povezivanje i pretraživanje SemAF diskursnih oznaka

Linguistic Linked Open Data (LLOD) are technologies that provide a powerful instrument for representing and interpreting language phenomena on a web-scale. The main objective of this paper is to demonstrate how LLOD technologies can be applied to represent and annotate a corpus composed of multiword discourse markers, and what the effects of this are. In particular, it is our aim to apply semantic web standards such as RDF and OWL for publishing and integrating data. We present a novel scheme for discourse annotation that combines ISO standards describing discourse relations and dialogue acts – ISO DR-Core (ISO 24617-8) and ISO-Dialogue Acts (ISO 24617-2) in 9 languages (cf. Silvano and Damova 2022; Silvano, et al. 2022). We develop an OWL ontology to formalize that scheme, provide a newly annotated dataset and link its RDF edition with the ontology. Consequently, we describe the conjoint querying of the ontology and the annotations by means of SPARQL, the standard query language for the web of data. The ultimate result is that we are able to perform queries over multiple, interlinked datasets with complex internal structure. This is a first, but essential step, in developing novel, powerful, and groundbreaking means for the corpus-based study of multilingual discourse, communication analysis, or attitudes discovery.

Defeasible RDFS via rational closure

In the field of non-monotonic logics, the notion of Rational Closure (RC) is acknowledged as a notable approach. In recent years, RC has gained popularity in the context of Description Logics (DLs), the logic underpinning the standard semantic Web Ontology Language OWL 2, whose main ingredients are classes, the relationship among classes and roles, which are used to describe the properties of classes.In this work, we show instead how to integrate RC within the triple language RDFS (Resource Description Framework Schema), which together with OWL 2 is a major standard semantic web ontology language.To do so, we start from ρdf, a minimal, but significant RDFS fragment that covers the essential features of RDFS, and then extend it to ρdf⊥, allowing to state that two entities are incompatible/disjoint with each other. Eventually, we propose defeasible ρdf⊥ via a typical RC construction allowing to state default class/property inclusions.Furthermore, to overcome the main weaknesses of RC in our context, i.e., the “drowning problem” (viz. the “inheritance blocking problem”), we further extend our construction by leveraging Defeasible Inheritance Networks (DIN) defining a new non-monotonic inference relation that combines the advantages of both (RC and DIN). To the best of our knowledge this is the first time of such an attempt.In summary, the main features of our approach are: (i) the defeasible ρdf⊥ we propose here remains syntactically a triple language by extending it with new predicate symbols with specific semantics; (ii) the logic is defined in such a way that any RDFS reasoner/store may handle the new predicates as ordinary terms if it does not want to take account of the extra non-monotonic capabilities; (iii) the defeasible entailment decision procedure is built on top of the ρdf⊥ entailment decision procedure, which in turn is an extension of the one for ρdf via some additional inference rules favouring a potential implementation; (iv) the computational complexity of deciding entailment in ρdf and ρdf⊥ are the same; and (v) defeasible entailment can be decided via a polynomial number of calls to an oracle deciding ground triple entailment in ρdf⊥ and, in particular, deciding defeasible entailment can be done in polynomial time.

Bilingual dictionary generation and enrichment via graph exploration

In recent years, we have witnessed a steady growth of linguistic information represented and exposed as linked data on the Web. Such linguistic linked data have stimulated the development and use of openly available linguistic knowledge graphs, as is the case with the Apertium RDF, a collection of interconnected bilingual dictionaries represented and accessible through Semantic Web standards. In this work, we explore techniques that exploit the graph nature of bilingual dictionaries to automatically infer new links (translations). We build upon a cycle density based method: partitioning the graph into biconnected components for a speed-up, and simplifying the pipeline through a careful structural analysis that reduces hyperparameter tuning requirements. We also analyse the shortcomings of traditional evaluation metrics used for translation inference and propose to complement them with new ones, both-word precision (BWP) and both-word recall (BWR), aimed at being more informative of algorithmic improvements. Over twenty-seven language pairs, our algorithm produces dictionaries about 70% the size of existing Apertium RDF dictionaries at a high BWP of 85% from scratch within a minute. Human evaluation shows that 78% of the additional translations generated for dictionary enrichment are correct as well. We further describe an interesting use-case: inferring synonyms within a single language, on which our initial human-based evaluation shows an average accuracy of 84%. We release our tool as free/open-source software which can not only be applied to RDF data and Apertium dictionaries, but is also easily usable for other formats and communities.

A Linked Data Application for Harmonizing Heterogeneous Biomedical Information

In the biomedical field, there is an ever-increasing number of large, fragmented, and isolated data sources stored in databases and ontologies that use heterogeneous formats and poorly integrated schemes. Researchers and healthcare professionals find it extremely difficult to master this huge amount of data and extract relevant information. In this work, we propose a linked data approach, based on multilayer networks and semantic Web standards, capable of integrating and harmonizing several biomedical datasets with different schemas and semi-structured data through a multi-model database providing polyglot persistence. The domain chosen concerns the analysis and aggregation of available data on neuroendocrine neoplasms (NENs), a relatively rare type of neoplasm. Integrated information includes twelve public datasets available in heterogeneous schemas and formats including RDF, CSV, TSV, SQL, OWL, and OBO. The proposed integrated model consists of six interconnected layers representing, respectively, information on the disease, the related phenotypic alterations, the affected genes, the related biological processes, molecular functions, the involved human tissues, and drugs and compounds that show documented interactions with them. The defined scheme extends an existing three-layer model covering a subset of the mentioned aspects. A client–server application was also developed to browse and search for information on the integrated model. The main challenges of this work concern the complexity of the biomedical domain, the syntactic and semantic heterogeneity of the datasets, and the organization of the integrated model. Unlike related works, multilayer networks have been adopted to organize the model in a manageable and stratified structure, without the need to change the original datasets but by transforming their data “on the fly” to respond to user requests.