Multilingual Ontology Research Articles

Empowering healthcare education: A multilingual ontology for medical informatics and digital health (MIMO) integrated to artificial intelligence powered training in smart hospitals.

Objective: A comprehensive understanding of professional and technical terms is essential to achieving practical results in multidisciplinary projects dealing with health informatics and digital health. The medical informatics multilingual ontology (MIMO) initiative has been created through international cooperation. MIMO is continuously updated and comprises over 3700 concepts in 37 languages on the Health Terminology/Ontology Portal (HeTOP). Methods: We conducted case studies to assess the feasibility and impact of integrating MIMO into real-world healthcare projects. In HosmartAI, MIMO is used to index technological tools in a dedicated marketplace and improve partners' communication. Then, in SaNuRN, MIMO supports the development of a "Catalog and Index of Digital Health Teaching Resources" (CIDHR) backing digital health resources retrieval for health and allied health students. Results: In HosmartAI, MIMO facilitates the indexation of technological tools and smooths partners' interactions. In SaNuRN within CIDHR, MIMO ensures that students and practitioners access up-to-date, multilingual, and high-quality resources to enhance their learning endeavors. Conclusion: Integrating MIMO into training in smart hospital projects allows healthcare students and experts worldwide with different mother tongues and knowledge to tackle challenges facing the health informatics and digital health landscape to find innovative solutions improving initial and continuous education.

Constructing a Multilingual E-Learning Ontology through Web Crawling and Scraping

The emergence of digital technologies has transformed the landscape of education, driving the exploration of innovative methods to improve the efficiency and effectiveness of university e- learning. This study focuses on leveraging network management principles in combination with web crawling trends to propose a novel approach: a web crawling and scraping-driven method for constructing a multilingual ontology tailored specifically for university e-learning. The primary goal of this research is to create a comprehensive and continuously updated knowledge repository by systematically gathering and extracting information from a wide range of online sources. By incorporating multilingual capabilities into the proposed ontology, the aim is to transcend language barriers and establish a globally accessible and inclusive e-learning environment. This approach recognizes the intricate relationship between technology and education, highlighting the potential of automated data retrieval and ontology construction in reshaping the future of university e-learning. This research contributes significantly to the rapidly growing field of educational technology by introducing a forward-thinking paradigm. It empowers both educators and learners with a versatile and personalized learning experience that transcends cultural and linguistic boundaries. As the digital era continues to evolve, this approach serves as a beacon of innovation, exemplifying the transformative power of integrating cutting-edge technology with pedagogical efforts. In essence, this study presents a groundbreaking approach to enhance university e- learning by harnessing the capabilities of web crawling, scraping, and multilingual ontology construction. It emphasizes the importance of adapting to the ever-evolving digital landscape to provide an inclusive and accessible education experience for learners worldwide. Ultimately, this research represents a significant step forward in the ongoing effort to revolutionize education through the integration of advanced technology and pedagogical innovation.

Semi-Automatic Building and Learning of a Multilingual Ontology

Most online platforms, applications, and Websites use a massive amount of heterogeneous evolving data. These data must be structured and normalized before integration to improve the search and increase the relevance of results. An ontology can address this critical task by efficiently managing data and providing structured formats through techniques such as the Web Ontology Language (OWL). However, building an ontology can be costly, primarily if conducted manually. In this context, we propose a new methodology for automatically building and learning a multilingual ontology using Arabic as the base language via a corpus collected from Wikipedia. Our proposed methodology relies on Finite-state transducers (FSTs). FSTs are regrouped into a cascade to reduce errors and minimize ambiguity. The produced ontology is extended to English and French and independent language images via a translator we developed using APIs. The rationale for starting with the Arabic corpus to extract terms is that entity linking is more convenient from Arabic to other languages. In addition, many Wikipedia articles in English and French (for instance) do not have associated Arabic articles, but the opposite is true. In addition, dealing with Arabic terms permits us to enrich the Arabic module of the free linguistic platform we use in dictionaries and graphs. To assess the efficiency of our proposed methodology, we conducted performance metrics. The reported results are encouraging and promising.

