A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery

Semantic Web for Health Care and Life Sciences: a review of the state of the art

Integration of health information systems are crucial to advance the effective delivery of healthcare for individuals and communities across organizational boundaries. Semantic Web technologies may be used to connect, correlate, and integrate heterogeneous datasets spread over the internet. However, when working with sensitive data, such as health data, security mechanisms are needed. A scoping review of the literature was undertaken to provide a broad view of security mechanisms applied to, or along with, Semantic Web technologies that could allow its use with health data. Searches were conducted in the most relevant databases for the scope of this work. The findings were classified according to the main objective and features presented by each solution. Twenty-six studies were included in the review. They introduced mechanisms that addressed several security attributes, such as authentication, authorization, integrity, availability, confidentiality, privacy, and provenance. These mechanisms support access control frameworks, semantic and functional interoperability infrastructures, and privacy compliance solutions. The findings suggest that the application and use of Semantic Web technologies is still growing, with the healthcare area being particularly interested. The main security mechanisms for Semantic Web technologies, the key security attributes and properties, and the main gaps in the literature were identified, helping to understand the technical needs to mitigate the risks of handling personal health information over the Semantic Web. Also, this research has shown that complex and robust solutions are available to successfully address several security properties and features, depending on the context that the electronic health data is being managed.

This paper presents a systematic literature review of research focused on use of Semantic Web technologies in formal educational contexts. Through systematic search, the review has identified 199 research articles, which are examined with the intention of identifying prevalent themes within the body of research within the field of formal educational use of Semantic Web technologies. The themes identified by the review are: (1) Semantic Web ontologies; (2) Efficient distribution, accessibility, retrieval, reuse and combination of educational resources; (3) Linked Data; (4) Semantic Web enhanced virtual learning environments and personalization of learning environments; (5) Semantic Web learning objects; (6) Evaluation, feedback and assessment; (7) Semantic Web services; (8) Pedagogical tools for teachers and students. Furthermore, this review seeks to examine how these themes and the use of Semantic Web technologies in formal education reflects on the ongoing discussion of how pedagogy and technology should interact. The outset of this discussion is a previous study by Dirckinck‐Holmfeld, which establishes that there is a tendency to emphasize technology over pedagogy in educational design and development.

<div><p><em> Machine Translation has been an area of linguistic research for almost more than two decades now. But it still remains a very challenging task for devising an automated system which will deliver accurate translations of the natural languages. However, great strides have been made in this field with more success owing to the development of technologies of the web and off late there is a renewed interest in this area of research. </em></p><p><em> Technological advancements in the preceding two decades have influenced Machine Translation in a considerable way. Several MT approaches including Statistical Machine Translation greatly benefitted from these advancements, basically making use of the availability of extensive corpora. Web technology web3.0 uses the semantic web technology which represents any object or resource in the web both syntactically and semantically. This type of representation is very much useful for the computing systems to search any content on the internet similar to lexical search and improve the internet based translations making it more effective and efficient.</em></p><p><em> In this paper we propose a technique to improve existing statistical Machine Translation methods by making use of semantic web technology. Our focus will be on Tamil and Tamil to English MT. The proposed method could successfully integrate a semantic web technique in the process of WSD which forms part of the MT system. The integration is accomplished by using the capabilities of RDFS and OWL into the WSD component of the MT model. The contribution of this work lies in showing that integrating a Semantic web technique in the WSD system significantly improves the performance of a statistical MT system for a translation from Tamil to English.</em></p></div><em> In this paper we assume the availability of large corpora in Tamil language and specific domain based ontologies with Tamil semantic web technology using web3.0. We are positive on the expansion and development of Tamil semantic web and subsequently infer that Tamil to English MT will greatly improve the disambiguation concept apart from other related benefits. This method could enable the enhancement of translation quality by improving on word sense disambiguation process while text is translated from Tamil to English language. This method can also be extended to other languages such as Hindi and Indian Languages.</em>