Multilingual Knowledge Organization of Cultural Routes: The Case of the Grand Canal

ABSTRACTThis study collects heterogeneous information from multiple sources related to the Grand Canal, a cultural route in China, to construct a multilingual ontology of the Grand Canal. The research designs a data translation, optimization, and multilingual ontology construction method to reveal the internal and external characteristics and associations of the Grand Canal, which can provide users with multilingual and ordered knowledge of the cultural route, providing a feasible solution for digital preservation and global sharing of the cultural routes. On this basis, a multilingual knowledge service system is built.

Digital Emergency Management for a Complex One Health Landscape: the Need for Standardization, Integration, and Interoperability.

Planning reliable long-term planning actions to handle disruptive events requires a timely development of technological infrastructures, as well as the set-up of focused strategies for emergency management. The paper aims to highlight the needs for standardization, integration, and interoperability between Accident & Emergency Informatics (A&EI) and One Digital Health (ODH), as fields capable of dealing with peculiar dynamics for a technology-boosted management of emergencies under an overarching One Health panorama. An integrative analysis of the literature was conducted to draw attention to specific foci on the correlation between ODH and A&EI, in particular: (i) the management of disruptive events from private smart spaces to diseases spreading, and (ii) the concepts of (health-related) quality of life and well-being. A digitally-focused management of emergency events that tackles the inextricable interconnectedness between humans, animals, and surrounding environment, demands standardization, integration, and systems interoperability. A consistent and finalized process of adoption and implementation of methods and tools from the International Standard Accident Number (ISAN), via findability, accessibility, interoperability, and reusability (FAIR) data principles, to Medical Informatics and Digital Health Multilingual Ontology (MIMO) - capable of looking at different approaches to encourage the integration between the ODH framework and the A&EI vision, provides a first answer to these needs. ODH and A&EI look at different scales but with similar goals for converging health and environmental-related data management standards to enable multi-sources, interdisciplinary, and real-time data integration and interoperability. This allows holistic digital health both in routine and emergency events.

Spanish Synonyms as Part of a Multilingual Event-Type Ontology

Abstract This paper presents an ongoing work on the multilingual event-type ontology SynSemClass, where multilingual verbal synonymy is formalized in terms of syntactic and semantic properties. In the ontology, verbs are grouped into synonym classes, both monolingually and cross-lingually. Specifically, verbs are considered to belong to the same class if they both express the same meaning in a specific context, and their valency frame can be mapped to the set of roles defined for a particular class. SynSemClass is built following a bottom-up approach where translational equivalents are automatically extracted from parallel corpora and annotated by human annotators. The task of the annotators consists in mapping the valency frame of a particular verb with the set of roles defined for the class where the verb is included as a potential class member, establishing links to external resources, and selecting relevant examples. The Spanish part of the ontology currently contains 257 classes enriched with Spanish synonyms. The resulting resource provides finegrained syntactic and semantic information on multilingual verbal synonyms and links to other existing monolingual and multilingual resources.

Sharing Digital Health Educational Resources in a One-Stop Shop Portal: Tutorial on the Catalog and Index of Digital Health Teaching Resources (CIDHR) Semantic Search Engine.

Access to reliable and accurate digital health web-based resources is crucial. However, the lack of dedicated search engines for non-English languages, such as French, is a significant obstacle in this field. Thus, we developed and implemented a multilingual, multiterminology semantic search engine called Catalog and Index of Digital Health Teaching Resources (CIDHR). CIDHR is freely accessible to everyone, with a focus on French-speaking resources. CIDHR has been initiated to provide validated, high-quality content tailored to the specific needs of each user profile, be it students or professionals. This study's primary aim in developing and implementing the CIDHR is to improve knowledge sharing and spreading in digital health and health informatics and expand the health-related educational community, primarily French speaking but also in other languages. We intend to support the continuous development of initial (ie, bachelor level), advanced (ie, master and doctoral levels), and continuing training (ie, professionals and postgraduate levels) in digital health for health and social work fields. The main objective is to describe the development and implementation of CIDHR. The hypothesis guiding this research is that controlled vocabularies dedicated to medical informatics and digital health, such as the Medical Informatics Multilingual Ontology (MIMO) and the concepts structuring the French National Referential on Digital Health (FNRDH), to index digital health teaching and learning resources, are effectively increasing the availability and accessibility of these resources to medical students and other health care professionals. First, resource identification is processed by medical librarians from websites and scientific sources preselected and validated by domain experts and surveyed every week. Then, based on MIMO and FNRDH, the educational resources are indexed for each related knowledge domain. The same resources are also tagged with relevant academic and professional experience levels. Afterward, the indexed resources are shared with the digital health teaching and learning community. The last step consists of assessing CIDHR by obtaining informal feedback from users. Resource identification and evaluation processes were executed by a dedicated team of medical librarians, aiming to collect and curate an extensive collection of digital health teaching and learning resources. The resources that successfully passed the evaluation process were promptly included in CIDHR. These resources were diligently indexed (with MIMO and FNRDH) and tagged for the study field and degree level. By October 2023, a total of 371 indexed resources were available on a dedicated portal. CIDHR is a multilingual digital health education semantic search engine and platform that aims to increase the accessibility of educational resources to the broader health care-related community. It focuses on making resources "findable," "accessible," "interoperable," and "reusable" by using a one-stop shop portal approach. CIDHR has and will have an essential role in increasing digital health literacy.

Toward the Multilingual Semantic Web: Multilingual Ontology Matching and Assessment

The amount of multilingual data on the Web proliferates; therefore, developing ontologies in various natural languages is attracting considerable attention. In order to achieve semantic interoperability for the multilingual Web, cross-lingual ontology matching techniques are highly required. This paper proposes a Multilingual Ontology Matching (MoMatch) approach for matching ontologies in different natural languages. MoMatch uses machine translation and various string similarity techniques to identify correspondences across different ontologies. Furthermore, we propose a Quality Assessment Suite for Ontologies (QASO) that comprises 14 metrics, out of which seven metrics are used to assess the quality of the matching process and seven metrics are used to evaluate the quality of the ontology. We present an in-depth comparison of different string similarity techniques across various languages to get the most effective similarity measure(s) between multilingual terms. To illustrate the applicability of our approach and how it can be used in different domains, we present two use cases. MoMatch has been implemented using Scala and Apache Spark under an open-source license. We have compared our results with the results from the Ontology Alignment Evaluation Initiative (OAEI 2020). MoMatch has achieved significantly high precision, recall, and F-measure compared to five state-of-the-art matching approaches.

Представление знаний в многоязычных образовательных ресурсах

Introduction. Ontologies are now recognised as the advanced standard of knowledge representation for e-learning and some industries. In particular, the development of multilingual ontological education resources is characterised as a promising area of research in the context of industry universities’ digital transformation. The article deals with the development of an academic course multilingual ontology in a Controlled Natural Language. Relevance. Although there are many ontology editors, national developers of education resources should be familiar with formal logic and have a good command of English. Therefore, it is difficult to discuss widespread use of ontology-based education solutions in Russian universities. Materials and Methods. The article offers a version of Controlled Russian Language for academic knowledge representation. A methodology to be used for compiling academic course ontologies is developed. As an example, a piece of ontology for the Introductory Course on Railways is considered. Results and Discussion. To support this way of knowledge representation, a prototype of ontology editor Onto.plus was developed to support the version of Controlled Russian Language. To implement the multilanguage function, equivalent versions for the Controlled Russian Language ontology were developed in English and Chinese. Conclusions. The solutions are a contribution to the implementation of an open project to develop an ontology resource integrating universities and industry.

A Semantic and Smart Framework for Handling Multilingual Linguistic Knowledge

The authors propose a semantic and smart assistance framework for handling linguistic knowledge, called LingFramework. It targets both expert and novice users. It aims to assist the user in understanding the different aspects of the linguistic domain and ease the process of proposing lingware applications. LingFramework is based on a multilingual linguistic domain ontology called LingOnto. It allows (1) representing linguistic data, linguistic processing functionalities and linguistic processing features, and (2) reasoning, via a SWRL-based reasoning engine, about the linguistic knowledge. Currently, it covers English, French, and Arabic languages. To facilitate the interaction with LingOnto, an ontology visualization tool called LingGraph is proposed. It offers an easy-to-use interface for users not familiar with ontologies. It provides a SPARQL pattern-based approach to allow a smart search interaction functionality. LingFramework is applied to assist the user in identifying valid linguistic processing pipelines related to lingware applications. The evaluation results are promising.