Semantic mashup of biomedical data

Metaphorical language enables humans to express abstract concepts through concrete experiences, making it an essential facet of natural language. Despite its prevalence, computational understanding and generation of metaphor remain fundamentally challenging due to their conceptual complexity and semantic ambiguity. This study presents a novel large-scale metaphor Knowledge Graph (KG) designed to explicitly encode the structure of metaphorical expressions and enable their computational interpretation and generation. The proposed approach integrates multiple layers of semantic information, such as frames, roles, domains, concepts, lexical units, and paraphrases, into a unified KG framework. The construction pipeline employs FrameNet, semantic role labeling (SRL), and Abstract Meaning Representation (AMR) parsing to extract conceptual and relational information from a curated corpus of metaphoric and literal sentence pairs. The resulting KG comprises over 108,000 triples, 12 node types and 13,544 role mappings. Intrinsic evaluations reveal high coverage and consistency in capturing metaphorical structures, while extrinsic evaluations demonstrate the effectiveness of KG in improving metaphor generation tasks. When used as a guide for metaphor generation, the KG improves performance across standard evaluation metrics and receives superior human ratings for fluency, creativity, and Faithfulness compared to baseline neural systems. These results indicate that a structurally grounded representation of metaphor, encoded as a knowledge graph, provides a scalable and interpretable pathway toward metaphor-aware natural language understanding and generation.

The Mapping Manuscript Migrations (MMM) project transformed three separate datasets relating to the history and provenance of medieval and Renaissance manuscripts into a unified knowledge graph. The source databases are: Schoenberg Database of Manuscripts, from the Schoenberg Institute for Manuscript Studies, University of Pennsylvania; Bibale, from the Institut de recherche et d’histoire des textes (IRHT-CNRS, Paris); and Medieval Manuscripts in Oxford Libraries, from the Bodleian Libraries, University of Oxford. The data consist of more than 20 million RDF triples which have been mapped to the MMM Data Model. The model combines classes and properties from CIDOC-CRM and FRBR, together with some specific MMM elements. The Knowledge Graph was created using the MMM data transformation pipeline. The MMM dataset is available from the Zenodo repository, and can be directly deployed on a SPARQL endpoint using a docker recipe. To test and demonstrate its usefulness, the MMM Knowledge Graph is in use in the MMM Semantic Portal: https://mappingmanuscriptmigrations.org.

This work deals with the applications of Semantic Publishing technologies in the legal domain, i.e., the use of Semantic Web technologies to address issues related to the Legal Scholarly Publishing. R

Traditionally, research in applying Semantic Web technology to multimedia information systems has focused on using annotations and ontologies to improve the retrieval process. This paper concentrates on improving the presentation of the retrieval results. First, our approach uses ontological domain knowledge to select and organize the content relevant to the topic the user is interested in. Domain ontologies are valuable in the presentation generation process, because effective presentations are those that succeed in conveying the relevant domain semantics to the user. Explicit discourse and narrative knowledge allows selection of appropriate presentation genres and creation of narrative structures, which are used for conveying these domain relations. In addition, knowledge of graphic design and media characteristics is essential to transform abstract presentation structures into real multimedia presentations. Design knowledge determines how the semantics and presentation structure are expressed in the multimedia presentation. In traditional Web environments, this type of design knowledge remains implicit, hidden in style sheets and other document transformation code. Our second use of Semantic Web technology is to model design knowledge explicitly, and to enable it to drive the transformations needed to turn annotated media items into structured presentations.

As building information modeling (BIM) gains popularity in the architecture, engineering, and construction (AEC) industry, manufacturers are required to distribute their product specifications in digital product models. Currently, manufacturers mainly employ proprietary formats, such as BIM objects supplemented by PDF documents to represent their product data descriptions. However, these formats do not support flexible automated product search and data integration. This paper describes the use of Semantic Web technologies in combination with BIM-based visual programming language (VPL) to automatically integrate product data from external databases. To facilitate data integration, we introduced a method to semantically represent product data linked with the CEN/TS 17623:2021 standard using ontologies in web ontology language (OWL). The study has focused on the use case of a manufacturer of lighting products. Results show that building designers are able to execute a more efficient product search that satisfies their query requirements and returns suitable products of their choice from the manufacturer's database based on their requests. This approach eliminates the time-consuming and error-prone process of manually entering product data into BIM software.

Preservation of Cultural Heritage (CH) collections in the best possible condition for the longest time possible is a crucial part of CH Institutions activity, since it ensures artefacts’ effective function in perpetuity. In this context, preservation processes that do not include any physical interaction with an object or collection can be regarded as preventive conservation. Preventive conservation measures and activities include among others the monitoring and management of environmental factors, in order to reduce potential risks of collections condition. The advent of the Internet of Things (IoT) can help towards this goal by automating the collection of data through sensors deployed in the cultural space and providing available services based on the IoT ecosystem. IoT technologies can facilitate the preventive conservation of tangible CH by exploiting streaming data produced by networks of sensors that keep track of changes in environmental parameters of a particular museum, in order to monitor the condition of its collections. Moreover, Semantic Web (SW) technologies could increase the efficiency of sensed data management by introducing reasoning mechanisms that will result in useful inferences regarding the combination of long-term or short-term records of sensed data and material decay. This work summarizes current state-of-the-art frameworks and monitoring systems that collect data from sensors in CH environments and the use of semantic web technologies for the efficient management of conservation and sensor data. Based on this study, it proposes an IoT infrastructure with semantic tools, which aims to enhance preventive conservation science.

In the practice of building models, we have encountered a number of methodological areas that need to be addressed in order to make the modelling process more robust, scalable, maintainable and transparent. In the context of modelling socio-technical systems (existing infrastructures and businesses) models are primarily data-driven: they contain heterogeneous agents, numerous assumptions and facts about agents and their environment. The data (the facts and assumptions used in the models) span multiple disciplines and are contributed by multiple domain experts. The focus of this chapter is to present new options for improving the management of model data. The issue of data management will be addressed at two levels: that of researchers who need to collaborate when creating and maintaining the model data, and that of data management within a simulation model. The methods proposed in this chapter rely on the use of Semantic Web technologies and philosophies to address data management issues. It is the goal of Semantic Web to make data understandable and useful for both machines and humans. In this chapter we will discuss the complications of modelling socio-technical systems and suggest uses of Semantic Web technologies to aid both collaboration between the modellers and knowledge management for the agents.

Last years witnessed a shift from the potential utility in digitisation to a crucial need to enjoy activities virtually. In fact, before 2019, data curators recognised the utility of performing data digitisation, while during the lockdown caused by the COVID-19, investing in virtual and remote activities to make culture survive became crucial as no one could enjoy Cultural Heritage in person. The Cultural Heritage community heavily invested in digitisation campaigns, mainly modelling data as Knowledge Graphs by becoming one of the most successful Semantic Web technologies application domains. Despite the vast investment in Cultural Heritage Knowledge Graphs, the syntactic complexity of RDF query languages, e.g., SPARQL, negatively affects and threatens data exploitation, risking leaving this enormous potential untapped. Thus, we aim to support the Cultural Heritage community (and everyone interested in Cultural Heritage) in querying Knowledge Graphs without requiring technical competencies in Semantic Web technologies. We propose an engaging exploitation tool accessible to all without losing sight of developers’ technological challenges. Engagement is achieved by letting the Cultural Heritage community leave the passive position of the visitor and actively create their Virtual Assistant extensions to exploit proprietary or public Knowledge Graphs in question-answering. By accessible to all, we mean that the proposed software framework is freely available on GitHub and Zenodo with an open-source license. We do not lose sight of developers’ technical challenges, which are carefully considered in the design and evaluation phases. This article first analyses the effort invested in publishing Cultural Heritage Knowledge Graphs to quantify data developers can rely on in designing and implementing data exploitation tools in this domain. Moreover, we point out challenges developers may face in exploiting them in automatic approaches. Second, it presents a domain-agnostic Knowledge Graph exploitation approach based on virtual assistants as they naturally enable question-answering features where users formulate questions in natural language directly by their smartphones. Then, we discuss the design and implementation of this approach within an automatic community-shared software framework (a.k.a. generator) of virtual assistant extensions and its evaluation in terms of performance and perceived utility according to end-users. Finally, according to a taxonomy of the Cultural Heritage field, we present a use case for each category to show the applicability of the proposed approach in the Cultural Heritage domain. In overviewing our analysis and the proposed approach, we point out challenges that a developer may face in designing virtual assistant extensions to query Knowledge Graphs, and we show the effect of these challenges in practice.

Different software processing affects the peak picking and metabolic pathway recognition of metabolomics data

The use of Semantic Web Technologies supports research in the field of digital humanities. In this paper we focus on the creation of semantic independent online databases such as those of historical prosopography. These databases contain biographical information of historical persons. We focus on this information with an interest in German professorial career patterns from the 16th to the 18th century. In that respect, we describe the process of building an Early Modern Scholarly Career RDF Knowledge Graph from two existing prosopography online databases: the Catalogus Professorum Lipsiensium and the Catalogus Professorum Helmstadiensium. Further, we provide an insight in how to query the information using KBox to answer research questions.