LL(O)D and NLP perspectives on semantic change for humanities research

This paper presents an overview of the LL(O)D and NLP methods, tools and data for detecting and representing semantic change, with its main application in humanities research. The paper’s aim is to provide the starting point for the construction of a workflow and set of multilingual diachronic ontologies within the humanities use case of the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, CA18209. The survey focuses on the essential aspects needed to understand the current trends and to build applications in this area of study.

An Ontology based Smart Management of Linguistic Knowledge

Natural language processing provides a very significant contribution to various application areas such as multilingual big data, information retrieval, data integration and multilingual web. However, handling linguistic knowledge to develop such lingware applications is a crucial issue, especially for linguistic novice users. To deal with this issue, a "smart" linguistic knowledge management may help the users to understand the meaning, scope and especially the use of related techniques and algorithms. In this paper, (1) we propose a semantic processing of linguistic knowledge based on a multilingual linguistic domain ontology, called LingOnto. Compared to related work, LingOnto does not only handles linguistic data, but also linguistic processing functionalities and linguistic processing features. Besides, it allows, via a reasoning engine, inferring new linguistic knowledge and assisting in the process of proposing lingware applications. This is particularly useful for novice users, but can also provide new perspectives for the expert ones. LingOnto covers the French, English and Arabic languages. (2) We propose also an assisted user friendly ontology visualization tool called LingGraph. It facilitates the interaction with LingOnto. It offers an easy to use interface for users not familiar with ontologies. It is based on a SPARQL pattern-based approach to allow a smart search interaction functionality to visualize only the ontological view corresponding to the user’s needs and preferences. In order to evaluate LingOnto, we apply it to a framework of identifying valid natural language processing pipelines. Finally, we give the results of the carried-out experiments.

Digital Health Multilingual Ontology to Index Teaching Resources.

The aim of this paper is to present the use of Medical Informatics Multilingual Ontology (MIMO) to index digital health resources that are (and will be) included in SaNuRN (project to teach digital health). MIMO currently contains 1,379 concepts and is integrated into HeTOP, which is a cross-lingual multiterminogy server. Existing teaching resources have been reindexed with MIMO concepts and integrated into a dedicated website. A total of 345 resources have been indexed with MIMO concepts and are freely available at https://doccismef.chu-rouen.fr/dc/#env=sanurn. The development of a multilingual MIMO for enhancing the quality and the efficiency of international projects is challenging. A specific semantic search engine has been deployed to give access to digital health teaching resources.

Medical informatics and digital health multilingual ontology (MIMO): A tool to improve international collaborations

BackgroundEven if English is the leading language for international communication, it is essential to keep in mind that research runs at the local level by local teams generally communicating in their local/national language, especially in Europe among European projects. ObjectiveTherefore, the European Federation for Medical Informatics - Working Group on Health Informatics for Inter-regional Cooperation” has one objective: To develop a multilingual ontology focusing on Health Informatics and Digital Health as a collaboration tool that improves international and, in particular, European collaborations. ResultsWe have developed the Medical Informatics and Digital Health Multilingual Ontology (MIMO). Hosted on the Health Terminology/Ontology Portal (HeTOP), MIMO contains around 1,000 concepts, 460 MeSH Descriptors, 220 MeSH Concepts, and more than 300 newly created concepts. MIMO is continuously updated to comprise as recent as possible concepts and their translations in more than 30 languages. Moreover, the MIMO’s development team constantly improves MIMO content and supporting information. Thus, during workshop discussions and one-on-one exchanges, the MIMO team has collected domain experts’ opinions about the community’s interests and suggestions for future enhancements. Moreover, MIMO will be integrated to support the annotation and categorization of research products into the HosmartAI European project involving more than 20 countries around Europe and worldwide. ConclusionMIMO is hosted by HeTOP (Health Terminology/Ontology Portal), which integrates 100 terminologies and ontologies in 55 languages. MIMO is freely available online. MIMO is portable to other knowledge platforms as part of MIMO’s main aims to facilitate communication between medical librarians, translators, and researchers as well as to support students’ self-learning.

A Framework for Building a Multilingual Industrial Ontology: Methodology and a Case Study for Building Smartphone English-Arabic Ontology

As Web 3.0 is blooming, ontologies augment semantic Web with semi–structured knowledge. Industrial ontologies can help in improving online commercial communication and marketing. In addition, conceptualizing the enterprise knowledge can improve information retrieval for industrial applications. Having ontologies combine multiple languages can help in delivering the knowledge to a broad sector of Internet users. In addition, multi-lingual ontologies can also help in commercial transactions. This research paper provides a framework model for building industrial multilingual ontologies which include Corpus Determination, Filtering, Analysis, Ontology Building, and Ontology Evaluation. It also addresses factors to be considered when modeling multilingual ontologies. A case study for building a bilingual English-Arabic ontology for smart phones is presented. The ontology was illustrated using an ontology editor and visualization tool. The built ontology consists of 67 classes and 18 instances presented in both Arabic and English. In addition, applications for using the ontology are presented. Future research directions for the built industrial ontology are presented.

Educational Projection Systems, Levels And Prerequisites Of Mathematical Ontology OntoMathEdu

The developed educational projections, levels and prerequisites of the mathematical educational multilingual ontology OntoMathEdu are presented. Educational projection is viewed as the formalization of a certain system of subject training in mathematics. It is a subset of OntoMathEdu ontology concepts, which are structured at this stage of ontology development using two didactic relationships – educational level and prerequisites.
 Educational levels are allocated on the basis of the teaching standards of the corresponding education system, the relation of prerequisites is determined by the sequence of the studied concepts in a particular education system.
 The OntoMathEdu ontology defines two projections representing the educational systems of Russia and Great Britain. The algorithm for constructing an ontology through linking various projections allows it to be further replenished with new educational projections, which can later be used in the system of multilingual teaching of mathematics.

MLGrafViz: multilingual ontology visualization plug-in for protégé

Natural Language Processing (NLP) is rapidly increasing in all domains of knowledge acquisition to facilitate different language user. It is required to develop knowledge based NLP systems to provide better results. Knowledge based systems can be implemented using ontologies where ontology is a collection of terms and concepts arranged taxonomically. The concepts that are visualized graphically are more understandable than in the text form. In this research paper, new multilingual ontology visualization plug-in MLGrafViz is developed to visualize ontologies in different natural languages. This plug-in is developed for protégé ontology editor. This plug-in allows the user to translate and visualize the core ontology into 135 languages.

MLGrafViz: multilingual ontology visualization plug-in for Protégé

Natural language processing (NLP) is rapidly increasing in all domains of knowledge acquisition to facilitate different language user. It is required to develop knowledge-based NLP systems to provide better results. Knowledge based systems can be implemented using ontologies where ontology is a collection of terms and concepts arranged taxonomically. The concepts that are visualized graphically are more understandable than in the text form. In this research paper, new multilingual ontology visualization plug-in MLGrafViz is developed to visualize ontologies in different natural languages. This plug-in is developed for Protégé ontology editor. This plug-in allows the user to translate and visualize the core ontology into 135 languages.

Building a multilingual ontology for education domain using monto method

Ontologies are emerging technology in building knowledge based information retrieval systems. It is used to conceptualize the information in human understandable manner. Knowledge based information retrieval are widely used in the domain like Education, Artificial Intelligence, Healthcare and so on. It is important to provide multilingual information of those domains to facilitate multi-language users. In this paper, we propose a multilingual ontology (MOnto) methodology to develop multilingual ontology applications for education domain. New algorithms are proposed for merging and mapping multilingual ontologies.

Building a multilingual ontology for education domain using monto method

Ontologies are emerging technology in building knowledge based information retrieval systems. It is used to conceptualize the information in human understandable manner. Knowledge based information retrieval are widely used in the domain like Education, Artificial Intelligence, Healthcare and so on. It is important to provide multilingual information of those domains to facilitate multi-language users. In this paper, we propose a MOnto (Multilingual Ontology) methodology to develop multilingual ontology applications for education domain. New algorithms are proposed for merging and mapping multilingual ontologies.